Testing cloud-controlled intelligent connected vehicles (ICVs) requires simulation environments that faithfully emulate both vehicle behavior and realistic communication latencies. This paper proposes a latency-aware co-simulation platform integrating CarMaker and Vissim to evaluate safety and comfort under real-world vehicle-to-cloud (V2C) latency conditions. Three communication latency models, derived from empirical 5G measurements in China and Hungary, are incorporated and statistically modeled using Gamma distributions. A proactive conflict module (PCM) is proposed to dynamically control background vehicles and generate safety-critical scenarios. The platform is validated through experiments involving an exemplary system under test (SUT) across eight testing conditions combining two PCM modes (enabled/disabled) and four latency conditions (none, China, Hungary, abnormal). Safety and comfort are assessed using metrics including collision rate, distance headway, post-encroachment time, and the spectral characteristics of longitudinal acceleration. Results show that the PCM effectively increases driving environment criticality, while V2C latency reduces ride comfort and, under extreme driving conditions, further aggravates safety-critical scenarios. These findings confirm the platform’s effectiveness in systematically evaluating cloud-controlled ICVs under diverse testing conditions.

In real-world graphs, node attributes are often incomplete due to acquisition costs or privacy restrictions, reducing representation quality and harming downstream predictions in graph neural networks (GNNs). A common remedy is feature-propagation-based imputation. However, cold-start effects arising from attribute resetting and low-degree nodes impede effective propagation and convergence in these methods. To address these challenges, we propose AttriReBoost (ARB), a propagation-based method that mitigates cold-start issues in attribute-missing graphs. ARB enhances global feature propagation (FP) by redefining initial boundary conditions and strategically integrating virtual edges, thereby improving node connectivity and ensuring stable and efficient convergence. The method supports gradient-free attribute reconstruction with low computational overhead, and we provide a rigorous convergence analysis. Extensive experiments on several real-world benchmark datasets demonstrate the effectiveness of ARB, achieving an average accuracy improvement of 5.11% over state-of-the-art methods. In addition, ARB exhibits remarkable computational efficiency, processing a large-scale graph with 2.44 million nodes in just 16 s on a single GPU. Our code is available at https://github.com/limengran98/ARB.

Deep Reinforcement Learning (DRL) is becoming a prominent method for autonomous driving due to its strong capability to generate complex driving policy. However, DRL motion planning still has limitations in safety performance including learning quality, convergence speed and the safety guarantee. To this end, this work proposes a safety-enhanced deep reinforcement learning method with dynamic safety guidance (DSG-DRL) for lane-change motion planning. It bears the following key features: 1) Able to learn a safer DRL driving policy by additionally including potentially unsafe behaviors; 2) Able to accelerate learning a safe policy by making dangerous driving experiences impressive; 3) Able to further enhance the driving safety by avoiding unexpected reckless action. The proposed DSG-DRL motion planner dares to make mistakes to learn the safe driving policy better and faster. By evaluating anticipated risk, it learns not only from the maneuvers right at the moments of collisions, but also from the dangerous maneuvers leading towards collisions. Besides, risk driving experiences are enhanced with additional memory batches and sampling prioritization. Moreover, reckless actions can be prevented by dynamic constraints both in training and testing, which further improves the safety performance. Simulation validation shows that the proposed method can learn a safer driving policy with faster convergence speed, achieving the high safety performance while keeping the driving efficiency.

Risk quantification is a critical component of safe autonomous driving, however, constrained by the limited perception range and occlusion of single-vehicle systems in complex and dense scenarios. Vehicle-to-everything (V2X) paradigm has been a promising solution to sharing complementary perception information, nevertheless, how to ensure the risk interpretability while understanding multi-agent interaction with V2X remains an open question. In this paper, we introduce the first V2X-enabled risk quantification pipeline, CooperRisk, to fuse perception information from multiple agents and quantify the scenario driving risk in future multiple timestamps. The risk is represented as a scenario risk map to ensure interpretability based on risk severity and exposure, and the multi-agent interaction is captured by the learning-based cooperative prediction model. We carefully design a risk-oriented transformer-based prediction model with multi-modality and multi-agent considerations. It aims to ensure scene-consistent future behaviors of multiple agents and avoid conflicting predictions that could lead to overly conservative risk quantification and cause the ego vehicle to become overly hesitant to drive. Then, the temporal risk maps could serve to guide a model predictive control planner. We evaluate the CooperRisk pipeline in a real-world V2X dataset V2XPnP, and the experiments demonstrate its superior performance in risk quantification, showing a 44.35% decrease in conflict rate between the ego vehicle and background traffic participants.

