Current LiDAR-based Vehicle-to-Everything (V2X) multi-agent perception systems have shown the significant success on 3D object detection. While these models perform well in the trained clean weather, they struggle in unseen adverse weather conditions with the domain gap. In this paper, we propose a Domain Generalization based approach, named V2X-DGW, for LiDAR-based 3D object detection on multi-agent perception system under adverse weather conditions. Our research aims to not only maintain favorable multi-agent performance in the clean weather but also promote the performance in the unseen adverse weather conditions by learning only on the clean weather data. To realize the Domain Generalization, we first introduce the Adaptive Weather Augmentation (AWA) to mimic the unseen adverse weather conditions, and then propose two alignments for generalizable representation learning: Trust-region Weatherinvariant Alignment (TWA) and Agent-aware Contrastive Alignment (ACA). To evaluate this research, we add Fog, Rain, Snow conditions on two publicized multi-agent datasets based on physics-based models, resulting in two new datasets: OPV2V-w and V2XSet-w. Extensive experiments demonstrate that our V2X-DGW achieved significant improvements in the unseen adverse weathers. The code is available at https://github.com/Baolu1998/V2X-DGW.

LiDAR-based Vehicle-to-Everything (V2X) cooperative perception has demonstrated its impact on the safety and effectiveness of autonomous driving. Since current cooperative perception algorithms are trained and tested on the same dataset, the generalization ability of cooperative perception systems remains underexplored. This paper is the first work to study the Domain Generalization problem of LiDAR-based V2X cooperative perception (V2X-DG) for 3D detection based on four widely-used open source datasets: OPV2V, V2XSet, V2V4Real and DAIR-V2X. Our research seeks to sustain high performance not only within the source domain but also across other unseen domains, achieved solely through training on source domain. To this end, we propose Cooperative Mixup Augmentation based Generalization (CMAG) to improve the model generalization capability by simulating the unseen cooperation, which is designed compactly for the domain gaps in cooperative perception. Furthermore, we propose a constraint for the regularization of the robust generalized feature representation learning: Cooperation Feature Consistency (CFC), which aligns the intermediately fused features of the generalized cooperation by CMAG and the early fused features of the original cooperation in source domain. Extensive experiments demonstrate that our approach achieves significant performance gains when generalizing to other unseen datasets while it also maintains strong performance on the source dataset.

Although extensive research has been conducted on modeling the stable longitudinal controller of automated vehicles (AVs) to dampen traffic oscillations, the real-world performance of these controllers in actual vehicles remains uncertain. In the operation of real-world AVs, the delay between actual dynamics and the commands prevents the controller's command from being effectively implemented to dampen traffic oscillations. Thus, this study adapts the designed controllers within an AV test platform to compare the theoretically stable conditions with the actual oscillation dampening performance. Initially, we compute the stable conditions for both the traditional car-following controller, which assumes no delay, and the longitudinal controller that accounts for the dynamic response of the vehicle. Through empirical experiments, we demonstrate that the longitudinal controller predicts vehicle stability more accurately than conventional car-following controller, showing an improvement from an average prediction accuracy rate of 0.59 to 0.91. Also, the experiments uncover specific delays inherent in dynamics systems, with a response delay of 0.34 seconds. Our work makes two principal contributions to the field of AV control systems. First, it empirically validates that the longitudinal model, which accounts for the vehicle's dynamic responses, offers a more precise representation of vehicular behavior. Second, the relatively brief response delay identified expands the stability region, thereby enhancing vehicle control and safety. The longitudinal controller is critical for enhancing AV performance and reliability in dampening traffic oscillations.

Cooperative driving systems are expected to enhance safety, mobility, and efficiency through vehicle connectivity technologies. Lower-level vehicle-to-vehicle (V2V) communication transmits high-frequency status information, such as location, velocity, and acceleration, between vehicles. This approach contributes limitedly to prediction accuracy, requires high-frequency hardware, and is sensitive to communication delays. Recent studies have shown that intent sharing, which conveys planning trajectories, significantly improves prediction accuracy and control performance but requires higher band-width. However, mainstream vehicle communication methods struggle to balance cost and bandwidth for effective intent sharing. High-bandwidth wireless communication methods such as dedicated short-range communication (DSRC) and cellular vehicle-to-everything (C-V2X) cost much for devices, while low-cost visible light communication (VLC) can hardly support the necessary bandwidth. To address this challenge, we propose a lightweight intent sharing approach that reduces data transmission volume while maintaining prediction accuracy. Specifically, intended velocity trajectories are represented using regressed polynomial functions over a fixed time period, requiring only the transmission of polynomial coefficients and a timestamp for synchronization. The feasibility of this approach is demonstrated through simulations of car-following behavior using a Linear–Quadratic Regulator (LQR). Additionally, real vehicle experiments using a designated velocity cycle further validate the method. Results show that both planned and actual trajectories of the following vehicle closely align with those using ideal intent sharing approaches under significantly reduced communication data volume.

