9 papers
VP-AutoTest: A Virtual-Physical Fusion Autonomous Driving Testing Platform
Yiming Cui, Shiyu Fang, Jiarui Zhang +6
The rapid development of autonomous vehicles has led to a surge in testing demand. Traditional testing methods, such as virtual simulation, closed-course, and public road testing,…
A Knowledge-Driven Diffusion Policy for End-to-End Autonomous Driving Based on Expert Routing
Chengkai Xu, Jiaqi Liu, Yicheng Guo +2
End-to-end autonomous driving remains constrained by the difficulty of producing adaptive, robust, and interpretable decision-making across diverse scenarios. Existing methods ofte…
Interactive Adversarial Testing of Autonomous Vehicles with Adjustable Confrontation Intensity
Yicheng Guo, Chengkai Xu, Jiaqi Liu +3
Scientific testing techniques are essential for ensuring the safe operation of autonomous vehicles (AVs), with high-risk, highly interactive scenarios being a primary focus. To add…
LeAD: The LLM Enhanced Planning System Converged with End-to-end Autonomous Driving
Yuhang Zhang, Jiaqi Liu, Chengkai Xu +2
A principal barrier to large-scale deployment of urban autonomous driving systems lies in the prevalence of complex scenarios and edge cases. Existing systems fail to effectively i…
Towards Emergency Scenarios: An Integrated Decision-making Framework of Multi-lane Platoon Reorganization
Aijing Kong, Chengkai Xu, Xian Wu +2
To enhance the ability for vehicle platoons to respond to emergency scenarios, a platoon distribution reorganization decision-making framework is proposed. This framework contains…
Towards Human-Centric Autonomous Driving: A Fast-Slow Architecture Integrating Large Language Model Guidance with Reinforcement Learning
Chengkai Xu, Jiaqi Liu, Yicheng Guo +3
Autonomous driving has made significant strides through data-driven techniques, achieving robust performance in standardized tasks. However, existing methods frequently overlook us…