Recent research in pedestrian simulation often aims to develop realistic behaviors in various situations, but it is challenging for existing algorithms to generate behaviors that identify weaknesses in automated vehicles’ performance in extreme and unlikely scenarios and edge cases. To address this, specialized pedestrian behavior algorithms are needed. Current research focuses on realistic trajectories using social force models and reinforcement learning based models. However, we propose a reinforcement learning algorithm that specifically targets collisions and better uncovers unique failure modes of automated vehicle controllers. Our algorithm is efficient and generates more severe collisions, allowing for the identification and correction of weaknesses in autonomous driving algorithms in complex and varied scenarios.

A connected and automated vehicle safety metric determines the performance of a subject vehicle (SV) by analyzing the data involving the interactions among the SV and other dynamic road users and environmental features. When the data set contains only a finite set of samples collected from the naturalistic mixed multi-modal traffic driving environment, a metric is expected to generalize the safety assessment outcome from the observed finite samples to the unobserved cases by specifying in what domain the SV is expected to be safe and how safe the SV is, statistically, in that domain. However, to the best of our knowledge, none of the existing safety metrics is able to justify the above properties with an operational domain specific, guaranteed complete, and provably unbiased safety evaluation outcome. In this paper, we propose a novel safety metric that involves the$\alpha $-shape and the$\epsilon $-almost robustly forward invariant set to characterize the SV’s almost safe operable domain and the probability for the SV to remain inside the safe domain indefinitely, respectively. The empirical performance of the proposed method is demonstrated in several different operational design domains through a series of cases covering a variety of fidelity levels (real-world and simulators), driving environments (highway, urban, and intersections), road users (car, truck, and pedestrian), and SV driving behaviors (human driver and self driving algorithms).

Modeling mixed-traffic motion and interactions is crucial to assess safety, efficiency, and feasibility of future urban areas. The lack of traffic regulations, diverse transport modes, and the dynamic nature of mixed-traffic zones like shared spaces make realistic modeling of such environments challenging. This paper focuses on the generalizability of the motion model, i.e., its ability to generate realistic behavior in different environmental settings, an aspect which is lacking in existing works. Specifically, our first contribution is a novel and systematic process of formulating general motion models for pedestrians and cars, and application of this process is to extend our Game-Theoretic Social Force Model (GSFM) towards a general model for generating a large variety of motion behaviors of pedestrians and cars from different shared spaces. Our second contribution is to consider different motion patterns of pedestrians and cars by calibrating motion-related features of individual road users and clustering them into groups. We analyze three clustering approaches. The calibration and evaluation of our model are performed on three different shared space data sets. The results indicate that our model can realistically simulate a wide range of motion behaviors and interaction scenarios, and that model performance improves by considering heterogeneity in road users’ motion.

An online evolving method, named evolving finite state machine (e-FSM), is proposed to develop an optimal Markov driving model. The model has the same properties as a standard Markov model, but its states and transition dynamics evolve without human supervision. In this article, we introduce: 1) the principles of the e-FSM’s novel capabilities:online state determinationandonline transition-dynamics identificationfor elaborating the Markov driving model and 2) an advanced online evolving framework (a-OEF) for supporting the reinforcement-learning-based controller’s decision making by using the evolved model. For the evaluation of the proposed methodology and framework, the ego vehicle is controlled by the double deep${Q}$-network (DDQN) controller with and without the a-OEF in the multilane driving scenario where various naturalistic traffic situations are simulated. Simulation results show that better control performance in terms offastandsafedriving is achieved via the DDQN with the a-OEF, which demonstrates that the Markov driving models evolved by the e-FSMs effectively support detecting and revising the controller’s incorrect decision making.

Monocular vision based navigation for automated driving is a challenging task due to the lack of enough information to compute temporal relationships among objects on the road. Optical flow is an option to obtain temporal information from monocular camera images, and has been used widely with the purpose of identifying objects and their relative motion. This work proposes to generate an artificial potential field, i.e. visual potential field, from a sequence of images using sparse optical flow, which is used together with a gradient tracking sliding mode controller to navigate the vehicle to destination without collision with obstacles. The angular reference for the vehicle is computed online. This work considers that the vehicle does not require to have a priori information from the map or obstacles to navigate successfully. The proposed technique is tested both in synthetic and real images.

