1 citations · 2 across the 12 of their papers we have counts for
14 papers
A Survey on End-to-End Autonomous Driving Training from the Perspectives of Data, Strategy, and Platform
Chengkai Xu, Yiming Cui, Jiaqi Liu +7
Autonomous driving is a cornerstone technology for the future of intelligent transportation, where end-to-end learning has emerged as a transformative paradigm that directly maps m…
DriveHierarchy: A Benchmark for Diagnosing VLM Driving Capabilities from Open-Loop Understanding to Closed-Loop Execution
Chengkai Xu, Jiaqi Liu, Yicheng Guo +2
Evaluating VLM-based autonomous driving remains difficult because driving competence is composite, where a capable system must ground traffic participants and hazards, integrate co…
Evaluation as Evolution: Transforming Adversarial Diffusion into Closed-Loop Curricula for Autonomous Vehicles
Yicheng Guo, Jiaqi Liu, Chengkai Xu +2
Autonomous vehicles in interactive traffic environments are often limited by the scarcity of safety-critical tail events in static datasets, which biases learned policies toward av…
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…
Interact, Instruct to Improve: A LLM-Driven Parallel Actor-Reasoner Framework for Enhancing Autonomous Vehicle Interactions
Shiyu Fang, Jiaqi Liu, Chengkai Xu +3
Autonomous Vehicles (AVs) have entered the commercialization stage, but their limited ability to interact and express intentions still poses challenges in interactions with Human-d…
TeLL-Drive: Enhancing Autonomous Driving with Teacher LLM-Guided Deep Reinforcement Learning
Chengkai Xu, Jiaqi Liu, Shiyu Fang +4
Although Deep Reinforcement Learning (DRL) and Large Language Models (LLMs) each show promise in addressing decision-making challenges in autonomous driving, DRL often suffers from…
Recognize then Resolve: A Hybrid Framework for Understanding Interaction and Cooperative Conflict Resolution in Mixed Traffic
Shiyu Fang, Donghao Zhou, Yiming Cui +3
A lack of understanding of interactions and the inability to effectively resolve conflicts continue to impede the progress of Connected Autonomous Vehicles (CAVs) in their interact…
Language-Driven Policy Distillation for Cooperative Driving in Multi-Agent Reinforcement Learning
Jiaqi Liu, Chengkai Xu, Peng Hang +4
The cooperative driving technology of Connected and Autonomous Vehicles (CAVs) is crucial for improving the efficiency and safety of transportation systems. Learning-based methods,…
Towards Safe and Robust Autonomous Vehicle Platooning: A Self-Organizing Cooperative Control Framework
Chengkai Xu, Zihao Deng, Jiaqi Liu +4
In hybrid traffic environments where human-driven vehicles (HDVs) and autonomous vehicles (AVs) coexist, achieving safe and robust decision-making for AV platooning remains a compl…