For fully automated heavy-duty truck platoons, the uncertain behavior of surrounding traffic participants makes platooning control and stability extremely complex. This may lead to a deterioration in fuel efficiency and could even result in collisions. To mitigate the impact of traffic participants’ uncertainty and enhance fuel economy, this paper proposes an energy-efficient speed planning method for automated truck platoons considering uncertainty-aware trajectory prediction. In trajectory prediction uncertainty modeling, perceptual uncertainty is incorporated into the loss function of the prediction module to reduce overconfidence in long-horizon predictions. To generate accurate long-horizon platoon speed trajectories that better align with vehicle dynamic characteristics, vehicle response delays are identified and and integrated into the planning process. With varying levels of prediction uncertainty and response delays, the platoon speed planning method adapts more safely and proactively to multi-vehicle interaction scenarios. Experimental results from two representative traffic flow conditions demonstrate that the proposed method effectively enhances both safety and energy efficiency in complex traffic conditions, particularly in cut-in events.

Despite compliance with safety standards such as ISO 26262, the perception systems of autonomous vehicles still face numerous challenges during real-world operation. In this work, we propose a framework to identify safety-critical scenarios specifically induced by perception failures, aiming to support more targeted and effective scenario-based testing. Importantly, the key challenge lies in disentangling whether perception failures directly lead to critical outcomes. To address this, we propose an Online Causal Reinforcement Searching (OCRS) framework that simultaneously performs scenario search and causal reasoning. Focusing on visual unawareness and perception degradation, OCRS employs an LSTM-RNN controller to identify accident-prone scenarios linked to perception failures, which are then verified in a counterfactual world to determine causal responsibility. To improve efficiency, an online scenario classifier with passive-aggressive updates is introduced to dynamically filter out non-critical cases. The experimental results demonstrate both the effectiveness and efficiency of the proposed approach, achieving a 33% reduction in execution time for searchingperception-failure-induced corner cases. Furthermore, we evaluate the method under adverse weather conditions such as snow and fog, confirming that OCRS remains effective in identifying various failure modes.

Recent studies have suggested that potential short-comings of certain perception modules can be discovered by analyzing the performance of worst scenarios. However, finding the worst perception scenario (WPS) requires datasets with rich semantic annotations of the scenes in visual perception tasks and it is time-consuming to label all the scenario data. To address this, we proposed a method of prediction for WPS, which utilized prior information to predict the model performance under the absence of annotations. Specifically, this paper introduced a scenario matcher based on hybrid re-ranking, which combined the labeled and unlabeled data to generate the pseudo-sample set. In addition, we designed a sample reorganization module to update this sample set through the nearest neighbor retrieval. We also discussed the distribution relationship between labeled and unlabeled data, categorizing it into three cases, and validated the effectiveness of the proposed method on the KITTI and ApolloScape datasets.

Ceiling painting is an essential yet labor-intensive task in the construction industry, making automation necessary to overcome issues such as inconsistent manual quality and associated health hazards. This paper addresses the multi-target coverage trajectory planning problem for ceiling painting robots operating in complex indoor environments, aiming to generate efficient, collision-free, and kinematically feasible trajectories that fully cover all designated peeling areas. The planning problem is first formulated as a mixed-integer optimal control model and then discretized into a mixed-integer nonlinear program. Conventional solvers struggle with prohibitive computational complexity due to integer variables and non-convex constraints arising from kinematic feasibility, collision avoidance, and coverage requirements. To overcome these computational challenges, we propose a two-stage planning structure. In the first stage, the planner constructs a coarse trajectory by integrating waypoint clustering, probabilistic roadmap-based collision-free path planning, and Traveling Salesman Problem optimization. The second stage addresses the non-convexity of the coverage constraints through an alternating optimization approach, iteratively fixing integer and continuous variables to achieve convexification and refine the trajectory. To further improve computational efficiency and scalability, the trajectory is segmented and each segment optimized independently. Simulation results demonstrate the effectiveness and reliability of the proposed planner, highlighting significant reductions in computational time compared with baseline methods.

Sampling, generation, and evaluation of scenarios are essential steps for intelligent testing of autonomous vehicles. Since uncertainty in driving behavior always leads to different occurrence frequencies of scenarios, we have to sample these scenarios in naturalistic datasets. Furthermore, a specified scenario needs to be further enriched and the driving behavior within it needs to be fully described to carry out generation in simulation systems. However, existing approaches generate scenarios randomly and uncontrollably, which makes them unable to precisely generate the specified scenarios. The driving behavior they describe is also memoryless and inflexible. To address the two issues, we propose a task-driven controllable scenario generation framework that can generate scenarios with the consideration of the driving behavior of Surrounding Vehicles (SVs) in a controllable manner. We first manually assign the driving behavior based on different testing tasks for all the considered vehicles. Then we expand the driving behavior temporally as the continuation and transition of several motion activities and generate the corresponding vehicle trajectories spatially. We adopt And-Or Graph (AOG) to model the transition between these motion activities. In contrast to the common memoryless Markov process, our framework generates driving behavior with continuity and driving memory. Finally, we evaluate our framework by generating lane-changing scenarios.

Optimization-based methods are commonly applied in autonomous driving trajectory planners, which transform the continuous-time trajectory planning problem into a finite nonlinear program with constraints imposed at finite collocation points. However, potential violations between adjacent collocation points can occur. To address this issue thoroughly, we propose a safety-guaranteed collision-avoidance model to mitigate collision risks within optimization-based trajectory planners. This model introduces an “embodied footprint”, an enlarged representation of the vehicle’s nominal footprint. If the embodied footprints do not collide with obstacles at finite collocation points, then the ego vehicle’s nominal footprint is guaranteed to be collision-free at any of the infinite moments between adjacent collocation points. According to our theoretical analysis, we define the geometric size of an embodied footprint as a simple function of vehicle velocity and curvature. Particularly, we propose a trajectory optimizer with the embodied footprints that can theoretically set an appropriate number of collocation points prior to the optimization process. We conduct this research to enhance the foundation of optimization-based planners in robotics. Comparative simulations and field tests validate the completeness, solution speed, and solution quality of our proposal.

Under extreme conditions, vehicles may encounter critical instability and cause traffic accidents due to the tire force saturation. In such cases, accurately predicting the vehicle instability is conducive to vehicle safety because drivers or vehicle controllers can be alerted and take early interventions to ensure driving safety. However, the existing stability assessment methods tend to be conservative, hard to quantify, and often ignore the coupled longitudinal and lateral dynamics, as well as the nonlinear characteristics of tires. Simultaneously, under extreme operating conditions, the assessment of vehicle potential risk imposes higher demands on the prediction accuracy of vehicle motion states. To address these 2 issues, this paper proposes a predictive vehicle stability assessment method using 3-dimensional Lyapunov exponents (3D-LEs) for a nonlinear vehicle system. Firstly, a nonlinear 8-degree-of-freedom vehicle dynamics model is constructed for an electric vehicle, aiming to capture the coupling dynamic characteristics and the tire force saturation under extreme conditions. To minimize the simulation-reality disparities, the vehicle parameters are automatically calibrated through Bayesian optimization using field test data. Secondly, to predict the potential risk of vehicle instability precisely, a physics-informed neural network based state prediction module is established for the vehicle stability assessment system. The ordinary differential equations of the vehicle system are integrated into neural networks to obtain physically consistent predictions of vehicle dynamic motion. Finally, the 3D-LEs, encompassing lateral motion, yaw motion, and roll motion, are employed to concurrently evaluate vehicle stability. Experimental results demonstrate that the predictive vehicle stability assessment method accurately evaluates the stability of predicted state sequences, enabling safer and more stable control under extreme conditions.