Deployment of SAE Level 3+ automated vehicles faces validation and certification challenges due to uncertainty and state space size of the operating domain. We propose a validation and testing methodology that aims to minimize unknown unknowns through minimization of scenarios that have not been accounted for, and scenarios that have not been identified due to modeling deficiencies. The methodology utilizes simulators with different levels of fidelity for residual risk handling, functional hierarchies for simplification of complex navigation tasks, and the Backtracking Process Algorithm to identify scenarios of risk significance. The methodology is demonstrated on a scenario with an intersection preceded by a traffic light. Through use of the testing flowchart, we were able to identify and remedy scenarios leading to undesirable events.

The deployment of autonomous vehicles requires safety assurance and performance guarantees of the developed system. However, this is complex due to the number of scenario variations and uncertainty associated with the operating environment. To alleviate this challenge, we propose a model-based validation methodology that relies on a functional hierarchy for the breakdown and simplification of the system navigation functions, and the Backtracking Process Algorithm to identify, trace, and probabilistically quantify risk significant event sequences (scenarios) that lead to Top Events of interest (such as requirement violations). This methodology is demonstrated on a scenario with an occluded pedestrian crossing the road. We are able to identify risks associated with the actor classification problem and sudden changes in behavior of the pedestrian.

Vehicle-pedestrian interaction (VPI) is one of the most challenging tasks for automated driving systems. The design of driving strategies for such systems usually starts with verifying VPI in simulation. This work proposed an improved framework for the study of VPI in uncontrolled pedestrian crossing scenarios. The framework admits the mutual effect between the pedestrian and the vehicle. A multi-state social force based pedestrian motion model was designed to describe the microscopic motion of the pedestrian crossing behavior. The pedestrian model considers major interaction factors such as the accepted gap of the pedestrian's decision on when to start crossing, the desired speed of the pedestrian, and the effect of the vehicle on the pedestrian while the pedestrian is crossing the road. Vehicle driving strategies focus on the longitudinal motion control, for which the feedback obstacle avoidance control and the model predictive control were tested and compared in the framework. The simulation results verified that the proposed framework can generate a variety of VPI scenarios, consisting of either the pedestrian yielding to the vehicle or the vehicle yielding to the pedestrian. The framework can be easily extended to apply different approaches to the VPI problems.

Automated driving in urban settings is challenging. Human participant behavior is difficult to model, and conventional, rule-based Automated Driving Systems (ADSs) tend to fail when they face unmodeled dynamics. On the other hand, the more recent, end-to-end Deep Reinforcement Learning (DRL) based model-free ADSs have shown promising results. However, pure learning-based approaches lack the hard-coded safety measures of model-based controllers. Here we propose a hybrid approach for integrating a path planning pipe into a vision based DRL framework to alleviate the shortcomings of both worlds. In summary, the DRL agent is trained to follow the path planner's waypoints as close as possible. The agent learns this policy by interacting with the environment. The reward function contains two major terms: the penalty of straying away from the path planner and the penalty of having a collision. The latter has precedence in the form of having a significantly greater numerical value. Experimental results show that the proposed method can plan its path and navigate between randomly chosen origin-destination points in CARLA, a dynamic urban simulation environment. Our code is open-source and available online.

Shockwaves lead to speed variation and capacity drop, which hamper the stationarity and throughput of traffic network greatly in reality. In order to dominate or suppress shockwaves, there exist two philosophies: the analytical and numerical methods to investigate various traffic management schemes. However, both are studied completely separately in the existing literature. In this paper, we primarily focus on the uniformity and combination of the two philosophies, especially in terms of traffic evolution and shockwave trajectory extraction. Aiming at exploring the uniformity, numerical methods are equipped with gradient boundary detection and polar-parameter coordinate projection to iteratively calculate traffic states and extract shockwave trajectories as line segments. By contrast, analytical methods derive traffic evolution from fundamental diagrams and present shockwave trajectories as vector graphics in the traffic time-space diagram. Furthermore, to rationally measure the accuracy of extracted trajectories, a calibration model is established to decrease angle and distance errors yielded by the two methods. In the uncontrolled and variable speed limit (VSL)-controlled bottleneck scenarios, the simulation results have shown that: 1) both analytical and numerical methods have the capability to precisely describe traffic evolution and the effects of VSL strategies on recovering traffic throughput; 2) shockwave trajectories extracted by the two methods are coincident, with the significant reduction of relative distance errors from 15%-80% to 0.1%-3%; and 3) it is quite promising to take full advantage of both the visual/intuitive nature of analytical methods and the iterative optimization of numerical methods to investigate efficient traffic management strategies.

