4 papers
CogAD: Cognitive-Hierarchy Guided End-to-End Autonomous Driving
Zhennan Wang, Jianing Teng, Canqun Xiang +4
While end-to-end autonomous driving has advanced significantly, prevailing methods remain fundamentally misaligned with human cognitive principles in both perception and planning.…
EgoLive: A Large-Scale Egocentric Dataset from Real-World Human Tasks
Yihang Li, Xuelong Wei, Jingzhou Luo +26
The advancement of robot learning is currently hindered by the scarcity of large-scale, high-quality datasets. While established data collection methods such as teleoperation and u…
HMVLM: Multistage Reasoning-Enhanced Vision-Language Model for Long-Tailed Driving Scenarios
Daming Wang, Yuhao Song, Zijian He +4
We present HaoMo Vision-Language Model (HMVLM), an end-to-end driving framework that implements the slow branch of a cognitively inspired fast-slow architecture. A fast controller…
HMAD: Advancing E2E Driving with Anchored Offset Proposals and Simulation-Supervised Multi-target Scoring
Bin Wang, Pingjun Li, Jinkun Liu +7
End-to-end autonomous driving faces persistent challenges in both generating diverse, rule-compliant trajectories and robustly selecting the optimal path from these options via lea…