Autonomous driving (AD) continues to grapple with the complexity of dynamic and interactive traffic environments, where the primary difficulty stems from insufficient modeling of inter-vehicle interactions—particularly, how autonomous agents should perceive and respond to surrounding vehicles’ influence. To address this, this paper proposed ExpliDrive, an explainable data-driven approach for interaction-aware autonomous driving. Its highlights lie in bridging Model Predictive Control (MPC) and Transformers. The proposed approach builds a generalized system dynamics in which interaction effects between vehicles are explicitly modeled. Specifically, a Transformer encoder-decoder is employed to encode the interaction patterns among vehicles, and these learned effects are seamlessly embedded into the motion planning process. Hence, the proposed approach bears following features: i) enabling proactively interaction-aware autonomous driving; ii) data-driven yet explainable; iii) integrating the prediction into motion planning. Open-looped evaluation demonstrates the proposed approach achieves the lowest prediction errors, from ADE@1s (0.16m) to ADE@5s (0.80m). Close-looped planning shows the proposed approach has significant benefits in driving success rate and flexibility.

Reinforcement Learning (RL) offers a promising solution to enable evolutionary automated driving. However, conventional RL methods often struggle with risk performance, as updated policies may fail to enhance performance or even lead to deterioration. To address this challenge, this research introduces a High Confidence Policy Improvement Reinforcement Learning-based (HCPI-RL) planner, designed to achieve the monotonic evolution of automated driving. The HCPI-RL planner features a novel RL policy update paradigm, ensuring that each newly learned policy outperforms previous policies, achieving monotonic performance enhancement. Hence, the proposed HCPI-RL planner has the following features: i) Evolutionary automated driving with guaranteed monotonic performance enhancement; ii) Capability of handling scenarios with emergency; iii) Enhanced decision-making optimality. Experimental results demonstrate that the proposed HCPI-RL planner enhances policy return by at least 20.1% and driving efficiency by at least 15.6%, compared to the conventional RL-based planners.

Current Autopilot systems struggle in highly dynamic traffic scenarios due to their lack of human-like competitive capability. This research proposes a human-like Autopilot planner with right-of-way priority acquisition capability. It bears the following features: i) human-like Autopilot with proactive right-of-way acquisition capability; ii) with enhanced maneuvering optimality; iii) aggressive but not unsafe; iv) with real-time implementation capability. The proposed approach can identify surrounding vehicles' driving styles in real time and plan motions based on Stackelberg competition. Simulation results show that the proposed approach acquires right-of-way priority proactively. The proposed approach enhances driving efficiency by up to 3.57% across different congestion levels and improves driving safety by up to 13.52% compared to the conventional Autopilot planner. The proposed approach shows remarkable flexibility in dense traffic. Additionally, the proposed approach enables real-time implementation by maintaining less than 100 milliseconds of computation time.

In this research, a lane-level navigation system is designed to enhance vehicle mobility at signalized intersections. A deep learning-based lane-level long-term speed prediction (LLSP) predictor was developed to forecast traffic conditions for the upcoming planning horizon. Additionally, a lane-level navigation with speed guidance (LNSG) planner was introduced to determine the optimal lane-level route and the recommended travel speed for the ego vehicle. The performance of the proposed system was assessed using a software-in-the-loop simulation platform, considering various scenarios such as different traffic demands, vehicle arrival times at the control area, and planning resolutions. The evaluation results demonstrate that the proposed navigation system effectively improves the mobility of the ego vehicle by providing optimal lane and speed recommendations. Compared to the lane-keeping strategy, the system can reduce travel time by up to 30.2% in various traffic conditions.

Road Side Units (RSUs) play a pivotal role in enhancing the safety of Connected Vehicles (CVs), yet their safety benefits hinge significantly on effective deployment strategies. Traditional approaches often focus on high-risk areas, but such locations may not necessarily yield the most substantial safety improvements. This study redefines hot spots as road segments where RSU deployment results in the greatest reduction of collision risk and introduces a cost-efficient method for identifying such locations. The proposed method requires only small-scale real-world driving data, which is further augmented to support broader scenario evaluation. Additionally, an accelerated sampling strategy is incorporated to enhance the efficiency of the identification process. Simulation-based evaluations demonstrate that the method achieves superior performance in terms of safety impact, data efficiency, compared to conventional approaches.

Perception technology plays a crucial role in vehicle automation, yet traditional approaches solely rely on onboard computing and have inherent limitations in perception range. To improve perception range, edge computing is introduced. Through edge computing, some perception computing tasks can be offloaded from onboard sensors to sensors installed on roadside infrastructures. This infrastructure-enabled perception (IEP) expands perception range beyond what onboard sensors alone can achieve. However, existing IEP systems have limited precision in long-distance perception and require costly sensors for optimal performance. To enhance the performance without significant financial investment, this article proposes an enhanced IEP system. The proposed IEP system adopts a virtual-detector-based perception solution, designing multiple virtual detectors to detect vehicle arrivals. Unlike traditional IEP approaches, the proposed system does not rely on dense data points for estimating vehicle geometry. Instead, it bypasses the geometry-estimation step and only needs sparse data points to detect vehicle arrivals. Consequently, even with sparse data points at long perception distances, our IEP system can achieve high precision in long-distance perception. Due to this enhanced solution, the proposed IEP system has the following features: 1) ensuring wide perception range; 2) enabling cm-level precision perception; 3) maintaining robustness against perception distances, vehicle speeds, and sensor frequencies; 4) compatible with mass-produced and cost-effective sensors; and 5) laying a foundation for infrastructure-enabled cooperative driving. Experimental validation confirms the system’s advanced features and demonstrates its superiority over the state-of-the-art IEP system. It achieves a wide perception range up to 150 m and a low localization error down to 6.500 cm. Further investigation suggests that the IEP system should primarily be deployed on expressways and implemented for speed-related cooperative driving applications, such as speed harmonization.