This paper proposes a framework to recognize driving intentions and to predict driving behaviors of lane changing on the highway by using externally sensable traffic data from the host-vehicle. The framework consists of a driving characteristic estimator and a driving behavior predictor. A driver's implicit driving characteristic information is uniquely determined and detected by proposed the online-estimator. Neural-network based behavior predictor is developed and validated by testing with the real naturalistic traffic data from Next Generation Simulation (NGSIM), which demonstrates the effectiveness in identifying the driving characteristics and transforming into accurate behavior prediction in real-world traffic situations.

This research presents a novel framework for automated fault detection in cyber-physical systems, with specific focus on large-scale vehicle networks. Agents in a network develop system identification models of themselves which are sent to a local or global authority. The authority excites the system models and generates a fixed-size vector for each one using an echo state network coupled with an autoencoder. The resultant vectors are grouped using standard clustering algorithms, with each group representing similar system model responses. A human expert labels each group once, so that any new group members can be can be associated with the group label. The largest group is assumed to be operating nominally, with all other groups representing a fault or off-nominal operation. We apply our framework to a detailed vehicle cooling system model to demonstrate its efficacy.

Predicting the collective motion of a group of pedestrians (a crowd) under the vehicle influence is essential for the development of autonomous vehicles to deal with mixed urban scenarios where interpersonal interaction and vehicle-crowd interaction (VCI) are significant. This usually requires a model that can describe individual pedestrian motion under the influence of nearby pedestrians and the vehicle. This study proposed two pedestrian trajectory datasets, CITR dataset and DUT dataset, so that the pedestrian motion models can be further calibrated and verified, especially when vehicle influence on pedestrians plays an important role. CITR dataset consists of experimentally designed fundamental VCI scenarios (front, back, and lateral VCIs) and provides unique ID for each pedestrian, which is suitable for exploring a specific aspect of VCI. DUT dataset gives two ordinary and natural VCI scenarios in crowded university campus, which can be used for more general purpose VCI exploration. The trajectories of pedestrians, as well as vehicles, were extracted by processing video frames that come from a down-facing camera mounted on a hovering drone as the recording equipment. The final trajectories of pedestrians and vehicles were refined by Kalman filters with linear point-mass model and nonlinear bicycle model, respectively, in which xy-velocity of pedestrians and longitudinal speed and orientation of vehicles were estimated. The statistics of the velocity magnitude distribution demonstrated the validity of the proposed dataset. In total, there are approximate 340 pedestrian trajectories in CITR dataset and 1793 pedestrian trajectories in DUT dataset. The dataset is available at GitHub.

To solve the intelligent vehicles’ problems of ‘where am I?’ and ‘what is around me?’, a dense 3D sematic Simultaneous Localization and Mapping (SLAM) system is proposed to evaluate the pose of the intelligent vehicles and build the dense 3D semantic map. We address these challenges by combining a state of art Stereo-ORB-SLAM system and Convolutional Neural Networks. Firstly, we build a dense 3D point cloud map by using a four thread Stereo-ORB-SLAM system. Subsequently, a fully convolutional neural network architecture which uses RGB-D image as input is used to obtain pixel-wise segmentation. Finally, we fuse the geometric information and semantic information to get the semantic map. We test our method on the KITTI dataset and our dataset made with the Fpgalena stereo camera. Results indicate the system was effective in the real-time building of a semantic map, the speed of the entire system is about 10Hz, and the loop closing function can eliminate most of the drifting errors.

Visual-based semantic segmentation for traffic scene plays an important role in intelligent vehicles. In this paper, we present a new real-time deep fully convolution neural network (FCNN) for pixel-wise segmentation with six channel inputs. The six channel inputs include the RGB three channel color image, the Disparity (D) image generated by stereo vision sensor, the image to describe the Height (H) of each pixel above road ground, and the image to describe the Angle (A) between each pixel normal direction and the predicted direction of gravity, which are defined as a RGB-DHA multi-feature map. The FCNN is simplified and modified based on AlexNet to meet the real-time requirements of intelligent vehicle for environmental perception. The proposed algorithm is tested and compared in Cityscapes dataset, yields global accuracies 73.4% and 22ms for $400 \times 200$ resolution image with one Titan X GPU.

