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
Vehicle-to-Everything (V2X) cooperation enables beyond-line-of-sight perception, mitigating occlusions in single-vehicle sensing. However, existing V2X benchmarks provide limited support for closed-loop evaluation and language-grounded supervision, hindering the development of vision-language models (VLMs) for end-to-e...
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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