With the emerging Internet of Things (IoT) and Vehicle-Road-Cloud Integration System (VRCIS) technologies, coordinating Connected and Automated Vehicles (CAVs) and traffic signal is becoming a practical solution to further enhance traffic efficiency. However, current studies still have limitations. Firstly, there is a domain mismatch between CAV trajectory planning (temporal domain) and signal optimization (spatial domain). This mismatch requires separate modeling of trajectory planning and signal optimization, which greatly reduces global optimality. Secondly, previous studies are not applicable to actual mixed traffic environment, since they mostly simplify Human-driven Vehicle’s (HV) behavior without considering queuing and stop-and-go maneuvers. Therefore, we propose a novel Multi-Vehicles and Signal Cooperation (MVSC) planner to solve the limitations via following designs. (i) Joint optimization is achieved via formulating in the spatial domain, unifying CAV’s planning domain with traffic signal optimizing domain. (ii) A parallel algorithm is designed for the adaptation to numbers of CAVs. This algorithm is based on Alternating Direction Method of Multipliers (ADMM), making full use of IoT and VRCIS. (iii) HV queuing and stop-and-go behaviors are considered in our modeling. Simulation results show that the proposed MVSC planner can enhance efficiency and ecology by 23.60% and 15.63%. At CAV’s penetration rate of 40% and V/C ratio of 0.75, the proposed planner shows its full potential in performance enhancement. The average computation time of parallel computing approach is only within 10 milliseconds, which confirms the real-time implementation capability.

Demand Responsive Transit (DRT) emerges as one of the most promising public transit operating patterns, which operates without fixed stations or routes, aiming to provide flexible and passenger-oriented services. However, the current DRT dispatching hardly achieves a balance between the operating costs and service flexibility, leading to a high failure rate of DRT operation. To address this issue, this research proposes a dispatching method for DRT with passengers’ hidden preference exploitation capability. The proposed DRT dispatching method overcomes the imbalance shortcomings of conventional method and bears the following features: 1) With the capability of exploiting the passengers’ hidden preference; 2) With the capability of making the most of the passengers’ room for compromise. To evaluate the proposed dispatching method, a numerical experiment compared with conventional DRT dispatching model is conducted, and sensitivity analysis is performed for passenger satisfaction level threshold. The evaluation results show that with the capability of exploiting the passengers’ hidden preference, the proposed DRT dispatching method is able to improve the average travel time by 27.8%~47.5% and reduce the average waiting time by 26.4%~47.4%; via making the most of the passengers’ room for compromise, the proposed DRT dispatching method is able to enhance the passenger average satisfaction by 65.3%~85.7%. The benefit range is caused by different values of passenger satisfaction level threshold.

Personalization is crucial for the widespread adoption of advanced driver assistance systems. To match up with each user’s preference, the online evolution capability is a must. However, conventional evolution methods learn from naturalistic driving data, which requires a lot of computing power and cannot be applied online. To address this challenge, this paper proposes a lesson learning approach: learning from the user’s takeover interventions. By learning a lesson from real-time takeover behavior, a driving zone is generated to ensure perceived safety. Within the driving zone, a personalized trajectory is planned based on model predictive control, with an objective learned from the user’s takeover. The proposed lesson learning framework is highlighted for its faster evolution capability, adeptness at experience accumulation, assurance of perceived safety, and computational efficiency. Simulation results demonstrate that the proposed system consistently achieves successful customization without further takeover interventions. Accumulated experience yields a 24% enhancement in evolution efficiency. The average number of learning iterations is only 13.8. The average computation time is 0.08 seconds.

Automated Vehicle (AV) safety is a critical issue and appeals to worldwide focus. To ensure AV safety, AV functions should be tested and evaluated in an enormous number of scenarios. Since such AV testing is time-consuming, scenario filters have been developed to identify safety-critical scenarios and omit ordinary ones. However, the scenarios identified by these filters do not uniquely match the AV function to be tested and are most likely not critical for the AV function. Therefore, an enhanced scenario filter is proposed in this paper. It bears the following features: 1) Automated-driving-function-specific scenario identification; 2) High coverage of critical scenarios; 3) Enhanced identification efficiency by avoiding adopting a surrogate model; 4) High reliability of critical scenario identification. To enable the above features, the proposed filter formulates the identification problem into an optimization problem and solves it with a model-free approach. Experiments have been conducted to evaluate and validate the proposed filter. The results confirm that the proposed filter is able to improve coverage of critical scenarios, efficiency of identification, and reliability of identification compared to the state-of-the-art filter. Specifically, the proposed filter improves coverage by up to 70 percent, efficiency by up to 97 percent, and reliability by up to 22 percent. The results also reveal that the proposed filter shows an increasing advantage for testing AV functions with higher complexity.