Pedestrian safety is of paramount importance for intelligent transportation systems. This study focuses on the scenarios where pedestrians appear as crowds and interact with moving vehicles in a relatively-free space. Based on social force model (SFM), a vehicle-crowd interaction (VCI) model is pro- posed to describe both the behavior of crowd pedestrians and vehicle. Specifically, a heuristic-based and effective modeling of vehicle influence on pedestrians is designed and incorporated into the crowd-only SFM. Qualitative analysis of systematic simulations of various VCI scenarios demonstrates the effective- ness of the proposed model. This model can effectively describe the VCI scenarios such as vehicle approaching from different directions, vehicle zigzagging, and vehicle sharply turning.

Dynamic eco-driving is a well-known umbrella term describing speed control schemes that utilize connected and automated vehicle technology for the purpose of saving fuel. If dynamic eco-driving is to be widely prescribed as an integral part of widespread fuel-saving endeavors, its expected performance as part of the overall traffic system must be analyzed. Specifically, it must be determined to what extent this type of control remains effective in the presence of dense traffic. This paper presents a series of multi-vehicle traffic simulations, which begin to answer important questions surrounding the effects of dynamic eco-driving on traffic and its potential for fuel savings in a mixed traffic environment. Three representative methods of dynamic eco-driving are tested in various high traffic scenarios and the estimated fuel economy, trip time, and average speed results are compared. Independent variables include technology penetration rate and amount of traffic, quantified by the delay level of service of the road network's traffic light facility. It is shown that, for the given test cases, average mpg increases linearly with technology penetration rate and dynamic eco-driving causes an average increase in mpg regardless of traffic amount. Overall results are promising for the usefulness of this clever class of fuel-saving technologies, in high traffic as well as low.

Vehicle speed prediction can benefit a wide range of vehicle control designs, especially for fuel economy applications. This paper shows a computationally light vehicle short term speed predictor designed for on-board implementation, using minimal information of speed measurement only. The predictor generalizes historical speed data's underlying pattern and predicts from probability aspect. One novelty of the method is the usage of fuzzy modeling to eliminate the resolution limitation in vehicle acceleration state definition, classification, and prediction. The method uses Auto-regressive (AR) model to capture vehicle speed data's short term dynamics, and classifies the data into multiple acceleration states by fuzzy membership. In the prediction process, acceleration measurements are mapped to the Markov states by fuzzy encoding, and future acceleration states are predicted by Markov transition. Deterministic speed prediction is calculated from the trained AR models, which are selected by fuzzy state membership similarity. The developed predictor is tested with a vehicle's real urban driving data, and the effectiveness of the incorporated techniques is verified by a comparison study.

The longitudinal control of an autonomous vehicle usually suffers from lateral interruptions, such as the cutting in/out of the lead vehicle, deteriorating its performance and even endangering driving safety. To address this problem, we present a model predictive-based approach for longitudinal control in autonomous driving by taking the lateral interruptions into account. First, a virtual lead vehicle scheme is introduced to predict the future behavior of the actual lead vehicle. By following the virtual lead vehicle rather than the actual lead vehicle, the control of the host vehicle is simplified to keep a proper following gap problem. Then, a strategic car-following gap (CFG) model, generated from highway naturalistic driving data, is employed to describe the safety hazard and the probability of cut-ins by other vehicles. A model predictive controller, incorporating the strategic CFG model as well as the acceleration and jerk limitations in the objective function, is designed for the longitudinal control of the host vehicle. Solving the optimal control problem can not only smooth the oscillation and overshoots caused by the lateral interruptions but also reduce the probability of cut-ins from the adjacent lanes. The proposed approach is simulated and validated through some predefined test scenarios in CarSim software.