The proper management of mixed traffic is crucial for unleashing the benefits of connected and automated vehicles (CAVs). Generally, the benefits of CAVs can be categorized into one-dimensional benefits in car-following performance and two-dimensional benefits in efficiently addressing right-of-way conflicts. Currently, the most effective approach to achieve this is by establishing a dedicated right-of-way for CAVs. However, existing strategies are limited to dedicated lane strategy, which can only unleash the one-dimensional benefits of CAVs while the two-dimensional benefits remain untapped. Therefore, this paper proposes a novel management approach for mixed traffic called the dedicated link strategy. The dedicated link refers to the road link that only allows CAVs to use. This strategy can unleash both the one-dimensional and two-dimensional benefits of CAVs via: (i) dedicated link deployment at the road network level and (ii) a novel intersection management approach. Specifically, at the macroscopic road network level, we introduce a bi-level dedicated link deployment model and design an artificial bee colony based algorithm to solve the optimal dedicated link deployment. At the microscopic intersection level, we develop a novel intersection management approach that integrates traditional traffic signal strategy with the emerging signal-free cooperative driving method, thereby boosting the efficiency of intersections. The macroscopic and microscopic methods will complement each other to achieve efficient management of network-wide mixed traffic systems. Finally, we verify the performance of the dedicated link strategy through comprehensive experiments. In essence, the proposed dedicated link strategy unifies the existing dedicated lane strategy and dedicated intersection strategy, providing a general solution for mixed traffic management.

Achieving excellent generalization on perceiving real traffic scenarios with diversity is the long-term goal for building robust autonomous driving systems. A recent theoretical study shows that the generalization on the worst-group of test samples is far more difficult than others. Therefore, we propose to discover potential shortness of certain perception module by analyzing its worst-scenario performance. However, with the benchmark datasets growing huge and tremendous, exhaustive searching for the worst perception scenario (WPS) seems to be time consuming and unnecessary. To address this, we present an automatic searching scheme empowered by reinforcement learning. In this case, worst scenario mining is formulated as the discrete search on the Visual Operation Design Domain (ODD), namely scenario representation, by optimizing LSTM-RNN controller with the worst-performance reward. Moreover, a time-efficient K-reciprocal re-ranking technique is utilized to match the predicted scenario parameters with existing test data. The proposed method has been validated by finding the most challenging scenarios for various vehicle detectors on KITTI, BDD100k and our own benchmark set EVB. Furthermore, searching performances w.r.t different Visual ODDs are investigated and it is found that visual representations through generative adversarial network contribute to a better performance.

Scenario engineering plays a vital role in various Industry 5.0 applications. In the field of autonomous driving systems, driving scenario data are important for the training and testing of critical modules. However, the corner scenario cases are usually rare and necessary to be extended. Existing methods cannot handle the interpretation and reasoning of the generation process well, which reduces the reliability and usability of the generated scenarios. With the rapid development of Foundation Models, especially the large language model (LLM), we can conduct scenario generation via more powerful tools. In this article, we propose LLMScenario, a novel LLM-driven scenario generation framework, which is composed of scenario prompt engineering, LLM scenario generation, and evaluation feedback tuning. The minimum scenario description specific to LLM is given by scenario analysis and ablation studies. We also appropriately design the score functions in terms of reality and rarity to evaluate the generated scenarios. The model performance is further enhanced through chain-of-thoughts and experiences. Different LLMs are also compared with our framework. Experimental results on naturalistic datasets demonstrate the effectiveness of LLMScenario, which can provide solid support for scenario engineering in Industry 5.0.

Multitask learning has led to great success in many deep learning applications during the last decade. However, recent experiments have demonstrated that the performance of multitask learning depends on how to balance the relationship between different tasks. Therefore, many approaches have been proposed to adjust per-task gradient directions or design a more appropriate task reweighting scheme based on task-level statistics. In this article, we discuss how to boost the performance of multitask learning by using more fine-grained sample gradient information. To this end, we propose the concept of sample gradient similarity, which measures the agreement between the sample gradient for a task and the true gradient. Based on this concept, greater weight is assigned to more consistent tasks and more robust training samples to improve the training process of multitask learning. Extensive experimental results show that our proposed method outperforms the state-of-the-art algorithms on a series of challenging multitask datasets.

Recent studies have made dozens of attempts to apply multi-agent deep reinforcement learning (MARL) for large-scale traffic signal control. However, most related studies have ignored how to master arterial traffic signal control. We cannot easily extract useful information and search solution space because the arterial traffic control problem has large state-action spaces. Here we tackle these issues by proposing a multi-agent attention-base soft actor-critic (MASAC) model to master arterial traffic control. Specifically, we implement the attention mechanism in the actor and critic network to enhance traffic information extraction ability. More importantly, we are the first to apply the soft actor-critic (SAC) algorithm to train the arterial traffic control model to search more solution spaces. Testing results indicate that the MASAC method significantly outperforms existing MARL algorithms and the multiband-based method. These findings can help researchers to design better model structures for other MARL problems.

Connected and automated vehicles (CAVs) are expected to play a vital role in the emerging intelligent transportation system. In recent years, researchers have proposed various cooperative driving methods for CAVs, and there is an urgent need for a generic and unified traffic simulator to simulate and evaluate these methods. However, traditional traffic simulators have two critical deficiencies for CAV simulation needs: 1) the planning and dynamical modeling of vehicles in traditional simulators are based on a feedback mode, which is incompatible with the feed-forward decision and planning that CAVs commonly adopt; 2) the traditional simulators cannot provide typical traffic scenarios and corresponding standardized algorithms for multi-CAV cooperative driving. In this paper, we introduce CAVSim, a novel microscopic traffic simulator for CAVs, to address these deficiencies. CAVSim is developed modularly according to the emerging technology of the CAV environment, emphasizes feed-forward decision and planning for CAVs, and highlights the cooperative decision and planning components in the CAV environment. CAVSim incorporates rich and typical traffic scenarios and provides standardized cooperative driving algorithms and comparable performance metrics for multi-CAV cooperative driving. With CAVSim, researchers can conveniently deploy decision, planning, and control methods for CAVs at different levels, evaluate their performance, compare them with the standardized algorithms incorporated in CAVSim, and even further explore their impact on traffic flow. As a unified platform for CAVs, CAVSim can facilitate the studies on CAVs and promote the advancement of methods and techniques for CAVs.

