5 papers
Beyond Planning: Differentiable Control-in-the-loop End-to-End Autonomous Driving
Zhidong Wang, Zirui Li, Zitong Shan +6
End-to-end autonomous driving has achieved remarkable progress by planning trajectories directly from raw sensor data. Yet the step from planning to control still relies on hand-tu…
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…
Ego-Dynamics-Augmented World Model for Autonomous Driving with Zero-Shot Cross-Embodiment Adaptation
Zhidong Wang, Jingsong Liang, Zirui Li +3
End-to-end autonomous driving requires generalization ability across platforms with dissimilar physical characteristics. The chassis defines the physical embodiment of each platfor…
AutoMoT: A Unified Vision-Language-Action Model with Asynchronous Mixture-of-Transformers for End-to-End Autonomous Driving
Wenhui Huang, Songyan Zhang, Qihang Huang +6
Integrating vision-language models (VLMs) into end-to-end (E2E) autonomous driving (AD) systems has shown promise in improving scene understanding. However, existing integration st…
HGNET: A Hierarchical Feature Guided Network for Occupancy Flow Field Prediction
Zhan Chen, Chen Tang, Lu Xiong
Predicting the motion of multiple traffic participants has always been one of the most challenging tasks in autonomous driving. The recently proposed occupancy flow field predictio…