Naturally occurring flocks and swarms have long commanded human attention, with much engineering inspiration drawn from the beauty, order, and capability of these highly decentralized systems. More recent simulation and modeling of swarms has given rise to interesting mathematical problems as well as useful control strategies for machine applications. Although highway systems are sometimes mentioned in the literature as a possible swarm theory application, a microscopic, decentralized model of vehicle interactions based on swarming philosophy does not exist to our knowledge. In this paper, a decentralized model made up of ordinary differential equations and smooth functions is developed. It is designed to describe the interactions of vehicles on a two-lane highway. The purpose of this new model is not primarily traffic simulation, but rather cooperative control design. The philosophy behind the modeling is borrowed from work on swarm theory, especially those simulations employing the motion control ideas known as Reynolds' Rules. Vehicles in the swarm have different desired speeds, which can be maintained by changing lanes to avoid slower-moving lead vehicles, while also avoiding both frontal and side collisions. Stability analysis of the proposed model has been presented, as well as simulation results and possible uses.

In this paper, we analyze mixed traffic environments consisting of fully autonomous vehicles, vehicles capable of communication only, and manually driven vehicles to determine what self-generated content should be shared among peer vehicles for increased traffic intelligence. For this purpose, we present information sharing utility-cost tables for a variety of communication strategies. These tables are used to determine communication requirements in terms of bandwidth, distance, packet delay and loss rate tolerance. We specifically evaluate vehicle lane change events due to their role as foundational building blocks in most other traffic scenarios. The presented work demonstrates requirements for the communication systems in mixed-traffic environments based on sharing and fusing necessary sensor information using occupancy grid mapping.

The previous studies have shown that optimizing the driving velocity profiles and route selection based on the availability of the traffic lights' operation information in a traffic network can significantly reduce the individual and cumulative energy consumption of on-road vehicles for the urban driving. In this paper, we propose an accurate and precise stochastic online estimation method of the parameters of the traffic lights operating at a piecewise constant period. In this paper, we first model the traffic lights with a semi-hidden Markov model (SHMM) and then develop the period measurement model governed by a unique noise model specific to the indirect traffic light period measurements. The proposed method solves the estimation problem in two stages: in the first stage, we determine the sequence of the Markovian states maximizing the probability given the measurements and the SHMM parameters; then, in the second stage, we update the period and state duration estimates based on the Bayesian tracking given the corresponding latest measurements. The simulation and real vehicle data results prove that the proposed method can accurately estimate the switching times and the period of the piecewise fixed-period traffic lights.

Energy consumption of a vehicle is a factor of several environmental and driving conditions, such as air flow density, road grade, and vehicle weight. Accurate estimation of these factors influences the control performance, diagnostics, and the vehicle's overall energy consumption. Individual vehicle dynamics, as part of a large convoy governing principles, will expand to include the states that are shared between vehicles. The controller performance relies on the estimated parameters to minimize energy consumption. The estimation of environmental and driving conditions for individual vehicles as part of a convoy is a challenging task. This paper introduces an adaptive model-based energy factor estimation in large-scale convoys. These factors are influenced by vehicle parameters and driving condition uncertainties. These uncertainties, if not estimated correctly, shift the predicted energy consumption and result in low control performance. Mathematical formulation of the proposed estimator in the context of large-scale system is studied through several case study scenarios, and their effectiveness is demonstrated.

This study illustrates a methodology to reduce the time and effort spent on full-scale Intelligent Transportation System testing, through the use of small-scale testbeds. Scaled down testing platforms enable the researchers to implement, compare, and assess different architectures for intelligent transportation by deploying hardware-in-the-loop (HIL) simulation and testing, giving strong indications on the performance and high-level behavior of such systems at full scale. The performance of the scaled down testing is illustrated using a specific example based on an autonomous parking. The approach is demonstrated on intelligent transportation system testbed in The Ohio State University Control and Intelligent Transportation Research Laboratory. The detailed experimental results show the applicability and robustness of the proposed system.

We present a framework for the estimation of driver behavior at intersections, with applications to autonomous driving and vehicle safety. The framework is based on modeling the driver behavior and vehicle dynamics as a hybrid-state system (HSS), with driver decisions being modeled as a discrete-state system and the vehicle dynamics modeled as a continuous-state system. The proposed estimation method uses observable parameters to track the instantaneous continuous state and estimates the most likely behavior of a driver given these observations. This paper describes a framework that encompasses the hybrid structure of vehicle-driver coupling and uses hidden Markov models (HMMs) to estimate driver behavior from filtered continuous observations. Such a method is suitable for scenarios that involve unknown decisions of other vehicles, such as lane changes or intersection access. Such a framework requires extensive data collection, and the authors describe the procedure used in collecting and analyzing vehicle driving data. For illustration, the proposed hybrid architecture and driver behavior estimation techniques are trained and tested near intersections with exemplary results provided. Comparison is made between the proposed framework, simple classifiers, and naturalistic driver estimation. Obtained results show promise for using the HSS-HMM framework.