Lane change is a common-yet-challenging driving behavior for automated vehicles. To improve the safety and efficiency of automated vehicles, researchers have proposed various lane-change decision models. However, most of the existing models consider lane-change behavior as a one-player decision-making problem, ignoring the essential multi-agent properties when vehicles are driving in traffic. Such models lead to deficiencies in interaction and collaboration between vehicles, which results in hazardous driving behaviors and overall traffic inefficiency. In this paper, we revisit the lane-change problem and propose a bi-level lane-change behavior planning strategy, where the upper level is a novel multi-agent deep reinforcement learning (DRL) based lane-change decision model and the lower level is a negotiation based right-of-way assignment model. We promote the collaboration performance of the upper-level lane-change decision model from three crucial aspects. First, we formulate the lane-change decision problem with a novel multi-agent reinforcement learning model, which provides a more appropriate paradigm for collaboration than the single-agent model. Second, we encode the driving intentions of surrounding vehicles into the observation space, which can empower multiple vehicles to implicitly negotiate the right-of-way in decision-making and enable the model to determine the right-of-way in a collaborative manner. Third, an ingenious reward function is designed to allow the vehicles to consider not only ego benefits but also the impact of changing lanes on traffic, which will guide the multi-agent system to learn excellent coordination performance. With the upper-level lane-change decisions, the lower-level right-of-way assignment model is used to guarantee the safety of lane-change behaviors. The experiments show that the proposed approaches can lead to safe, efficient, and harmonious lane-change behaviors, which boosts the collaboration between vehicles and in turn improves the safety and efficiency of the overall traffic. Moreover, the proposed approaches promote the microscopic synchronization of vehicles, which can lead to the macroscopic synchronization of traffic flow.

Connected and automated vehicles (CAVs) have the potential to significantly improve the safety and efficiency of traffic. One revolutionary CAV’s impact on transportation system is cooperative driving that turns signalized intersections to be signal-free and boosts traffic efficiency by better organizing the passing order of CAVs. However, how to get the optimal passing order is an NP-hard problem (specifically, enumerating based algorithm takes days to find the optimal solution to a 20-CAV scenario). Here, we introduce a novel cooperative driving algorithm (AlphaOrder) that combines offline deep learning and online tree searching to find a near-optimal passing order in real-time. AlphaOrder builds a pointer network model from solved scenarios and generates near-optimal passing orders instantaneously for new scenarios. For the scenarios with 40 CAVs, AlphaOrder reduces the travel delay by more than 20% on average compared to the best-so-far MCTS based algorithm. Moreover, our algorithm provides a general approach to managing preemptive resource sharing between multi-agents (e.g., scheduling multiple automated guided vehicles (AGVs) and unmanned aerial vehicles (UAVs) at conflicting areas).

Improving the generalization performance of deep neural networks (DNNs) trained by minibatch stochastic gradient descent (SGD) has raised lots of concerns from deep learning practitioners. The standard simple random sampling (SRS) scheme used in minibatch SGD treats all training samples equally in gradient estimation. In this article, we study a new data selection method based on the intrinsic property of the training set to help DNNs have better generalization performance. Our theoretical analysis suggests that this new sampling scheme, called the nontypicality sampling scheme, boosts the generalization performance of DNNs through biasing the solution toward wider minima, under certain assumptions. We confirm our findings experimentally and show that more variants of minibatch SGD can also benefit from the new sampling scheme. Finally, we discuss an extension of the nontypicality sampling scheme that holds promise to enhance both generalization performance and convergence speed of minibatch SGD.

Interest in autonomous driving (AD) and intelligent vehicles (IVs) is growing at a rapid pace due to the convenience, safety, and economic benefits. Although a number of surveys have reviewed research achievements in this field, they are still limited in specific tasks and lack systematic summaries and research directions in the future. Our work is divided into three independent articles and the first part is a survey of surveys (SoS) for total technologies of AD and IVs that involves the history, summarizes the milestones, and provides the perspectives, ethics, and future research directions. This is the second part (Part I for this technical survey) to review the development of control, computing system design, communication, high-definition map (HD map), testing, and human behaviors in IVs. In addition, the third part (Part II for this technical survey) is to review the perception and planning sections. The objective of this article is to involve all the sections of AD, summarize the latest technical milestones, and guide abecedarians to quickly understand the development of AD and IVs. Combining the SoS and Part II, we anticipate that this work will bring novel and diverse insights to researchers and abecedarians, and serve as a bridge between past and future.

The key procedure of haze image synthesis with adversarial training lies in the disentanglement of the feature involved only in haze synthesis, i.e.,the style feature, from the feature representing the invariant semantic content, i.e.,the content feature. Previous methods introduced a binary classifier to constrain the domain membership from being distinguished through the learned content feature during the training stage, thereby the style information is separated from the content feature. However, we find that these methods cannot achieve complete content-style disentanglement. The entanglement of the flawed style feature with content information inevitably leads to the inferior rendering of haze images. To address this issue, we propose a self-supervised style regression model with stochastic linear interpolation that can suppress the content information in the style feature. Ablative experiments demonstrate the disentangling completeness and its superiority in density-aware haze image synthesis. Moreover, the synthesized haze data are applied to test the generalization ability of vehicle detectors. Further study on the relation between haze density and detection performance shows that haze has an obvious impact on the generalization ability of vehicle detectors and that the degree of performance degradation is linearly correlated to the haze density, which in turn validates the effectiveness of the proposed method.

Autonomous vehicles (AVs) are widely believed to be good for improving transportation safety and efficiency. However, recent fatal accidents by some of their prototypes remind us that there are no operationalizable and quantitative safe driving strategies available for an AV in a wide range of situations to avoid collisions. In contrast with many recent studies that focused on ethical considerations when AVs are facing unavoidable harms, we study how to proactively prevent collisions by setting up a set of decision rules for AVs to determine the right-of-way efficiently. Notably, we summarize three essential principles for AVs designing to increase driving safety, and establish a rule-based nine-step communication-decision model to implement them. Our method is constructed by analyzing how human drivers solve potential conflicts. The decision rules are designed to be ambiguity-free and readily computable with the least communication so that human drivers and AVs could easily understand each other in terms of their behaviors and intentions of. We have demonstrated the effectiveness of our method by comparing it with some alternative approaches.

Accurate trajectory prediction of surrounding vehicles is important for automated vehicles. To solve several existing problems of maneuver-based trajectory prediction, we propose four targeted solutions and establish a trajectory prediction model that integrates semi-supervised And-or Graph (AOG) and Spatio-temporal LSTM (ST-LSTM). To reduce the dependence on the well-labeled dataset, we introduce the concept of sub-maneuvers to improve the classifications of vehicle movements based on the given rough maneuver labels. AOG is used as the backbone of the probabilistic motion inference considering sub-maneuvers. We only define the basic units and inference logics of AOG and design a semi-supervised approach to directly learn the sub-maneuvers and the inference model structure from the training data, without manually specifying the structure (layers and nodes) of the inference model. This approach helps to avoid excessive artificial design or biases. The learned hierarchical motion inference model improves the interpretability of the overall trajectory prediction process. To utilize vehicle interaction information and further yield more accurate prediction, we adopt two different methods to consider vehicle interaction in the two sub-models (maneuver recognition and trajectory prediction). The experiment on NGSIM I-80 dataset shows that the maneuver-based model proposed in this paper (AOG-ST and refined AOG-ST-TB) performs more accurate trajectory prediction results. Although the AOG-ST seems clumsy and slow, we show that it is a flexible and quick model for trajectory prediction for various driving scenarios through the discussion and experiment.