Connected and Automated Vehicles (CAVs) have the potential to enhance traffic safety and efficiency. In contrast, aligning both vehicles’ utility with system-level interests in scenarios with conflicting road rights is challenging, hindering cooperative driving. This paper advocates a game theory model, which strategically incorporates deceptive information within incomplete information vehicle games, operating under the premise of imprecise perceptions. The equilibria derived reveal that CAVs can exploit deceptive strategies, not only gaining advantages that undermine the utility of the other vehicle in the game but also posing hazards to the overall benefits of the transportation system. Vast experiments were conducted, simulating diverse inbound traffic conditions at an intersection, validating the detrimental impact on efficiency and safety resulting from CAVs with perception uncertainties, and employing deceptive maneuvers within connected and automated transportation systems. Finally, the paper proposes feasible solutions and potential countermeasures to address the adverse consequences of deception in connected and automated transportation systems. It concludes by calling for integrating these insights into future research endeavors and pursuing to fully realize the potential and expectations of CAVs in enhancing the whole traffic performance.

Many studies have demonstrated the eco-driving capabilities of connected and automated vehicles (CAVs) to significantly enhance mobility systems. The majority of these studies have been conducted using simulations, which fail to capture the effects of practical uncertainties encountered in vehicle-to-anything (V2X) communications. In this paper, we investigated the performance of current cellular V2X (C-V2X) communications through systematic testing and provided a quantitative analysis of key performance indices (e.g., inter-packet gap and packet error rate) across various test scenarios. As one use case to demonstrate the benefits of C-V2X communication on the road, we tested the feasibility of eco-driving for a SAE level 3 (L3) automated vehicle (AV) communicating with a connected urban corridor capable of transmitting traffic light information (i.e., signal phase and timing). To achieve this, we implemented the eco-speed planning algorithm at a high-level in the AV control software system and ensured its interactions with other existing low-level control algorithms, as well as the C-V2X onboard unit. Finally, we experimentally demonstrated eco-driving of the L3 CAV on a scaled-down corridor with two signal-controlled intersections, revealing the AV’s ability to maintain smoother trajectories and avoid unnecessary stops compared to human-driven vehicles.

Recent research has demonstrated that the Vehicle-to-Everything (V2X) communication techniques can fundamentally improve the perception system for autonomous driving by collaborating between vehicle and infrastructure sensors. LiDAR is the commonly-used sensor for V2X autonomous driving due to its robustness in challenging scenarios. However, the LiDAR sensor is expensive, so the cost of equipping LiDAR sensors to a large number of infrastructures on the large-scale roadway network is extremely high, which has limited the wide deployment of the V2X cooperative perception system. How to discover an economic V2X cooperative perception system is never been well studied before. Inspired by the cost difference of the various point cloud densities of LiDAR, we propose the first Density-Sensitive Infrastructure LiDAR benchmark for economic V2X cooperative perception, named V2X-DSI, in this paper. Using the proposed V2X-DSI benchmark, we analyze the effect of cooperative perception performance under different beam infrastructure LiDAR. We specifically assess three state-of-the-art methods, i.e., OPV2V, V2X-ViT, and CoBEVT, using our V2X-DSI dataset. The results indicate that varying beam infrastructure LiDAR sensors play a crucial role in influencing cooperative perception performance.

This paper introduces a novel physical enhanced residual learning (PERL) framework for Connected Autonomous Vehicles (CAVs) platoon, aimed at addressing the challenges posed by the dynamic and unpredictable nature of traffic environments. The proposed framework synergistically combines a physical model, represented by Model Predictive Control (MPC), with data-driven online Q-learning. The MPC controller, enhanced for centralized CAV platoons, employs vehicle velocity as a control input and focuses on multi-objective cooperative optimization. The learning-based residual controller enriches the MPC with prior knowledge and corrects residuals caused by traffic disturbances. The PERL framework not only retains the interpretability and transparency of physics-based models but also significantly improves computational efficiency and control accuracy in real-world scenarios. The experimental results present that the online Q-learning PERL controller, in comparison to the MPC controller and PERL controller with a neural network, exhibits significantly reduced position and velocity errors. Specifically, the PERL's cumulative absolute position and velocity errors are, on average, 86.73% and 55.28% lower than the MPC's, and 12.82% and 18.83% lower than the neural network-based PERL's, in four tests with different reference trajectories and errors. The results demonstrate our advanced framework's superior accuracy and quick convergence capabilities, proving its effectiveness in maintaining platoon stability under diverse conditions.