Driving style, road geometry, and traffic conditions have a significant impact on vehicles' fuel economy. In general, drivers are not aware of the optimal velocity profile for a given route. Indeed, the global optimal velocity trajectory depends on many factors, and its calculation requires intensive computations. In this paper, we discuss the optimization of the speed trajectory to minimize fuel consumption and communicate it to the driver. With this information the driver can adjust his/her speed profile to reduce the overall fuel consumption. We propose to perform the computation-intensive calculations on a distinct computing platform called the “cloud.” In our approach, the driver sends the information of the intended travel destination to the cloud. In the cloud, the server generates a route, collects the associated traffic and geographical information, and solves the optimization problem by a spatial domain dynamic programming (DP) algorithm that utilizes accurate vehicle and fuel consumption models to determine the optimal speed trajectory along the route. Then, the server sends the speed trajectory to the vehicle where it is communicated to the driver. We tested the approach on a prototype vehicle equipped with a visual interface mounted on the dash of a test vehicle. The test results show 5%-15% improvement in fuel economy depending on the driver and route without a significant effect on the travel time. Although this paper implements the speed advisory system in a conventional vehicle, the solution is generic, and it is applicable to any kind of powertrain structure.

This paper introduces an energy consumption model for convoys of busses arriving to a bus stop with limited parking space. As the busses arrive randomly and stay for a fixed amount of time, the Monte Carlo analysis was used to determine the traffic flow and congestions. Simulation results with actualsize busses demonstrate that in a high-density traffic, convoy configurations would always yield higher energy efficiency and lower system energy dissipations. However, in low-density traffic, only for stop times of less than 5 minutes, convoys were more energy efficient compared to non-convoy bus arrivals. It was also observed that when stop times were increased beyond 5 minutes, individual arrival model yielded lower energy consumption. Simulation results and analysis are provided for various bus convoys and stop times.

The IEEE 802.11p, 1609.3, and 1609.4 WAVE standards are designed to facilitate intervehicle communication and ultimately improve traffic safety. Multiple safety applications and control algorithms have been proposed to use 802.11p Dedicated Short-Range Communication (DSRC) radios and message structures. An urban environment provides many challenges for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. These include multiple propagation paths and many occlusions, particularly in areas where V2V messages would be most useful such as blind spots, buildings, and other obstructions. The dense urban environments and high concentration of vehicles make it difficult to predict how reliable this communication will be. The Ohio State University's Vehicle and Traffic Simulator (VaTSim) is designed as a microsimulator of traffic. This paper describes the incorporation of V2V communication into VaTSim using Network Simulator 3 (NS3) and physical layer modeling to determine how different road layouts and building configurations will affect 802.11p communication. This paper explains the theory used to define the simulated line-of-sight (LOS) propagation, non-LOS (NLOS) propagation calculations, channel switching congestion, and the experiments performed to validate the models and the simulation.

This paper presents the cooperative adaptive cruise control implementation of Team Mekar at the Grand Cooperative Driving Challenge (GCDC). The Team Mekar vehicle used a dSpace microautobox for access to the vehicle controller area network bus and for control of the autonomous throttle intervention and the electric-motor-operated brake pedal. The vehicle was equipped with real-time kinematic Global Positioning System (RTK GPS) and an IEEE 802.11p modem installed in an onboard computer for vehicle-to-vehicle (V2V) communication. The Team Mekar vehicle did not have an original-equipment-manufacturer-supplied adaptive cruise control (ACC). ACC/Cooperative adaptive cruise control (CACC) based on V2V-communicated GPS position/velocity and preceding vehicle acceleration feedforward were implemented in the Team Mekar vehicle. This paper presents experimental and simulation results of the Team Mekar CACC implementation, along with a discussion of the problems encountered during the GCDC cooperative mobility runs.

