3 citations · 11 across the 25 of their papers we have counts for
8 papers · 2 filters
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…
CoReVLA: A Dual-Stage End-to-End Autonomous Driving Framework for Long-Tail Scenarios via Collect-and-Refine
Shiyu Fang, Yiming Cui, Haoyang Liang +3
Autonomous Driving (AD) systems have made notable progress, but their performance in long-tail, safety-critical scenarios remains limited. These rare cases contribute a disproporti…
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 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…
A Vehicle-Infrastructure Multi-layer Cooperative Decision-making Framework
Yiming Cui, Shiyu Fang, Peng Hang +1
Autonomous driving has entered the testing phase, but due to the limited decision-making capabilities of individual vehicle algorithms, safety and efficiency issues have become mor…
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…