Road shoulders and slopes often appear in unstructured environments. They make 2.5D vehicle trajectory planning commonly seen in our daily life, which lies on a 2D manifold embedded in a 3D space. The height difference of these terrains brings spatially dependent constraints on vehicle maneuvers, such as the limit on vehicle steering for vehicle tire protection when a vehicle approaches a road shoulder edge. These constraints have an “if-else” structure since they are activated only when the vehicle passes through the local area with a height difference, making the restriction on variables coupled with the judgment of variables. This makes the application of state-of-art optimization-based planners challenging. To solve this problem, we devise an approximation formulation for these constraints in the trajectory planning optimization problem, whose solution depends on a proper initial guess for the optimizer. We propose a two-stage trajectory planning framework, where the first stage improves the hybrid A* algorithm by adding spatially dependent constraints into node expansion to provide the initial guess. Then, the optimization problem with the formulated spatially dependent constraints is solved for further trajectory smoothness and quality. Finally, the simulation results validate the fast and high-quality planning performance of our proposed framework.

Curvy roads are a particular type of urban road scenario, wherein the curvature of the road centerline changes drastically. This paper is focused on the trajectory planning task for autonomous driving on a curvy road. The prevalent on-road trajectory planners in the Frenet frame cannot impose accurate restrictions on the trajectory curvature, thus easily making the resultant trajectories beyond the ego vehicle’s kinematic capability. Regarding planning in the Cartesian frame, selection-based methods suffer from the curse of dimensionality. By contrast, optimization-based methods in the Cartesian frame are more flexible to find optima in the continuous solution space, but the new challenges are how to tackle the intractable collision-avoidance constraints and nonconvex kinematic constraints. An iterative computation framework is proposed to accumulatively handle the complex constraints. Concretely, an intermediate problem is solved in each iteration, which contains linear and tractably scaled collision-avoidance constraints and softened kinematic constraints. Compared with the existing optimization-based planners, our proposal is less sensitive to the initial guess especially when it is not kinematically feasible. The efficiency of the proposed planner is validated by both simulations and real-world experiments. Source codes of this work are available athttps://github.com/libai1943/CartesianPlanner.

Motion planning in unstructured and low-speed environments is a fundamental and difficult task for all mobile robotics. If we view motion planning as an optimization problem, the non-convex collision avoidance constraints and the nonlinear vehicle dynamic constraints make motion planning challenging and time-consuming. In this paper, we propose a Successive Linearization in Feasible Set (SLiFS) algorithm to address these two difficulties. SLiFS consists of two steps. The first step is to iteratively construct convex feasible sets around the current trajectory to approximate non-convex collision avoidance constraints. The second step is to successively linearize the nonlinear dynamic constraints along the current trajectory and further penalize them into the objective function to avoid infeasible linearized constraints, so that the current trajectory can be reshaped within the obtained convex feasible sets by iteratively solving the linearized optimization problem. The main innovation of SLiFS algorithm is that we consider the$L_{1}$norm type penalty function in the second step. We find that the sparsity of the$L_{1}$norm might help to satisfy the robotic dynamic constraints by numerical experiments. Numerical testing results show that our proposed SLiFS algorithm has a high success rate to find feasible trajectories and costs much less time than the classical interior-point method.

In this paper, we study how to learn a harmonious deep reinforcement learning (DRL) based lane-changing strategy for autonomous vehicles without Vehicle-to-Everything (V2X) communication support. The basic framework of this paper can be viewed as a multi-agent reinforcement learning in which different agents will exchange their strategies after each round of learning to reach a zero-sum game state. Unlike cooperation driving, harmonious driving only relies on individual vehicles’ limited sensing results to balance overall and individual efficiency. Specifically, we propose a well-designed reward that combines individual efficiency with overall efficiency for harmony, instead of only emphasizing individual interests like competitive strategy. Testing results show that competitive strategy often leads to selfish lane change behaviors, anarchy of crowd, and thus the degeneration of traffic efficiency. In contrast, the proposed harmonious strategy can promote traffic efficiency in both free flow and traffic jam than the competitive strategy. This interesting finding indicates that we should take care of the reward setting for reinforcement learning-based AI robots (e.g., automated vehicles) design, when the utilities of these robots are not strictly in alignment.

The properties of cooperative driving strategies for planning and controlling Connected and Automated Vehicles (CAVs) at intersections range from some that achieve highly efficient coordination performance to others whose implementation is computationally fast. This paper comprehensively compares the performance of four representative strategies in terms of travel time, energy consumption, computation time, and fairness under different conditions, including the geometric configuration of intersections, asymmetry in traffic arrival rates, and the relative magnitude of these rates. Our simulation-based study has led to the following conclusions: 1) The Monte Carlo Tree Search (MCTS)-based strategy achieves the best traffic efficiency and has great performance in fuel consumption; 2) MCTS and Dynamic Resequencing (DR) strategies both perform well in all metrics of interest. If the computation budget is adequate, the MCTS strategy is recommended; otherwise, the DR strategy is preferable; 3) An asymmetric intersection has a noticeable impact on the strategies, whereas the influence of the arrival rates can be neglected. When the geometric shape is asymmetrical, the modified First-In-First-Out (FIFO) strategy significantly outperforms the FIFO strategy and works well when the traffic demand is moderate, but their performances are similar in other situations; and 4) Improving traffic efficiency sometimes comes at the cost of fairness, but the DR and MCTS strategies can be adjusted to realize a better trade-off between various performance metrics by appropriately designing their objective functions.

We address the problem of optimally controlling Connected and Automated Vehicles (CAVs) arriving from four multi-lane roads at a signal-free intersection where they conflict in terms of safely crossing (including turns) with no collision. The objective is to jointly minimize the travel time and energy consumption of each CAV while ensuring safety. This problem was solved in prior work for single-lane roads. A direct extension to multiple lanes on each road is limited by the computational complexity required to obtain an explicit optimal control solution. Instead, we propose a general framework that first converts a multi-lane intersection problem into a decentralized optimal control problem for each CAV with less conservative safety constraints than prior work. We then employ a method combining optimal control and control barrier functions, which has been shown to efficiently track tractable unconstrained optimal CAV trajectories while also guaranteeing the satisfaction of all constraints. Simulation examples are included to show the effectiveness of the proposed framework under symmetric and asymmetric intersection geometries and different CAV sequencing policies.

Though automated vehicles (AVs) are believed to play a crucial role in future transport, human driving vehicles will share the road with automated vehicles for a relatively long period. So, we need to enable automated vehicles to run along with human drivers especially when they may have conflicts in the right of way. One key problem is how to appropriately model human driving behaviors and quickly simulate their actions when training/testing automated vehicles. Many existing models were originally built for traffic flow studies and may not be suitable for automated vehicles studies. In this paper, we propose a set of new principles of human driving behaviors modeling and simulations. Then, we propose a Data-Driven Simulator (D2Sim) model for human behavior learning, description, and vehicle interaction simulation. In contrast to conventional microscopic traffic flow models, the D2Sim is a trajectory generation model that accepts rich driving environment information (e.g., lane geometry, crosswalks, traffic signals, surrounding vehicles, etc.). Different from many empirical trajectory records replay models, we can arbitrarily set the long-term intentions of the simulated vehicles and intentionally design the corner cases that had not been observed in practice. In addition, the D2Sim adopts adversarial learning to comprehend complex yet stochastic human driving behaviors from empirical data. Testing results show that the proposed model can quickly generate high-resolution trajectory data for training and testing.

