14 papers
Touching Projected Agents through the DAT Screen -System Design and Questions of Interaction Boundaries-
Takaya Tamamoto, Tomoko Yonezawa
Touching a virtual agent involves more than detecting a finger on a display: the contact surface can deform and resist the user's push, and the agent can respond to that action. We…
OccWAM: Occupancy World-Action Models with Joint Scene-to-Ego Flow Matching
Qihang Huang, Wenhui Huang, Collister Chua +6
Occupancy world models provide a structured, metric representation of future driving scenes, making them a promising basis for end-to-end autonomous driving. However, existing occu…
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…
WCog-VLA: A Dual-Level World-Cognitive Vision-Language-Action Model for End-to-End Autonomous Driving
Xuerun Yan, Zhexi Lian, Nuoheng Zhang +5
Vision-Language-Action (VLA) models have advanced end-to-end autonomous driving. However, existing methods either lack comprehensive world cognition or suffer from fragmented world…
Large Language Model based Interactive Decision-Making for Autonomous Driving
Xinwei Dong, Jiyang Li, Jiabin Xie +5
In high-conflict mixed-traffic scenarios involving human-driven and autonomous vehicles, most existing autonomous driving systems default to overly conservative behaviors, lack pro…
Toward Cooperative Driving in Mixed Traffic: An Adaptive Potential Game-Based Approach with Field Test Verification
Shiyu Fang, Xiaocong Zhao, Xuekai Liu +4
Connected autonomous vehicles (CAVs), which represent a significant advancement in autonomous driving technology, have the potential to greatly increase traffic safety and efficien…
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,…
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…
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…
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…
Towards Interactive and Learnable Cooperative Driving Automation: a Large Language Model-Driven Decision-Making Framework
Shiyu Fang, Jiaqi Liu, Mingyu Ding +4
At present, Connected Autonomous Vehicles (CAVs) have begun to open road testing around the world, but their safety and efficiency performance in complex scenarios is still not sat…
A Decision-Making GPT Model Augmented with Entropy Regularization for Autonomous Vehicles
Jiaqi Liu, Shiyu Fang, Xuekai Liu +3
In the domain of autonomous vehicles (AVs), decision-making is a critical factor that significantly influences the efficacy of autonomous navigation. As the field progresses, the e…