Active Traffic Management (ATM) plays a crucial role in alleviating congestion. However, traditional ATM strategies struggle to precisely control traffic demand to match bottleneck capacity. This limitation not only worsens congestion but also negatively impacts traffic, resulting in increased vehicle cruising discomfort and traffic flow fluctuations. To address this issue, this paper proposes a multi-centralized Trajectory-based Traffic Management (TTM) strategy. The goal is to enable precise demand control for multi-segment scenarios with the help of Connected and Automated Vehicles (CAVs). It is able to regulate traffic demand by precisely matching bottleneck capacity. The proposed TTM strategy bears three novel features: 1) stronger demand regulation capability in terms of control range and precision; 2) reduced negative impacts to achieve improved vehicle cruising comfort and traffic flow stability; and 3) enhanced computation efficiency to avoid the curse of dimensionality. The proposed TTM is evaluated against the conventional ATM strategy, named variable speed limit. The results confirm the aforementioned features of the proposed method and demonstrate its superiority over the conventional approach. Additional discussion highlights that the proposed TTM enhances lane-change smoothness by 78.6%. It is also revealed that the multi-centralized structure of the proposed TTM is critical in achieving a balance between global optimality and computational efficiency.

Cooperative Adaptive Cruise Control (CACC) often requires human takeover for tasks such as exiting a freeway. Direct human takeover can pose significant risks, especially given the close-following strategy employed by CACC, which might cause drivers to feel unsafe and execute hard braking, potentially leading to collisions. This research aims to develop a CACC takeover controller that ensures a smooth transition from automated to human control. The proposed CACC takeover maneuver employs an indirect human-machine shared control approach, modeled as a Stackelberg competition where the machine acts as the leader and the human as the follower. The machine guides the human to respond in a manner that aligns with the machine’s expectations, aiding in maintaining following stability. Additionally, the human reaction function is integrated into the machine’s predictive control system, moving beyond a simple “prediction-planning” pipeline to enhance planning optimality. The controller has been verified to 1) enable a smooth takeover maneuver of CACC; 2) ensure string stability in the condition that the platoon has less than 6 CAVs and human control authority is less than 40%; 3) enhance both perceived and actual safety through machine interventions; and 4) reduce the impact on upstream traffic by up to 60%.

Accurately and safely predicting the trajectories of surrounding vehicles is essential for fully realizing autonomous driving (AD). This paper presents the Human-Like Trajectory Prediction model (HLTP++), which emulates human cognitive processes to improve trajectory prediction in AD. HLTP++ incorporates a novel teacher-student knowledge distillation framework. The “teacher” model, equipped with an adaptive visual sector, mimics the dynamic allocation of attention human drivers exhibit based on factors like spatial orientation, proximity, and driving speed. On the other hand, the “student” model focuses on real-time interaction and human decision-making, drawing parallels to the human memory storage mechanism. Furthermore, we improve the model’s efficiency by introducing a new Fourier Adaptive Spike Neural Network (FA-SNN), allowing for faster and more precise predictions with fewer parameters. Evaluated using the NGSIM, HighD, and MoCAD benchmarks, HLTP++ demonstrates superior performance compared to existing models, which reduces the predicted trajectory error with over 11% on the NGSIM dataset and 25% on the HighD datasets. Moreover, HLTP++ demonstrates strong adaptability in challenging environments with incomplete input data. This marks a significant stride in the journey towards fully AD systems.

A motion planner is established to realize piloting automated driving on unstructured roads. It has the following features: i) improved adaptivity to over-the-horizon driving environment, ii) enhanced compatibility with unstructured roads, and iii) guaranteed computational efficiency for real time application. The performance of the proposed motion planner was evaluated in a software-in-the-loop simulation platform. section. The evaluation includes: i) unstructured roads compatibility validation, and ii) validation of adaptivity to traffic events. Experiment results showed that applying the planner can enhance adaptivity to over-the-horizon traffic events on unstructured roads. The average travel efficiency enhancement is about 12.18% and the average perceived risk reduction is about 57.19%.

This paper proposed a right-of-way allocation method for Automated Intelligent Vehicles (AIVs) in automated container terminals. The results demonstrate a notable improvement in travel efficiency, up to 60%, along with responsiveness measured in seconds.