Deep learning has become the most powerful machine learning tool in the last decade. However, how to efficiently train deep neural networks remains to be thoroughly solved. The widely used minibatch stochastic gradient descent (SGD) still needs to be accelerated. As a promising tool to better understand the learning dynamic of minibatch SGD, the information bottleneck (IB) theory claims that the optimization process consists of an initial fitting phase and the following compression phase. Based on this principle, we further study typicality sampling, an efficient data selection method, and propose a new explanation of how it helps accelerate the training process of the deep networks. We show that the fitting phase depicted in the IB theory will be boosted with a high signal-to-noise ratio of gradient approximation if the typicality sampling is appropriately adopted. Furthermore, this finding also implies that the prior information of the training set is critical to the optimization process, and the better use of the most important data can help the information flow through the bottleneck faster. Both theoretical analysis and experimental results on synthetic and real-world datasets demonstrate our conclusions.

Intelligent vehicle testing received quickly increasing attention due to the intermittent accidents of intelligent vehicle prototypes that occurred recently. In this paper, we investigate the theoretical underpinnings of such testing and establish a rigid analyzing framework for general intelligence testing problems by borrowing the ideas of Probably Approximately Correct (PAC) learning. Our focus is on the relationship between the number of sampled scenarios and the testing efficiency. We explain various existing algorithms within this new framework and clarify some misconceptions about the reasoning underpinning these methods. We show that intelligent vehicles are testable if the testing scenarios are well defined and appropriately sampled. Moreover, we propose a sampling strategy to generate new challenging scenarios to boost testing efficiency.

Achieving excellent generalization on perceiving real traffic scenarios with diversity is the long-term goal for building robust autonomous driving systems. In this paper, we propose to discover potential shortness of certain perception module by analyzing its worst-scenario performance. However, with the benchmark datasets growing huge and tremendous, exhaustive searching for the worst perception scenario (WPS) seems to be time consuming and unnecessary. To address, we present an automatic searching scheme empowered by reinforcement learning. In this case, worst scenario mining is formulated as a discrete search problem. A single layer recurrent neural network with LSTM neurons is employed to predict WPS according to the searching reward, which is optimized by a vanilla policy gradient method. Moreover, to deal with the imbalanced distribution of real traffic scenarios, a KNN-like retrieval is utilized for searching the closest scenario samples. Effective yet efficient, the proposed method has been validated by finding the most challenging scenarios for various vehicle detectors on KITTI, BDD100k and our own benchmark set EVB. Further experiments reveal that detection networks with structural similarity share the similar WPS.

Internet of Things (IoT) is becoming increasingly prevalent in transportation systems. The traffic system depends on safer, faster, and more intelligent vehicles. Vehicular networks [vehicle-to-vehicle (V2V) and vehicle-to- Infrastructure (V2I)] and automated driving technique are two of the cornerstone technologies enabling the construction of the future-generation highly functional and intelligent transportation system. The IoT-based transportation system can provide enormous connections of devices and sensors for the networked automated vehicles. The capacity of connected autonomous vehicles (CAVs) is expected to be dramatically enhanced by employing IoT techniques.

In this paper, we propose a data-adaptive non-parametric kernel learning framework in margin based kernel methods. In model formulation, given an initial kernel matrix, a data-adaptive matrix with two constraints is imposed in an entry-wise scheme. Learning this data-adaptive matrix in a formulation-free strategy enlarges the margin between classes and thus improves the model flexibility. The introduced two constraints are imposed either exactly (on small data sets) or approximately (on large data sets) in our model, which provides a controllable trade-off between model flexibility and complexity with theoretical demonstration. In algorithm optimization, the objective function of our learning framework is proven to be gradient-Lipschitz continuous. Thereby, kernel and classifier/regressor learning can be efficiently optimized in a unified framework via Nesterov's acceleration. For the scalability issue, we study a decomposition-based approach to our model in the large sample case. The effectiveness of this approximation is illustrated by both empirical studies and theoretical guarantees. Experimental results on various classification and regression benchmark data sets demonstrate that our non-parametric kernel learning framework achieves good performance when compared with other representative kernel learning based algorithms.

Connected and automated vehicles (CAVs) show great potential to improve both traffic efficiency and safety by sharing information. This paper addresses the problem of coordinating two strings of vehicles at highway on-ramps efficiently and safely in the longitudinal direction. A rule-based adjusting algorithm is proposed to achieve a near-optimal merging sequence for vehicles coming from the mainline and entering through the ramp. Optimality analysis indicates that the proposed method performs very well compared with the global optimal solutions. Furthermore, to investigate the effectiveness and robustness of the proposed method, simulation-based case studies are carried out under both balanced and unbalanced scenarios. The results are compared with two other control strategies (i.e., rule-based methods and optimization-based methods) in terms of throughput, delay, computational cost, and fuel consumption.

In this paper, we propose a new cooperative driving strategy for connected and automated vehicles (CAVs) at unsignalized intersections. Based on the tree representation of the solution space for the passing order, we combine Monte Carlo tree search (MCTS) and some heuristic rules to find a nearly global-optimal passing order (leaf node) within a very short planning time. Testing results show that this new strategy can keep a good tradeoff between performance and computation flexibility.

Machine learning, especially deep neural networks, has developed rapidly in fields, including computer vision, speech recognition, and reinforcement learning. Although minibatch stochastic gradient descent (SGD) is one of the most popular stochastic optimization methods for training deep networks, it shows a slow convergence rate due to the large noise in the gradient approximation. In this article, we attempt to remedy this problem by building a more efficient batch selection method based on typicality sampling, which reduces the error of gradient estimation in conventional minibatch SGD. We analyze the convergence rate of the resulting typical batch SGD algorithm and compare the convergence properties between the minibatch SGD and the algorithm. Experimental results demonstrate that our batch selection scheme works well and more complex minibatch SGD variants can benefit from the proposed batch selection strategy.

In mobile crowd sensing (MCS), appropriate rewards are always expected to compensate the participants for their consumptions of physical resources and involvements of manual efforts. Hence, the research about incentive mechanisms is essential and useful for MCS, and the existing works focus on optimizing one of the performance, such as the data quality, or the platforms profit, so, how to design an incentive mechanism which not only can guarantee the high data quality but also can maximize the profit of platforms is challenging issue. In this paper, we propose a quality-based online incentive mechanism with unknown users cost distributions for MCS. The scheme uses a Markov Decision Procession (MDP) to online adjust the incentive price such that it tends to the optimal price. Moreover, the data quality request is also considered in our scheme. Furthermore, we evaluate the performances of our proposed scheme with single task and multiple tasks, extensive simulation results show that can perform better in profit and regret than the existing incentive scheme.

th for various services. Yet today’s widely used load balancing scheme, i.e., ECMP, may cause serious congestion when hash collision happens. Recent proposals either push load balancing function to a centralized controller or network edges. However, the centralized schemes are too slow for latency-sensitive flows, while the distributed schemes lack the global view and usually cannot make the best choices. In this paper, based on Software-Defined Networking (SDN), we present a new hybrid load balancing scheme called BLEND. It promotes the cooperation among network components and takes advantage of both global view and fast end-host action. BLEND aims to improve the throughput of big flows and reduce the latency of small and medium flows. It employs a controller to assign paths to big flows to achieve high throughput. In addition, in order to provide guidance for fast distributed load balancing decisions, the controller also calculates the optimal network delay thresholds for small and medium flows, while hosts utilize these thresholds to decide whether to change the current paths. BLEND is practical and easily deployable in the current data center networks. Comprehensive experiments demonstrate that BLEND outperforms both the centralized and distributed schemes and achieves at most 40% reduction in FCT and at most 2.8 times improvement in throughput.

