Heterogeneous vehicle platoons, consisting of a human-driven vehicle (HDV) as the leader and connected automated vehicles (CAVs) as followers, present a promising solution to address various challenges arising from fully autonomous driving. In this paper, we propose a novel LSTM-based distributed model predictive control (DMPC) platooning method. Initially, we develop and train a vehicle acceleration prediction model based on a long short-term memory (LSTM) network using real-world driving data. Subsequently, the predicted acceleration sequence of the leading HDV is integrated into the DMPC-based platoon control model for the following CAVs. To validate the effectiveness of our method, we conduct simulation experiments using real-world driving data. The results demonstrate that, with a time headway of 1 s, the maximum speed error and maximum spacing error of the heterogeneous vehicle platoon using the proposed LSTM-based DMPC are reduced by at least 5.8% and 5.9%, respectively, compared to the traditional DMPC method. Furthermore, the LSTM-based DMPC outperforms the Transformer-based DMPC method, resulting in a 1.0% reduction in maximum speed error and a 0.7% reduction in maximum spacing error. The proposed method effectively dampens oscillation caused by the leading HDV and enhances tracking accuracy.

Cooperative adaptive cruise control (CACC) has important significance for the development of the connected and automated vehicle (CAV) industry. The traditional proportional integral derivative (PID) platoon controller adjustment is not only time-consuming and laborious, but also unable to adapt to different working conditions. This paper proposes a learning control method for a vehicle platooning system using a deep deterministic policy gradient (DDPG)-based PID. The main contribution of this study is automating the PID weight tuning process by formulating this objective as a deep reinforcement learning (DRL) problem. The longitudinal control of the vehicle platooning is divided into upper and lower control structures. The upper-level controller based on the DDPG algorithm can adjust the current PID controller parameters. Through offline training and learning in a SUMO simulation software environment, the PID controller can adapt to different road and vehicular platooning acceleration and deceleration conditions. The lower-level controller controls the gas/brake pedal to accurately track the desired acceleration and speed. Based on the hardware-in-the-loop (HIL) simulation platform, the results show that in terms of the maximum speed error, for the DDPG-based PID controller this is 0.02–0.08 m/s less than for the conventional PID controller, with a maximum reduction of 5.48%. In addition, the maximum distance error of the DDPG-based PID controller is 0.77 m, which is 14.44% less than that of the conventional PID controller.

As autonomous vehicles increasingly populate roads, robust testing is essential to ensure their safety and reliability. Due to the limitation that traditional testing methodologies (real-world and simulation testing) are difficult to cover a wide range of scenarios and ensure repeatability, this study proposes a novel virtual-real fusion testing approach that integrates Graph Theory and Artificial Potential Fields (APF) in virtual-real fusion autonomous vehicle testing. Conducted using SUMO software, our strategic lane change and speed adjustment simulation experiments demonstrate that our approach can efficiently handle vehicle dynamics and environmental interactions compared to traditional Rapidly-exploring Random Tree (RRT) methods. The proposed method shows a significant reduction in maneuver completion times—up to 41% faster in simulations and 55% faster in real-world tests. Field experiments at the Vehicle-Road-Cloud Integrated Platform in Suzhou High-Speed Railway New Town confirmed the method’s practical viability and robustness under real traffic conditions. The results indicate that our integrated approach enhances the authenticity and efficiency of testing, thereby advancing the development of dependable, autonomous driving systems. This research not only contributes to the theoretical framework but also has practical implications for improving autonomous vehicle testing processes.

Automated valet parking (AVP) is a key component of autonomous driving systems. Its functionality and reliability need to be thoroughly tested before road application. Current testing technologies are limited by insufficient scenario coverage and lack of comprehensive evaluation indices. This study proposes an AVP testing and evaluation framework using OnSite (Open Naturalistic Simulation and Testing Environment) and Unity3D platforms. Through scenario construction based on field-collected data and model reconstruction, a testing scenario library is established, complying with industry standards. A simplified kinematic model, balancing simulation accuracy and operational efficiency, is applied to describe vehicle motion. A multidimensional evaluation system is developed with completion rate as a primary index and operation performance as a secondary index, which considers both parking efficiency and accuracy. Over 500 AVP algorithms are tested on the OnSite platform, and the testing results are evaluated through the Unity3D platform. The performance of the top 10 algorithms is analyzed. The evaluation platform is compared with CARLA simulation platform and field vehicle testing. This study finds that the framework provides an effective tool for AVP testing and evaluation; a variety of high-level AVP algorithms are developed, but their flexibility in complex dynamic scenarios has limitations. Future research should focus on exploring more sophisticated learning-based algorithms to enhance AVP adaptability and performance in complex dynamic environment.

End-to-end autonomous driving has demonstrated remarkable potential due to its strong scene-understanding capabilities. However, its performance degrades significantly in the presence of occlusions and complex multi-agent interactions, posing serious safety risks. Existing methods struggle to understand partially observed environments and accurately predict the dynamic behaviors of surrounding agents. To address these limitations, we propose OAIAD (Occlusion-Aware Interactive End-to-End Autonomous Driving), a novel end-to-end framework designed to enhance occlusion reasoning and interaction awareness. This framework specifically addresses the critical challenge of right-of-way conflicts in complex multi-agent scenarios. OAIAD employs a stereoscopic vectorized representation to explicitly model occluded areas and incorporates a module for joint optimization of trajectory prediction and planning to better capture future agent dynamics. By explicitly modeling interactive behaviors and leveraging joint trajectory optimization, OAIAD enhances the ego vehicle’s ability to negotiate the right-of-way interactions in a safe and socially compliant manner, significantly reducing conflict-induced collisions. Extensive evaluations on both open- and closed-loop datasets demonstrate that OAIAD significantly improves performance in occlusion-heavy and interaction-intensive scenarios. Real-world experiments further validate the practicality and robustness of our approach, highlighting its potential for deployment in complex urban environments.

This paper proposes a model-data-driven control method for a human-leading vehicle platoon, comprising a human-driven vehicle (HDV) as the leader and connected automated vehicles (CAVs) as followers. Initially, a representative trajectory of HDVs is constructed using principal component analysis and the K -means clustering algorithm, which is utilized as training dataset. Subsequently, we propose a novel platooning method, named deep reinforcement learning with model-based guidance (DRLMG). The output of model predictive control (MPC) is integrated into the input state and reward function of the deep reinforcement learning (DRL) algorithm. The DRL algorithm benefits from guidance provided by MPC, leading to more optimal decision-making. To ensure safety and stability, a safety filter is designed using control barrier function and the control Lyapunov function. Simulation experiments with real-world driving data show that DRLMG outperforms MPC, reducing speed error, spacing error, and acceleration change rate by 17.9%, 53.7%, and 47.1%, respectively. In comparison to pure DRL, DRLMG increases spacing error by 6.5% but reduces speed error by 15.4% and acceleration change rate by 14.3%. The proposed method enhances DRL’s generalization capability, dampens traffic oscillations caused by the leading HDV, and guarantees driving safety and stability.