This paper presents a comprehensive review of trajectory datasets from vehicles equipped with Advanced Driver Assistance Systems, with the aim of precisely modeling the behavior of Autonomous Vehicles (AVs). This study emphasizes the importance of trajectory data in the development of AV models, especially in car-following scenarios. We introduce and evaluate several datasets: the OpenACC Dataset, the Connected & Autonomous Transportation Systems Laboratory Open Dataset, the Vanderbilt ACC Dataset, the Central Ohio Dataset, and the Waymo Open Dataset. Each dataset offers unique insights into AV behaviors, yet they share common challenges in terms of data availability, processing, and standardization. After a series of data cleaning, outlier removal, and statistical analysis, this paper transforms datasets of varied formats into a uniform standard, thereby improving their applicability for modeling AV car-following behavior. Key contributions of this study include: 1. the transformation of all datasets into a unified standard format, enhancing their utility for broad research applications; 2. a comparative analysis of these datasets, highlighting their distinct characteristics and implications for car-following model development; 3. the provision of guidelines for future data collection projects.

Connected and Automated Vehicles (CAV) platoon is regarded as a promising means of improving traffic efficiency and safety. This study focuses on addressing a Multi-Vehicle Motion Planning (MVMP) problem for CAVs aiming to form a platoon in the mixed traffic flow with both CAVs and Human-Driven Vehicles (HDV), which utilizes the cooperative capabilities of the multi-vehicle queue. Generally, the MVMP problem would be formulated into a centralized form, which is numerically intractable due to the computational complexity. In addition, the uncertainty of human drivers’ behavior in mixed traffic presents a challenge in motion planning for multiple CAVs simultaneously. To overcome these issues, we propose a decentralized MVMP framework based on the Monte Carlo Tree Search (MCTS) algorithm, which splits the MVMP problem into a series of lane change tasks. The MCTS algorithm is applied to determine an optimal lane change decision, facilitating the advancement of platoon formation based on the current vehicle state. Subsequently, we establish both the longitudinal position adjustment model and the lane change motion planning model to efficiently execute the lane change maneuver, as well as taking into account some evaluation factors such as safety, rapidity, and comfort. Finally, we develop a simulation platform using SUMO and MATLAB to simulate a three-lane freeway with mixed traffic. The simulation results demonstrate that the proposed approach can efficiently organize individual CAVs in the three lanes into a platoon under 20 scenarios including multiple traffic demands and CAV ratios. Furthermore, compared to the existing methods, the proposed approach achieves a better performance in terms of platooning proportion, time consumption, and time delay.

Urged by a close future perspective of a traffic flow made of a mix of human-driven vehicles and automated vehicles (AVs), research has recently focused on studying the traffic flow characteristics of Adaptive Cruise Controls (ACCs), the most typical AV. However, in most works, the ACC system is studied under a simplifying and unrealistic assumption, or the ACC system modeled is inaccurate. This paper proposes a general hierarchical control system to model ACC systems with several assumptions based on the deficiencies above. Moreover, a field experiment was conducted, and the corresponding experimental data was used to verify the proposed hierarchical control system and assumptions. In addition, string stability is explored along with sensitivity analyses of control parameters based on an example under the constant time gap policy. The results show that different upper-level controller parameters have different delays, where the delay of the speed is negligible; the introduction of actuator delay and lag in the lower-level controller can significantly improve the model goodness of fit. Furthermore, optimizing the delay and lag in the lower-level controller can significantly enhance the string stability of ACCs than optimizing the control parameters.

Vehicles with adaptive cruise control, i.e., SAE Levels 1 and 2 automated vehicles (AVs), have been operating on roads with a significant and rapidly growing penetration rate. Identifying these AVs is critical to understanding near-future mixed traffic characteristics and managing highway mobility and safety. This study identifies adaptive cruise control-equipped vehicles from human-driven vehicles (HVs) by constructing a set of learning-based models using car-following trajectories in a short time window. It is extendible to Level 3 and + AV identification when data is available. To compare model performance and draw physical insights, two physics-based models are proposed based on the premise that, in general, the car-following behavior of an AV is less volatile than an HV. Four car-following datasets, including AV makes from different manufacturers, are mixed to build a comprehensive identification model. Results show that physics-based approaches identify more than 80% AVs and 70% HVs. The identification accuracy of learning-based models is even higher. For example, the cluster-aware long short-term memory network identifies 98.79% of AVs and 95.45% of HVs. Learning-based identification models developed by this study can be integrated with the existing infrastructure (e.g., surveillance cameras), which have been used to extract car-following trajectories, to detect AVs in mixed traffic streams. This opens unparalleled data-driven opportunities to analyze and control mixed traffic to enhance safety (e.g., notifying surrounding traffic of the presence of AVs) and mobility (e.g., opening AV dedicated lanes when the percentage is great enough).