The current adaptive cruise control (ACC) systems are susceptible to disruptive behaviors such as mandatory cut-ins, commonly known as "road bullying". To address this issue, this paper introduces an anti-bullying adaptive cruise control (AACC) approach endowed with proactive right-of-way protection capabilities. It bears the following features: i) with the capability of preventing bullying from mandatory cut-ins; ii) with real-time field implementation capability. The proposed approach utilizes inverse optimal control (IOC) technology to discern the driving styles of other road users online, subsequently employing Stackelberg competition for motion planning. Extensive simulation results demonstrate the efficacy of the proposed approach in thwarting bullying from mandatory cut-ins. Additionally, the approach exhibits support for real-time field deployment by maintaining computation times of less than 50 milliseconds.

Truck platooning is a promising technology, especially reducing fuel consumption. However, conventional truck platooning methods are short of field tests and lack the capability of platoon lane-change. It impedes the large-scale implementation, since a truck platoon may usually be blocked by a slow-moving vehicle. To address this challenge, we propose a truck platooning controller with lane change capability. This controller is highlighted for the following features: i) enhancing mobility by cutting through traffic one-by-one like a snake; ii) enhancing string stability by formulating in the spatial domain; iii) enhancing planning accuracy by utilizing a tractor-semitrailer-based truck dynamics model; iv) ready for large-scale implementation since it passes our field tests. Results have demonstrated that the proposed controller enhances the mobility of truck platooning by 7.44%.

The advent of Large Language Models (LLM) provides new insights to validate Automated Driving Systems (ADS). In the herein-introduced work, a novel approach to extracting scenarios from naturalistic driving datasets is presented. A framework called Chat2Scenario is proposed leveraging the advanced Natural Language Processing (NLP) capabilities of LLM to understand and identify different driving scenarios. By inputting descriptive texts of driving conditions and specifying the criticality metric thresholds, the framework efficiently searches for desired scenarios and converts them into ASAM OpenSCENARIO1and IPG CarMaker text files2. This methodology streamlines the scenario extraction process and enhances efficiency. Simulations are executed to validate the efficiency of the approach. The framework is presented based on a user-friendly web app and is accessible via the following link: https://github.com/ftgTUGraz/Chat2Scenario.

Automated Parking Assist (APA) systems are now facing great challenges with low adoption in applications, due to users’ concerns about parking capability, reliability, and completion efficiency. To upgrade the conventional APA planners and enhance user’s acceptance, this research proposes an optimal-control-based parking motion planner. Its highlight lies in its control logic: planning trajectories by mirroring the parking target. This method enables: i) parking capability in narrow spaces; ii) better parking reliability by expanding Operation Design Domain (ODD); iii) faster completion of parking process; iv) enhanced computational efficiency; v) universal to all types of parking. A comprehensive evaluation is conducted. Results demonstrate the proposed planner does enhance parking success rate by 40.6%, improve parking completion efficiency by 18.0%, and expand ODD by 86.1%. It shows its superiority in difficult parking cases, such as the parallel parking scenario and narrow spaces. Moreover, the average computation time of the proposed planner is 74 milliseconds. Results indicate that the proposed planner is ready for real-time commercial applications.

Truck platooning is a promising technology in freight transport. To commercialize truck platooning as early as possible, its evaluation is in urgent need. For truck platooning evaluation, simulation platforms play a crucial role. However, there has not been a simulation platform to meet the evaluation needs of various stakeholders, including Original Equipment Manufacturers (OEMs), Freight Operators (FOs) and Transportation Management Administrations (TMAs). To fill the research gap, this paper proposes a next-generation simulation platform. It integrates a traffic simulator, platoon management system, and truck control module to satisfy all the evaluation needs. The proposed platform bears the following features: i) Compatibility with various platooning decision makers, planners, controllers, vehicle types and platoon management strategies; ii) Capability of evaluating platoon performance on the lateral dimension; iii) Prototype platoon management system provided for FOs; iv) Capability of evaluating truck platoon management performance in terms of sustainability and economy; v) Capability of evaluating the impact of interactive background traffic on platoon performance. vi) Capability of evaluating the impact of truck platoon management on traffic mobility. The proposed platform is validated by comparison against an actual field test. Its credibility is confirmed in terms of truck platoon performance and interactive traffic simulation. Additional tests are conducted to evaluate truck platoon performance and the impact of truck platoons on mixed traffic. The results reveal that existing platoon lane-change technologies should be upgraded to be compatible with high-traffic-demand scenarios. It is also revealed that a localized and up-to-date assessment is required before allowing truck platooning.