In this paper, we study the testing tasks evaluation and testees ranking problem, in which tasks have different difficulty levels, and testees have different capabilities.We assume that a testee may have a probability to pass a certain task so as to allow certain uncertainty. The goal of this problem is to simultaneously determine the relative difficulty level of each testing task and the relative capability of every testee, purely based on the test outcome. We design two models to solve this problem. The first one assumes that the test outcome follows a certain Bernoulli distribution; while the second one assumes that the test outcome follows a certain Bernoulli distribution with the beta distribution-type a priori knowledge. Then, we form the original problem into likelihood estimation problems and solve them by using coordinate descent algorithms. We show that the beta distribution-type a priori knowledge is needed, when we only carry out a limited number of tests due to time and financial budgets. All these findings are useful to intelligence tests. Finally, we discuss how to extend this statistical learning model for more general cases as well as in a specific case in the field of Computational Social Systems like artificial social cognition evaluation.

To give a high-level summary to current approaches for implementing artificial intelligence (AI), we explain the key commonalities and major differences between Turing's approach and Wiener's approach in this perspective. Especially, the problems, successful achievements, limitations, and future research directions of existing approaches that follow Weiner's ideas are addressed, respectively, aiming to provide readers with a good start point and a roadmap. Some other related topics, for example, the role of human experts in developing AI, are also discussed to seek potential solutions for some existing difficulties.

Spatial-temporal traffic state estimation and the prediction of urban expressways is a vital component of traffic management and information systems. The adaptive smoothing method is one of the most frequently used approaches to estimate traffic states. However, the fixed filter parameters used in existing approaches sometimes fail to characterize traffic dynamics well. To better capture generation, propagation, and mitigation dynamics of traffic congestion, we propose an adaptive rolling smoothing (ARS) approach by dynamically tuning the filter parameters in a rolling horizon scheme for online applications. The fusion of heterogeneous traffic data combines aggregate traffic measurements (e.g., traffic flow rate, time occupancy, and speed collected by remote microwave sensors) and disaggregate information (e.g., timestamps of individual vehicles detected by license plate recognition cameras). A nonlinear traffic flow filter based on the virtual trajectory algorithm is established to reconstruct the spatial-temporal traffic state and estimate experienced travel times of individual vehicles. The results demonstrate the capability and effectiveness of the proposed ARS approach in the historical traffic state estimation and short-term traffic flow prediction. Complicated traffic states of weaving, merging, and diverging segments can be well distinguished by reconstructing time-space speed diagrams. The proposed approach can be extended to develop efficient missing data imputation algorithms and hierarchical control strategies for heterogeneously congested urban expressways.

Traffic flow prediction is one of the most popular topics in the field of the intelligent transportation system due to its importance. Powered by advanced machine learning techniques, especially the deep learning method, prediction accuracy noticeably increases in recent years. However, most existing methods applied a data-driven paradigm and tend to ignore the outliers, which result in poor performance while handling burst phenomena in the traffic system. To overcome this problem, the prediction model needs to recognize different patterns and handle them in different ways. In this paper, we propose a new prediction model (called pattern sensitive network) that can handle different traffic patterns automatically. By using adversarial training, our model can make more accurate predictions in unusual states without compromising its performance in usual states. Experiments demonstrate that our method can work well in both usual traffic states and unusual traffic states.

Kernel learning is a fundamental technique that has been intensively studied in the past decades. For the complicated practical tasks, the traditional "shallow" kernels (e.g., Gaussian kernel and sigmoid kernel) are not flexible enough to produce satisfactory performance. To address this shortcoming, this paper introduces a nonlinear layer in kernel learning to enhance the model flexibility. This layer is pairwise, which fully considers the coupling information among examples. So our model contains a fixed single mapping layer (i.e. a Gaussian kernel) as well as a nonlinear pairwise layer, thereby achieving better flexibility than the existing kernel structures. Moreover, the proposed structure can be seamlessly embedded to Support Vector Machines (SVM), of which the training process can be formulated as a joint optimization problem including nonlinear function learning and standard SVM optimization. We theoretically prove that the objective function is gradient-Lipschitz continuous, which further guides us how to accelerate the optimization process in a deep kernel architecture. Experimentally, we find that the proposed structure outperforms other state-ofthe-art kernel-based algorithms on various benchmark datasets, and thus the effectiveness of the incorporated pairwise layer with its training approach is demonstrated.

This paper proposes a learning-based predictive control technique for self-driving hybrid electric vehicle (HEV). This approach is a hierarchical framework. The higher-level is a human-like driver model, which is applied to predict accelerations in the car following situation to replicate a human driver's demonstrations. The lower-level is a reinforcement learning (RL)-based controller, which enforces the battery and fuel consumption constraints to improve energy efficiency of HEV. In addition, we present induced matrix norm (IMN) to handle cases that the training data cannot provide sufficient information on how to operate in current driving situation. Simulation results illustrate that the proposed method can reproduce human driver's driving style and promote fuel economy.

The ability of modeling and predicting vehicle motion activities is important for automated vehicles. In this paper, we propose an And-Or Graph based model to give a simple and clear description of motion activities. Compared to other models, this new model relaxes the Markov property requirement in transition between activities and is thus more flexible. The parameters of this model can be easily learned from data. Using the trained new model, we can predict the on-going motion activity label and its corresponding probability. Experiments show that a high prediction accuracy (97%) can be achieved by this new model.

Understanding travelers' route choice behavior is a key task in transportation studies. In this paper, we analyze the route choices of Beijing taxi drivers regarding four frequently mentioned cost-based route choice rules: pursuing shortest time, or distance, avoiding passing signalized intersections, or making left/right turnings. Test results show that route choices of drivers are not always optimal according to either of these rules. Instead, we argue that taxi drivers are bounded rational and usually choose a satisfactory route that belongs to one of the few routes that consume the shortest times. Test results show that more than 90% observed traces can be explained by this simple explanation.

Risky driving is a major cause of traffic accidents. In this paper, we propose a new method that recognizes risky driving behaviors purely based on vehicle speed time series. This method first retrieves the important distribution pattern of the sampled positive speed-change (value and duration) tuples for individual drivers within different speed ranges. Then, it identifies the risky drivers based on different patterns of drivers. Tests show the effectiveness of the proposed method. Since speed measurement is available on most of the newly build vehicles, this method can be easily implemented and used. The conclusion is useful to many traffic applications, e.g., driver training and insurance pricing.