One of the best way to increase road capacity is to enable vehicles to travel in a convoy (or platoon) with short distance headways from preceding vehicles. The objective of this study is to present a control system which can optimally control a convoy of closely following vehicles for stop-and-go type of traffic. The convoy needs to be string stable and must have robust control to dampen any disturbance caused in any part of the convoy. We develop a Finite State Machine which acts as a supervisory controller to guide a following vehicle to merge behind and follow the vehicle ahead. After merging, the vehicle following controller is a linear-quadratic regulator (LQR) based sequential-state feedback controller proposed over thirty years ago. The performance is evaluated with a scenario based on a recent “Grand Cooperating Driving Challenge”.

This paper presents an algorithm for in-surveillance and out-of-surveillance mobile ground target tracking. In this respect, a nonlinear Bayesian estimation filter is presented. Then, an adaptive algorithm is derived to reduce remarkably the computational burden of this filter, while not degrading the accuracy of the state estimates. Additionally, an algorithm employing the concepts of hospitability and synthetic inclination maps is introduced and is coupled with the nonlinear Bayesian filter to track the mobile ground target once it goes out-of-surveillance. Loosely speaking, the hospitability map can be viewed as a terrain-based map defining a likelihood or a “weight” for each point on the earth's surface proportional to the ability of the target to move and maneuver at that location. On the other hand, the synthetic inclination map describes how the target favors certain regions within the search area, hence being “synthetically” inclined to move toward them.

This paper presents a new approach to data fusion for automatic recognition, surveillance, and tracking in intelligent transportation systems. Robust data alignment (RDA), i.e., finding relational maps among a sequence of invariant feature data sets, is one of the key requirements for successful data fusion. To achieve RDA for correspondenceless data fusion, we construct a cost criterion based on the information theory and solve an optimization problem with a mixed search strategy that combines the Nelder-Mead simplex and random search methods. We evaluate the cost criterion and search strategy by a numerical stability test and suggest an outlier rejection technique for refining the previous feature data and, at the same time, extracting moving vehicles that are contained in the collected outliers. Experimental results on a video sequence that is collected from an unmanned aerial vehicle indicate the potential of aerial monitoring and tracking systems built on our information-theoretic RDA.

In this paper, we review technologies for autonomous ground vehicles and their present capabilities in research and in the automotive market. We outline technology requirements for enhanced functions and for infrastructure development. Since the recent Grand Challenge competition is a major force to advance technology in this field, we specifically refer to our experiences in developing a participating vehicle. We present a multisensor platform that has been proven in an off-road environment. It combines different sensing modalities that inherently yield uncertain information. Finite-state machines are formulated to generate rule-based autonomous behavior that enables fully autonomous off-road driving. Overall, the intent of the paper is to evaluate approaches and technologies used in the two Grand Challenges as they contribute to the needs of autonomous cars on the road

In the surveillance of multiple targets by mobile sensor agents (MSAs), system performance relies greatly on the motion-control strategy of the MSAs. This paper investigates the motion-planning problem for a limited resource of M MSAs in an environment of N targets (M

The primary focus of study in this paper is the background control theory for automated lane change maneuvers. We provide an analytic approach for the systematic development of controllers that will cause an autonomous vehicle to accomplish a smooth lane change suitable for use in an Automated Highway System. The design is motivated by the discontinuous availability of valid preview data from the sensing systems during lane-to-lane transitions. The task is accomplished by the generation of a virtual yaw reference and the utilization of a robust switching controller to generate steering commands that cause the vehicle to track that reference. In this way, the open loop lane change problem is converted into an equivalent virtual reference trajectory tracking problem. The approach considers optimality in elapsed time at an operating longitudinal velocity. Although the analysis is performed assuming that the road is straight, the generalization of the proposed algorithm to arbitrary road segments is rather straightforward. The outlined lane change algorithm has been implemented and tested on The Ohio State University test vehicles. Some of the experimental results are presented at the conclusion of this paper.

This paper discusses decentralized parallel distributed compensator design for Takagi-Sugeno fuzzy systems. The fuzzy system is viewed as an interconnection of subsystems some of which are strongly connected, while others being weakly connected. The necessary theory is developed so that one can associate this fuzzy system with another one in a higher dimensional space, the so-called expanded space, design decentralized parallel distributed compensators in the expanded space, then contract the solution for implementation on the original fuzzy system. In this respect, connective stability of the open loop and closed loop of the interconnected system is analyzed via the concepts of vector Lyapunov functions and M-matrices. Different Lyapunov functions generate different results for the discrete-time fuzzy system, quadratic Lyapunov generating the superior of the two. Following a similar approach, stabilization of the closed-loop fuzzy system using local parallel distributed compensators is investigated.