Autonomous driving requires efficient and safe decision making and motion planning in dynamic and uncertain environments. Future movement of surrounding vehicles is often difficult to represent. Besides, most existing studies consider decision making and planning/control separately. Both them may lead to the oscillation and unsafe for autonomous driving. This paper proposes an integrated framework of decision making and motion planning with oscillation-free capability. The proposed approach overcomes the shortcomings of autonomous driving for lane change/keeping maneuvers and is able to: i) make oscillation-free behavior decisions given biased prediction; ii) cut through in the traffic efficiently and safely when being in squeezed; iii) accelerate computation efficiency by building a state transfer model based on prediction uncertainty; iv) reduce the dissonance between decision-making and motion planning. A belief decision planner is designed with the uncertainty of the prediction trajectories. Lateral and longitudinal drivable corridors including the reference state and the related boundary constraints are built, which provide better suited information for planning to solve the optimal motion sequence more quickly and stably, and improve its consistency with decision module. Finally, the problem is formulated as an optimal control problem considering the vehicle dynamics and some soft constraints and the motion trajectory is solved by OSQP. Simulation and experimental tests are implemented to evaluate the feasibility and effectiveness of the proposed approach. Test results show that the integrated approach can make proper, safe and continuous decision and planning for autonomous vehicles and the calculation time is very low.

With the booming adoption of Electric Vehicles (EVs) globally, the need for reliable and resilient EV Charging Monitoring (EVCM) systems has become crucial. A major challenge in real-time EVCM is the handling of missing data caused by unexpected events, which can impair both real-time monitoring and its downstream applications. To address this vital yet underexplored issue, we propose a curriculum guided multi-feature fusion transformer (CurriFusFormer) learning framework – a novel approach designed to enhance the resilience of EVCM systems against real-time information omissions. Our framework integrates curriculum learning with a multi-feature fusion transformer model, capable of handling various patterns and rates of missing data, ranging from random to block omissions. This innovative approach leverages spatial, temporal, and static features to generate accurate real-time estimations for missing values in diverse scenarios. Extensive experiments on a real-world EVCM dataset demonstrate that CurriFusFormer can perform well with$R^{2}$ranging from 0.92 to 0.83 given the rising missing rate from 30-90%, outperforming seven popular and state-of-the-art methods, especially in scenarios with high missing rates and complex patterns, such as, at 90% missing rate, kNN ($R^{2} =0.65$), XGBoost ($R^{2} =0.78$), BRITS ($R^{2} =0.79$), TFT ($R^{2} =0.80$), and GRIN ($R^{2} =0.82$). All results suggest that the proposed framework could be a promising solution for developing future resilient EVCM networks.

Human drivers are requested by the Automated Vehicle (AV) to perform takeover actions if needed. Existing research mainly focuses on predicting the takeover quality due to distraction using wearable sensor data. It is unrealistic, unnatural, and inapplicable to require human drivers to wear these sensors when driving an AV so that their situational awareness for takeover actions can be continuously monitored. Moreover, traffic conflicts can be observed even if drivers take over as requested. Current practice mainly develops conflict-actuated collision avoidance systems that alert drivers once the traffic conflict reaches a certain threshold. There is a research need to anticipate conflicts other than by measuring them. Besides, drivers are still responsible for responding to the alerts, which leaves the possibility of resulting in human error-related safety issues. This research aims at developing a Non-intrusive, Ultra-advanced Collision Avoidance System (NIUCAS) under automated driving. NIUCAS applies the brake pedal for drivers if it predicts the absence of takeover actions due to distraction or predicts traffic conflicts before they can be measured. The NIUCAS prototype was implemented in a driving simulator. An experiment was conducted by recruiting sixty participants to drive a vehicle under Level 3 automation, going through jaywalking scenarios, and being requested to take over. Participants’ demographics were collected to predict the takeover actions, while vehicle-related performance was collected to predict the traffic conflicts. Three machine learning-based modeling techniques were chosen as candidates for predictions. Additionally, an empirical equation is formulated to quantify the safety benefits of implementing NIUCAS.

This paper proposed a speed harmonization controller for partially connected and automated traffic. It regulates the flow rate of the entire traffic by adjusting only the target cruising speed of Connected and Automated Vehicles (CAVs). The proposed controller bears the following features: i) compatibility enabled with partially connected and automated traffic consisting of CAVs and Human-driven Vehicles (HVs); ii) stability ensured for the traffic system under control; iii) precision guaranteed for the demand management on a multi-lane road with the help of a small portion of vehicles. To evaluate the proposed controller, a microscopic simulation evaluation was conducted. Results confirm that the control accuracy of the proposed controller is generally over 80% across all CAV Penetration Rates, demand levels (v/c ratio) and target demand drops (within 20%). A case study is presented to demonstrate the benefit of applying the proposed controller on a bottleneck. By preventing the onset of a breakdown and, along with it, a capacity drop, the proposed controller is able to increase the flow rate by 6%, reduce the number of stops by up to 90% and delay by approximately 5%.

This research aims to model system dynamics for mixed traffic flow consisting of Connected and Automated Vehicles (CAVs) and Human-driven Vehicles (HVs). It quantifies the impact of CAVs’ speed change on the overall traffic state on a real-time basis. The model describes the impedance of CAVs’ speed reduction on traffic flow and considers the impact of potential additional lane change induced by the speed reduction. To validate the effectiveness of the proposed model, a VISSIM based microscopic simulation evaluation is performed. The results confirm that the accuracy of the proposed model is generally over 80% with the CAVs’ speed reduction constrained within 20 km/h. Sensitivity analysis is conducted in terms of various CAV penetration rates and congestion levels. The proposed model demonstrates consistently good performance across all CAV penetration rates and congestion levels. A showcase is presented to show the effect of the system dynamics in active traffic management. The proposed model could serve as the foundation of CAV based traffic management applications, such as variable speed limit and speed harmonization.