In this paper, we propose a deep neural network-based car-following model that has two distinctive properties. First, unlike most existing car-following models that take only the instantaneous velocity, velocity difference, and position difference as inputs, this new model takes the velocities, velocity differences, and position differences that were observed in the last few time intervals as inputs. That is, we assume that drivers’ actions are temporally dependent in this model and try to embed prediction capability or memory effect of human drivers in a natural and efficient way. Second, this car-following model is built in a data-driven way, in which we reduce human interference to the minimum degree. Specially, we use recently developing deep neural networks rather than conventional neural networks to establish the model, since deep learning technique provides us more flexibility and accuracy to describe complicated human actions. Tests on empirical trajectory records show that this deep neural network-based car-following model yield significantly higher simulation accuracy than existing car-following models. All these findings provide a novel way to study traffic flow theory and traffic simulations.

In this paper, we propose a novel adaptive piecewise linear model for dynamic system identification. It has four unique features. First, the model designs a new kind of basis function for function approximation. It maintains the uniform shape for each basis function, so as to achieve a satisfactory tradeoff between generalization ability and model complexity. Second, the model takes the structure of basis functions as decision variables to optimize the formulated identification problems instead of taking expansion coefficients as decision variables as proposed by many existing approaches. Third, we establish an incremental design strategy to solve the system identification problems. In each step of the identification, the selection of optimal basis function is a Lipschitz continuous optimization problem that is likely to be easily handled with some mature toolboxes. This incremental design strategy greatly reduces the estimation cost. Fourth, we introduce a smoothing mechanism to avoid overfitting, when the output of dynamic systems is disturbed by noise. Tests on several benchmark dynamic systems demonstrate the potential of the proposed model.

With the development of intelligent technologies, self-driving vehicles are considered as a promising solution against accident, traffic congestion and pollution problems. Intelligent vehicle techniques have been the research focus all over the world. However, full self-driving vehicles are still far away from its realization and extensive application due to safety requirements and cost considerations. As a novel breakthrough, PArallel VEhicles (PAVE) incorporate the ACP theory, which facilitates real-time interaction and optimization of the actual self-driving vehicles and the artificial ones. As a result, PAVE can maintain intelligent control of the actual self-driving vehicles and achieve the global optimization via software-defined self-driving vehicles, intelligent infrastructure construction, and parallel control center. Besides, PAVE can effectively reduce the cost of high-precision equipments on the actual self-driving vehicles via remote processing and intelligent road(side) infrastructure, and also achieve improved safety and reliability via remote control, guidance and planning.

Parking is one basic function of autonomous vehicles. However, parking still remains difficult to be implemented, since it requires to generate a relatively long-term series of actions to reach a certain objective under complicated constraints. One recently proposed method used deep neural networks(DNN) to learn the relationship between the actual parking trajectories and the corresponding steering actions, so as to find the best parking trajectory via direct recalling. However, this method can only handle a special vehicle whose dynamic parameters are well known. In this paper, we use transfer learning technique to further extend this direct trajectory planning method and master general parking skills. We aim to mimic how human drivers make parking by using a specially designed deep neural network. The first few layers of this DNN contain the general parking trajectory planning knowledge for all kinds of vehicles; while the last few layers of this DNN can be quickly tuned to adapt various kinds of vehicles. Numerical tests show that, combining transfer learning and direct trajectory planning solution, our new approach enables automated vehicles to convey the knowledge of trajectory planning from one vehicle to another with a few try-and-tests.

Parking control problems remain to be fully solved for autonomous vehicles. Existing approaches usually first design a reference parking trajectory that does not exactly match vehicle dynamic constraints and then apply certain online negative feedback control to make the vehicle roughly track this reference trajectory. In this paper, we propose a novel trajectory planning method that directly links the actual parking trajectories and the steering actions to find the best parking trajectory. Tests show that this new approach has high reliability and less computation cost. Moreover, we also discuss how to counter with trajectory planning errors that are caused by model uncertainty in this paper. We show that an appropriate combination of feedforward trajectory planning and online feedback control can solve such problems.

In this paper, we provide a critical discussion of parallel systems for traffic control. Comparing each component of classical traffic control methodologies and their counterparts in parallel traffic control methodologies, we clarify the prominent features, possible benefits, and current shortcomings of parallel approaches. The conclusions may help achieve a better understanding of parallel traffic control and other parallel systems.

A battle between two Chinese taxi booking mobile apps, namely, Didi and Kuaidadi, had recently occurred in early 2014. These two apps, which are backed by Internet giants Tencent and Alipay, gave promotion fees to taxi drivers for each deal made and also allowed each taxi passenger to save some money, when a customer had taken a taxi through the app and paid the fare through the mobile payment method. As expected, the taxi service pattern had been greatly changed during this battle. To address the debates on social justice, equity, and improvements of taxi service, we collect 37-day trip data of over 9000 taxis in Beijing to study the influence of this pattern change. In the first 18 days, the battle had not occurred and in the remaining 19 days, the battle is white-hot. We quantitatively demonstrate how several important service indices (e.g., the traveling distances and idle time lengths) of taxi drivers had been changed. The spatial-temporal traveling patterns of taxis are then studied. Based on comprehensive analysis, the benefits and drawbacks brought by money promotion are finally discussed. The obtained results indicate that productively employing big data can help answer some important questions attracting the interest of the whole society.

This paper discusses the trend modeling for traffic time series. First, we recount two types of definitions for a long-term trend that appeared in previous studies and illustrate their intrinsic differences. We show that, by assuming an implicit temporal connection among the time series observed at different days/locations, the PCA trend brings several advantages to traffic time series analysis. We also describe and define the so-called short-term trend that cannot be characterized by existing definitions. Second, we sequentially review the role that trend modeling plays in four major problems in traffic time series analysis: abnormal data detection, data compression, missing data imputation, and traffic prediction. The relations between these problems are revealed, and the benefit of detrending is explained. For the first three problems, we summarize our findings in the last ten years and try to provide an integrated framework for future study. For traffic prediction problem, we present a new explanation on why prediction accuracy can be improved at data points representing the short-term trends if the traffic information from multiple sensors can be appropriately used. This finding indicates that the trend modeling is not only a technique to specify the temporal pattern but is also related to the spatial relation of traffic time series.

Freeway traffic state estimation is a vital component of traffic management and information systems. Macroscopic-model-based traffic state estimation methods are widely used in this field and have gained significant achievements. However, tests show that the inherent randomness of traffic flow and uncertainties in the initial conditions of models, model parameters, and model structures all influence traffic state estimations. To improve the estimation accuracy, this paper presents an ensemble learning framework to appropriately combine estimation results from multiple macroscopic traffic flow models. This framework first assumes that any models existing are imperfect and have their own strengths/weaknesses. It then estimates the online traffic states in a rolling horizon scheme. This framework automatically ensembles the information from each individual estimation model based on their performance during the selected regression horizon. In particular, we discuss three weighting algorithms, namely, least square regression, ridge regression, and lasso, which represent different presumptions of model capabilities. A field test based on real freeway measurements indicates that lasso ensemble best handles various uncertainties and improves estimation accuracy significantly. It should be also pointed out that the proposed framework is a flexible tool to assemble nonmodel-based traffic estimation algorithms. This framework can be also extended for many other applications, including traffic flow prediction and travel-time prediction.

