4 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…
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…