5 citations · 13 across the 24 of their papers we have counts for
10 papers · 1 filter
Zero-Human Demonstration End-to-end Autonomous Driving with Trajectory Scorer
Zhenxin Li, Nadine Chang, Wenhao Yao +9
Human demonstrations are widely considered the cornerstone of end-to-end (E2E) autonomous driving despite human demonstration's scarcity for long-tail and safety-critical scenarios…
VideoITG: Multimodal Video Understanding with Instructed Temporal Grounding
Shihao Wang, Guo Chen, De-an Huang +6
While Video Large Language Models (Video-LLMs) have shown significant potential in multimodal understanding and reasoning tasks, how to efficiently select the most informative fram…
Generalized Trajectory Scoring for End-to-end Multimodal Planning
Zhenxin Li, Wenhao Yao, Zi Wang +7
End-to-end multi-modal planning is a promising paradigm in autonomous driving, enabling decision-making with diverse trajectory candidates. A key component is a robust trajectory s…
DriveSuprim: Towards Precise Trajectory Selection for End-to-End Planning
Wenhao Yao, Zhenxin Li, Shiyi Lan +4
Autonomous vehicles must navigate safely in complex driving environments. Imitating a single expert trajectory, as in regression-based approaches, usually does not explicitly asses…
OmniDrive: A Holistic Vision-Language Dataset for Autonomous Driving with Counterfactual Reasoning
Shihao Wang, Zhiding Yu, Xiaohui Jiang +6
The advances in vision-language models (VLMs) have led to a growing interest in autonomous driving to leverage their strong reasoning capabilities. However, extending these capabil…
Hydra-MDP++: Advancing End-to-End Driving via Expert-Guided Hydra-Distillation
Kailin Li, Zhenxin Li, Shiyi Lan +6
Hydra-MDP++ introduces a novel teacher-student knowledge distillation framework with a multi-head decoder that learns from human demonstrations and rule-based experts. Using a ligh…
Centaur: Robust End-to-End Autonomous Driving with Test-Time Training
Chonghao Sima, Kashyap Chitta, Zhiding Yu +5
How can we rely on an end-to-end autonomous vehicle's complex decision-making system during deployment? One common solution is to have a ``fallback layer'' that checks the planned…
Enhancing Autonomous Driving Safety with Collision Scenario Integration
Zi Wang, Shiyi Lan, Xinglong Sun +4
Autonomous vehicle safety is crucial for the successful deployment of self-driving cars. However, most existing planning methods rely heavily on imitation learning, which limits th…
Hydra-NeXt: Robust Closed-Loop Driving with Open-Loop Training
Zhenxin Li, Shihao Wang, Shiyi Lan +3
End-to-end autonomous driving research currently faces a critical challenge in bridging the gap between open-loop training and closed-loop deployment. Current approaches are traine…
Eagle 2: Building Post-Training Data Strategies from Scratch for Frontier Vision-Language Models
Zhiqi Li, Guo Chen, Shilong Liu +24
Recently, promising progress has been made by open-source vision-language models (VLMs) in bringing their capabilities closer to those of proprietary frontier models. However, most…