This research proposed a robust Cooperative Adaptive Cruise Control (CACC) to overcome the shortcoming of the existing CACC controllers in dealing with unexpected events, such as malware attack, phishing attack, and DNS tunneling attack, where perception and data received via communication contradict with the reality. The proposed controller combines the advantage of two information flow topologies – All-Predecessor Following (APF) and Predecessor-Leader Following (PLF) control methods – to improve the capability of CACC platoons. The string stability of the proposed CACC controller was proven. The robustness to time delay switch (TDS) attacks was assessed using simulation. The normal cruise was simulated to show the capability of the proposed controller in a wide range of speeds. TDS attacks, which encompassed four different unexpected events, were tested to verify the robustness of the proposed CACC. Results confirmed that the proposed CACC controller is string stable and robust against these TDS attacks without crashing or causing jerks. It also showed that the proposed CACC controller outperformed a state-of-the-art CACC controller.

This research proposes a TSPcut controller that enables connected and automated buses to cut through traffic to make TSP green light. The proposed controller overcomes the shortcomings of conventional TSP strategies and is able to: 1) overtake slowing moving vehicles in order to catch TSP green time; 2) decide the best time to pass the intersection; 3) considering the stochasticity of surrounding traffic; and 4) functional under partially connected and automated environment. It takes full advantage of connected vehicle technology by taking in real-time vehicle and infrastructure information as optimization input. The problem is formulated as an SMPC problem and is solved by a high-efficient dynamic programming algorithm. The nonlinear bicycle model is adopted as the system dynamics to realize CAV bus’s lane-changing and overtaking function. The stochasticity of surrounding traffic is considered as a probability distribution which is transformed into a linear chance constraint. Simulation evaluation is conduct to compare the TSPcut against NTSP, CTSP and BocTSP. Sensitive analysis is conducted for congestion levels. The evaluation results demonstrate that the TSPcut improves the bus delay reduction by 17.9%–49.1%, and the benefits are 3.5% to 16.1% greater than that of other TSP systems. The range is caused by different congestion levels. In addition. Further tests are conducted to analyze how CAV bus’s arrival time and the speed of background traffic influence the performance of the TSPcut.

In this study, a Human-Lead-Platoon CACC ((HLP-CACC) controller is proposed for connected and automated vehicles to “include” human drivers in platooning process. The goal is to form a platoon between automated vehicles and human drivers so that turbulence caused by human drivers could be smoothed out by automated vehicles. Unlike the conventional CACC where only longitudinal control is automated, the proposed HLP-CACC regulates both longitudinally and laterally. In other words, the followers in an HLP-CACC platoon are fully autonomous. The controller is formulated utilizing model predictive control (MPC) solved by Chang-Hu’s method. The technology has the following advantages: 1) take advantage of human drivers’ perception to enable conditional full autonomy; 2) accommodate actuator delay in system dynamics to improve actuator control accuracy; 3) automates both longitudinally and laterally; and 4) ensures string stability in partially connected and automated vehicles environment. Both simulation tests and field tests were conducted to verify the effectiveness of the proposed algorithm. Four scenarios, including straight cruising, lane changing, U-turn and circling were tested. Sensitivity analysis was conducted for speed, turning radius, communication delay and oscillation acceleration. The results confirm that the proposed CACC controller is ready for field implementation. The computation time of the proposed optimal control is approximately$4~\sim ~8$milliseconds when running on an NVIDIA Drive PX 2 computer. Under the control of the proposed HLP-CACC, maximum longitudinal error and lateral error are both within 40 cm.

This paper proposed a speed harmonization controller for partially connected and automated traffic. It regulated the flow rate of the entire traffic by adjusting only the target cruising speed of Connected and Automated Vehicles (CAVs). The major breakthrough of the proposed controller is that it is able to manage mesoscopic level traffic by controlling microscope level status (desired speed) of a small portion of vehicles. To evaluate the proposed controller, a VISSIM based microscopic simulation evaluation was conducted. Sensitivity analysis was performed for CA V Penetration Rate (PR) and demand level (v/c ratio). Results confirm that the control accuracy of the proposed controller is over 85% across all CA V PRs and demand levels.

This research proposes a Model Predictive Control (MPC) based path tracker controller. It is designed for maneuvering an autonomous driving vehicle to follow its desired trajectory smoothly and accurately. The proposed path tracker has the following features: i) formulated in the time and space mixed-domain to improved control accuracy ii) with consideration of vehicle dynamics; iii) with consideration of vehicle control delay. Simulation and field test results demonstrate that the maximum longitudinal speed error is 2.3km/h and the maximum lateral position error is 11cm. It is 27% smaller than that of the conventional path-trackers. Moreover, the average computation time of the proposed path-tracker is 12 milliseconds on a laptop equipped with an Intel i7-4710MQ CPU. It indicates that the proposed path tracker is ready for real-time implementation.