A hierarchical structure that utilizes all the functionalities of a large-scale system and unifies the dynamics of the system with its functional behavior is introduced. The proposed hierarchy is formed by decomposing the physical structure of a system and by associating knowledge-rich controllers with the structure. The inclusion of structural information in the hierarchy has several advantages. First, it presents multifunctional descriptions of portions of the system. Then, it provides a modular decomposition such that complete reconstruction of the hierarchy is not required if some parts of the system change. Most importantly, it enables local failure handling and replanning. To demonstrate the physical decomposition, task assignment, and control process, a system with two robot arms and a camera was considered as an example.>

The resolution of the dynamics of flexible manipulators into rigid and flexible modes is considered. The decoupling is established on the single-link case by singular perturbation techniques. The flexural effects of the manipulator on its rigid-body motion are included by using the higher-order terms in the asymptotic expansion. The model used is the integro-partial-differential equation resulting from the extended Hamiltonian principle. Use of this model, rather than a finite-dimensional approximation, yields more insight and a more compact way of obtaining the higher-order terms that represent the coupling between the rigid and the flexure modes. Asymptotic perturbation techniques are utilized to generate a composite control law.>

Asymptotic regulation to a constant set-point by state feedback, and PI (proportional integral) control is considered for n-link multibody systems. Global asymptotic stability of these regulators is shown using Lyapunov's direct methods. The main contribution is the exclusion of explicit gravity cancellation. The implication of the above is the ensuing robustness of the controllers to parameter and payload variations. Furthermore, it is shown that the controllers can be implemented in a decentralized manner at each joint despite the nonlinear interconnections among the joints.>

The authors report laboratory results for a single-link flexible manipulator arm in which three separate control strategies are compared and contrasted: compensation using classical root locus techniques with endpoint position feedback, a full state feedback observer-based design, and compensation using endpoint acceleration feedback. The last technique, using accelerometer feedback, has received little attention to date. The presented results indicate great promise for its use in flexible manipulator control.>

The control problem for a two-arm robotic system in co-ordinated motion is addressed. A hierarchical framework, employing two levels of control hierarchy, is utilized, the decentralized model reference adaptive control approach using variable structure controllers (DMRA-VSC) is applied. Within the control hierarchy, the DMRA-VSC strategy is accomplished at the lower level, where control is responsible for the servoing of each joint. These local controllers are coordinated by the high-level, central controller, whose task is to provide the local controllers with the upper bound on the dynamical interactions with other subsystems. Advantages of the DMRA-VSC approach for multiple manipulator control include the inherent robustness properties to nonlinearities and interaction effects, the decentralization structure facilitating ease in multiple manipulator system programming and implementation, and the general structure of the controller which allows further extensions.

A simple control law is proposed to maintain the movement of a trotting quadruped. The assumption behind this control law is that one should be able to rely upon the quadruped's momentum for stability. The control law should only serve to redirect or maintain the quadruped's momentum. The result is a control law that works well so long as the quadruped is moving.

A decentralized variable structure control algorithm is developed for robotic manipulators to cause sliding modes to exist. New approaches are used to reach the sliding mode and to reduce non-ideal characteristics while in the sliding mode. Both position control and trajectory following are examined through two-link and three-link digital simulations.

An arbitrary interconnected network of intersecting streets with one-way traffic flow is considered in which some or all intersections are assumed to be operating at maximum capacity. In this case, it is desired to find a robust decentralized controller at each traffic intersection, so that for all perturbed external traffic flow sources/sinks, the resulting perturbed queues at each intersection are asymptotically “balanced”. The only local knowledge required of the controllers at each intersection is that re the queue lengths of the intersection. It is shown that the necessary and sufficient conditions for a solution to the problem to exist depend solely on the topology of the network and the percentage of vehicles turning at each intersection, and are identical to the conditions required for a solution to exist for the centralized controller case. In particular, it is shown that if turns always occur at an intersection, then a solution to the problem always exists. A characterization of controllers which solve the problem are then given. In particular, it is shown that the local controllers which solve the problem are identical to each other, which implies that if the network is expanded (assuming a solution exists) then one can use the same local controllers to solve the problem. It is also shown that the local controllers can, in principle, give “perfect control” to the problem. Some examples are included to illustrate the results.