3 papers
cs.RO2026
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.…
cs.CV2026
X-World: Controllable Ego-Centric Multi-Camera World Models for Scalable End-to-End Driving
Chaoda Zheng, Sean Li, Jinhao Deng +9
Scalable and reliable evaluation is increasingly critical in the end-to-end era of autonomous driving, where vision--language--action (VLA) policies directly map raw sensor streams…
cs.CV2025
FutureX: Enhance End-to-End Autonomous Driving via Latent Chain-of-Thought World Model
Hongbin Lin, Yiming Yang, Yifan Zhang +10
In autonomous driving, end-to-end planners learn scene representations from raw sensor data and utilize them to generate a motion plan or control actions. However, exclusive relian…