2 citations · 2 across the 7 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…