2 citations · 2 across the 7 of their papers we have counts for
10 papers
FocusDrive: Reasoning with Visual Focus for Autonomous Driving
Zhiyuan Liu, Zehong Ke, Yuanxin Tian +7
Driving decisions depend on both where to focus and how to act on what is seen. Effective driving reasoning must establish which objects matter, where they are, and how they inform…
READ: Learning Risk-Informed Fields for End-to-End Autonomous Driving
Zhiyuan Liu, Yuanxin Tian, Zehong Ke +5
Autonomous driving requires more than recognizing what is present in a scene: a planner must determine how road structure, surrounding agents, and their motion states should influe…
Roadside-Cooperative Autonomous Driving: From Data Platform to Vision-Language End-to-End Reasoning
Yitao Xu, Tong Wu, Yiyan Wu +7
Vehicle-to-Everything (V2X) cooperation enables beyond-line-of-sight perception, mitigating occlusions in single-vehicle sensing. However, existing V2X benchmarks provide limited s…
RISE: Roadside Infrastructure Sequence Understanding across 3D Tracking and Structured Vision-Language Reasoning
Yanbo Jiang, Haotian Zheng, Jiahao Wang +10
We present RISE (Roadside Infrastructure Sequence Understanding and Evaluation), a framework spanning metric 3D tracking and structured vision-language reasoning in roadside sequen…
DRIFT: Drift and Aggregation for Motion Planning
Yining Xing, Zhiyuan Liu, Zehong Ke +2
End-to-end trajectory planners need to represent multiple plausible driving behaviors while producing a single executable trajectory under real-time constraints. Proposal-based app…
Vault: One-Step Latent Generation with Positive-Anchored Rewards for Autonomous Driving
Yining Xing, Zehong Ke, Zhiyuan Liu +3
End-to-end autonomous driving must balance multimodal maneuver generation against real-time inference constraints. Diffusion planners capture diverse behaviors, but their iterative…
From Scene to Object: Text-Guided Dual-Gaze Prediction
Zehong Ke, Yanbo Jiang, Jinhao Li +5
Interpretable driver attention prediction is crucial for human-like autonomous driving. However, existing datasets provide only scene-level global gaze rather than fine-grained obj…
MISTY: High-Throughput Motion Planning via Mixer-based Single-step Drifting
Yining Xing, Zehong Ke, Yiqian Tu +3
Multi-modal trajectory generation is essential for safe autonomous driving, yet existing diffusion-based planners suffer from high inference latency due to iterative neural functio…
S2O: An Integrated Driving Decision-making Performance Evaluation Method Bridging Subjective Feeling to Objective Evaluation
Yuning Wang, Zehong Ke, Yanbo Jiang +4
Autonomous driving decision-making is one of the critical modules towards intelligent transportation systems, and how to evaluate the driving performance comprehensively and precis…
D2E-An Autonomous Decision-making Dataset involving Driver States and Human Evaluation
Zehong Ke, Yanbo Jiang, Yuning Wang +3
With the advancement of deep learning technology, data-driven methods are increasingly used in the decision-making of autonomous driving, and the quality of datasets greatly influe…