During the last 60 years, incessant efforts have been made to improve the efficiency of traffic control systems to meet ever-increasing traffic demands. Some recent works attempt to enhance traffic efficiency via vehicle-to-vehicle communications. In this paper, we aim to give a survey of some research frontiers in this trend, identifying early-stage key technologies and discussing potential benefits that will be gained. Our survey focuses on the control side and aims to highlight that the design philosophy for traffic control systems is undergoing a transition from feedback character to feedforward character. Moreover, we discuss some contrasting preferences in the design of traffic control systems and their relations to vehicular communications. The first pair of contrasting preferences are model-based predictive control versus simulation-based predictive control. The second pair are global planning-based control versus local self-organization-based control. The third pair are control using rich information that may be highly redundant versus control using concise information that is necessary. Both the potentials and drawbacks of these control strategies are explained. We hope these comparisons can shed some interesting light on future traffic control studies.

Travel time serves as a fundamental measurement for transportation systems and becomes increasingly important to both drivers and traffic operators. Existing speed interpolation algorithms use the average speed time series collected from upstream and downstream detectors to estimate the travel time of a road link. Such approaches often result in inaccurate estimations or even systematic bias, particularly when the real travel times quickly vary. To get rid of this problem, Coifman proposed a creative interpolation algorithm based on kinetic-wave models. This algorithm reconstructs vehicle trajectories according to the velocities and the headways of vehicles. However, it sometimes gives significant biased estimation, particularly when jams emerge from somewhere between the upstream and downstream detectors. To make an amendment, we design a new algorithm based on the temporal-spatial queueing model to describe the fast travel-time variations using only the speed and headway time series that is measured at upstream and downstream detectors. Numerical studies show that this new interpolation algorithm could better utilize the dynamic traffic flow information that is embedded in the speed/headway time series in some special cases.

In this paper, we propose a simple temporal-spatial queueing model to quantitatively address some typical congestion patterns that were observed around on/off-ramps. In particular, we examine three prime factors that play important roles in ramping traffic scenarios: the time τinfor a vehicle to join a jam queue, the time τoutfor this vehicle to depart from this jam queue, and the time intervalTfor the ramping vehicle to merge into the mainline. Based on Newell's simplified car-following model, we show how τinchanges with the main road flow rateqmain. Meanwhile,Tis the reciprocal of the ramping road flow rateqramp. Thus, we analytically derive the macroscopic phase diagram plotted on theqmain-versus-qrampplane and τin-versus-Tplane based on the proposed model. Further study shows that the new queueing model not only reserves the merits of Newell's model on the microscopic level but helps quantify the contributions of these parameters in characterizing macroscopic congestion patterns as well. Previous approaches distinguished phases merely through simulations, but our model could derive analytical boundaries for the phases. The phase transition conditions obtained by this model agree well with simulations and empirical observations. These findings help reveal the origins of some well-known phenomena during traffic congestion.

This paper provides a survey of recent works on cognitive cars with a focus on driver-oriented intelligent vehicle motion control. The main objective here is to clarify the goals and guidelines for future development in the area of advanced driver-assistance systems (ADASs). Two major research directions are investigated and discussed in detail: (1) stimuli-decisions-actions, which focuses on the driver side, and (2) perception enhancement-action-suggestion-function-delegation, which emphasizes the ADAS side. This paper addresses the important achievements and major difficulties of each direction and discusses how to combine the two directions into a single integrated system to obtain safety and comfort while driving. Other related topics, including driver training and infrastructure design, are also studied.

In this paper, we propose a fast greedy search algorithm for optimal single-cycle signal timing at individual oversaturated intersections. We illustrate the efficiency of the algorithm with a numerical example in the literature.

In this paper, we link two research directions of road traffic-the mesoscopic headway distribution model and the microscopic vehicle interaction model-together to account for the empirical headway/spacing distributions. A unified car-following model is proposed to simulate different driving scenarios, including traffic on highways and at intersections. Unlike our previous approaches, the parameters of this model are directly estimated from the Next Generation Simulation (NGSIM) Trajectory Data. In this model, empirical headway/spacing distributions are viewed as the outcomes of stochastic car-following behaviors and the reflections of the unconscious and inaccurate perceptions of space and/or time intervals that people may have. This explanation can be viewed as a natural extension of the well-known psychological car-following model (the action point model). Furthermore, the fast simulation speed of this model will benefit transportation planning and surrogate testing of traffic signals.

In this letter, we show that under the assumption of high resolution, the quantization errors of fGn and fBm signals with uniform quantizer can be treated as uncorrelated white noises.

The missing data problem greatly affects traffic analysis. In this paper, we put forward a new reliable method called probabilistic principal component analysis (PPCA) to impute the missing flow volume data based on historical data mining. First, we review the current missing data-imputation method and why it may fail to yield acceptable results in many traffic flow applications. Second, we examine the statistical properties of traffic flow volume time series. We show that the fluctuations of traffic flow are Gaussian type and that principal component analysis (PCA) can be used to retrieve the features of traffic flow. Third, we discuss how to use a robust PCA to filter out the abnormal traffic flow data that disturb the imputation process. Finally, we recall the theories of PPCA/Bayesian PCA-based imputation algorithms and compare their performance with some conventional methods, including the nearest/mean historical imputation methods and the local interpolation/regression methods. The experiments prove that the PPCA method provides significantly better performance than the conventional methods, reducing the root-mean-square imputation error by at least 25%.

The eight papers in this special issue focus on networking, sensing, and control for networked control systems (NCS). The papers, which are briefly summarized, examine the architectures, algorithms and applications of NCS.

A proper tire friction model is essential to model overall vehicle dynamics for simulation, analysis, or control purposes since a ground vehicle's motion is primarily determined by the friction forces transferred from roads via tires. Motivated by the developments of high-performance antilock brake systems (ABSs), traction control, and steering systems, significant research efforts had been put into tire/road friction modeling during the past 40 years. In this paper, a review of recent developments and trends in this area is presented, with attempts to provide a broad perspective of the initiatives and multidisciplinary techniques for related research. Different longitudinal, lateral, and integrated tire/road friction models are examined. The associated friction-situation monitoring and control synthesis are discussed with a special emphasis on ABS design

Cooperative driving technology with intervehicle communication attracts increasing interests in the last decade. Under cooperative driving guidance, motion of individual vehicles can be conducted in a more safe, deterministic and smooth manner, which is particularly useful to heavy duty vehicles, since their acceleration/ deceleration capacity is relatively low. Specifically in this paper, cooperative driving at adjacent blind intersections (intersections without traffic lights) is studied. A concept named "safety driving patterns" is proposed to represent the collision free movements of all encountered vehicles at intersections. The solution space of allowable movement schedules is then described by a spanning tree in terms of safety driving patterns. The trajectory planning algorithms are also formulated to determine the driving plans with least execution times using schedule trees.

The importance of laboratory experiments in control engineering education could never be overemphasized. Since the equipment and personnel resources are sometimes scarce, the so-called "remote lab " is developed in the last decade to provide every student the opportunity and freedom of experimentation. However, during the practice, it's found that students are in bad need of teachers' guidance when they doing remote experiments; but teachers could not be online all the time. In order to solve this dilemma, the concept of intelligent online autonomous guidance system is introduced and discussed in this short paper. And a simple demo system is also built within the WAVES (Web-based Audio/Video Education System) Lab of the University of Arizona to show the feasibility of this approach.