8 citations · 8 across the 7 of their papers we have counts for
9 papers
A Unified Candidate Set with Scene-Adaptive Refinement via Diffusion for End-to-End Autonomous Driving
Zhengfei Wu, Shuaixi Pan, Shuohan Chen +2
End-to-end autonomous driving is increasingly adopting a multimodal planning paradigm that generates multiple trajectory candidates and selects the final plan, making candidate-set…
Driving in Corner Case: A Real-World Adversarial Closed-Loop Evaluation Platform for End-to-End Autonomous Driving
Jiaheng Geng, Jiatong Du, Xinyu Zhang +3
Safety-critical corner cases, difficult to collect in the real world, are crucial for evaluating end-to-end autonomous driving. Adversarial interaction is an effective method to ge…
GEMINUS: Dual-aware Global and Scene-Adaptive Mixture-of-Experts for End-to-End Autonomous Driving
Chi Wan, Yixin Cui, Jiatong Du +5
End-to-end autonomous driving requires adaptive and robust handling of complex and diverse traffic environments. However, prevalent single-mode planning methods attempt to learn an…
Chain-of-Thought for Autonomous Driving: A Comprehensive Survey and Future Prospects
Yixin Cui, Haotian Lin, Shuo Yang +3
The rapid evolution of large language models in natural language processing has substantially elevated their semantic understanding and logical reasoning capabilities. Such profici…
Continual Adaptation for Autonomous Driving with the Mixture of Progressive Experts Network
Yixin Cui, Shuo Yang, Chi Wan +5
Learning-based autonomous driving requires continuous integration of diverse knowledge in complex traffic , yet existing methods exhibit significant limitations in adaptive capabil…
Co-MTP: A Cooperative Trajectory Prediction Framework with Multi-Temporal Fusion for Autonomous Driving
Xinyu Zhang, Zewei Zhou, Zhaoyi Wang +3
Vehicle-to-everything technologies (V2X) have become an ideal paradigm to extend the perception range and see through the occlusion. Exiting efforts focus on single-frame cooperati…
A Safe Self-evolution Algorithm for Autonomous Driving Based on Data-Driven Risk Quantification Model
Shuo Yang, Shizhen Li, Yanjun Huang +1
Autonomous driving systems with self-evolution capabilities have the potential to independently evolve in complex and open environments, allowing to handle more unknown scenarios.…
A Safety-Oriented Self-Learning Algorithm for Autonomous Driving: Evolution Starting from a Basic Model
Shuo Yang, Caojun Wang, Zhenyu Ma +2
Autonomous driving vehicles with self-learning capabilities are expected to evolve in complex environments to improve their ability to cope with different scenarios. However, most…
A Safe and Efficient Self-evolving Algorithm for Decision-making and Control of Autonomous Driving Systems
Shuo Yang, Liwen Wang, Yanjun Huang +1
Autonomous vehicles with a self-evolving ability are expected to cope with unknown scenarios in the real-world environment. Take advantage of trial and error mechanism, reinforceme…