This study investigates the problem of roadside unit (RSU) location optimization for information propagation under stochastic traffic conditions. The goal of RSU location optimization is to promote multihop information propagation in the Internet of Vehicles which is the promising application of the Internet of Things in transportation. Considering the information propagation time is significantly affected by traffic density and traffic density is endowed with randomness, the problem is formulated as a two-stage mixed-integer nonlinear stochastic programming. The model aims to minimize the sum of the cost associated with RSU investment and the expectation of the penalty cost associated with the network information propagation time exceeding an acceptable threshold. In the first stage of the programming, the number and location of RSUs are determined when network-wide traffic density is not realized. In the second stage, given the RSU location schemes determined in the first stage and the realization of traffic density, the information propagation shortest paths are determined for all origin-destination pairs to minimize network information propagation time. A genetic algorithm (GA) integrated with the solution of a mixed-integer linear programming (GA-MILP) is proposed to solve the model. Numerical results indicate that the advantage of the proposed model in the reduced information propagation time per cost over the deterministic model can be up to 15.54%. Compared with the conventional GA, the GA-MILP has 10.01% higher computation efficiency. This further leads to a 14.73% lower objective value achieved by the GA-MILP when the number of iterations is 50.

This article presents a mathematical model to design a demand-responsive connector (DRC) bus operational network for improving the service quality and accessibility of public transportation systems. The proposed model features an integrated framework that simultaneously guides passengers to reach their nearest bus stops and routes buses to transport passengers at selected bus stops to connected stations of major transit systems. Passengers' preferred time for pick up is fully considered in the model. For this purpose, this study proposes a multi-objective mixed-integer linear programing model to effectively capture the interactions between users with their predefined service time windows and DRC bus network. This study further develops a three-stage heuristic to yield suboptimal solutions to the model in a reasonable time. Case study results demonstrate the effectiveness of the proposed model.

In this research, an optimal control-based Cooperative Adaptive Cruise Control (CACC) system is proposed. The proposed system is able to enforce a target time gap between platoon members and is formulated in the space domain instead of the time domain which is adopted by most optimal control-based CACC systems in the past. By having this change, its robustness against communication failure is greatly improved and thus minimum safety headway buffer is reduced which leads to better mobility. In addition, third-order vehicle dynamics are modeled into the proposed control in order to improve control precision when implemented in the field. Local stability and string stability are theoretically proven. The proposed system is evaluated by simulation. Results reveal that the proposed CACC system outperforms the state-of-the-artH∞synthesis-based controller and linear feedback-based controller. The benefit of fuel consumption reduction ranges from 0.35% to 16.11%, while the benefit of CO2emission ranges from 0.48% to 12.40%. Furthermore, the proposed CACC improves local stability from 11.03% to 25.90%, and string stability by up to 23.82%. The computation speed of the proposed method is 1.26 ms (with prediction horizon as 1.5 s and resolution as 0.1 s) on a regular laptop which indicates the proposed system's potential to be applied in real-time.

Although Cooperative Adaptive Cruise Control (CACC) is a promising technology for Connected and Automated Vehicle (CAV), it is urgent to validate its applicability in real traffic situation. To support the validation, simulation plays a key role, but up-to-date simulation platforms are not generic enough in terms of CACC controller type, background traffic condition, road geometry and traffic control scheme. This paper proposes a generic simulation platform for CACC. It is featured by: i) Enabling evaluation of both CACC-controller performance and its impact on the transportation system; ii) Fast simulation speed and large-scale simulation; iii) Enabling simulation for human-machine task switching; iv) Compatibility with any CACC controller and any vehicle dynamics model.

This research proposes a distributed Successive Platoon-Lane-Change (SuPLC) controller based on optimal control.

Cooperative Adaptive Cruise Control (CACC) in previous researches typically refers to the linear controller with a gap policy. The system could not be designed to fulfill multiple objectives. This inspires the concept of optimal control based CACC in this paper. The basic procedure of the proposed controller is to gather the information collected by each vehicle to the computation unit first, then plan the trajectory of all the followers by solving an optimal control problem, and dispatch the optimal motion command to each vehicle at last. This paper models CACC under optimal control framework. A numerical approach inspired by dynamic programming is adopted to solve the control problem. The stability of the proposed controller is thoroughly investigated in terms of both local stability and string stability. To verify the concept of controller, solution, and the analysis about stability, simulation is carried out. The simulation verifies that the numerical method is effective with respect to computation time. Both theoretical analysis and simulation proved that the proposed optimal control based CACC is both local stable and string stable. The low computation burden, local stability, and string stability together guarantee the future implementation of the proposed controller.