2 citations · 3 across the 7 of their papers we have counts for
6 papers · 1 filter
Data-Driven Risk Fields for Safer End-to-End Autonomous Driving
Yuanxin Tian, Zhiyuan Liu, Jinhao Li +9
Safety is a fundamental requirement for autonomous driving, yet existing end-to-end driving models still lack explicit risk-aware learning capacities. Existing rule-based risk mode…
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…
CLEAR: Cognition and Latent Evaluation for Adaptive Routing in End-to-End Autonomous Driving
Yining Xing, Zehong Ke, Zhiyuan Liu +3
End-to-end autonomous driving models often struggle to balance multi-modal maneuver generation with real-time inference constraints. While diffusion models successfully capture div…
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…
Controllable Traffic Simulation through LLM-Guided Hierarchical Reasoning and Refinement
Zhiyuan Liu, Leheng Li, Yuning Wang +5
Evaluating autonomous driving systems in complex and diverse traffic scenarios through controllable simulation is essential to ensure their safety and reliability. However, existin…