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cs.RO2026
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…
cs.RO2026
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…
cs.RO2026
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…