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 models provide interpretable safety priors, yet their absolute risk scores depend on handcrafted functions, coefficients, and thresholds. Learni...
Yuan-Xin Tian, Zhi-Yuan Liu, Jin-Hao Li et al.· 0 citations
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 influence a future maneuver. Existing learning-based planners can capture these influences through latent scene features and trajectory decoders,...
Zhi-Yuan Liu, Yuan-Xin Tian, Ze-Hong Ke et al.· 0 citations
DRIFT, a fixed-depth planner that combines one-step drifting in a compact trajectory latent space with scene-aware proposal aggregation, is presented, showing that one-step latent proposal generation and direct aggregation provide an efficient design for multi-hypothesis motion planning.
Yi-Ning Xing, Zhi-Yuan Liu, Ze-Hong Ke et al.· arXiv.org· 1 citation
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