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arXiv AI By Yan Shi, Kehua Chen, Jiarui Qi, Yihong Tang, Haichen Liao, Yinhai Wang

DriveMind: Mind-Evolving Belief Tracking for Closed-Loop Autonomous Driving

Read the original on arXiv AI →

DriveMind is a multimodal large language model (MLLM) framework for autonomous driving that maintains multiple hypotheses about a traffic scene and updates their weights as new frames arrive, inspired by particle filtering. By propagating each hypothesis through a vision-language model, scoring against current observations, and resampling to preserve diversity, DriveMind improves driving performance on the Bench2Drive benchmark, raising the Driving Score from 68.50 to 81.37 and the Success Rate from 41.36% to 54.09%. The approach yields the largest gains in ambiguous scenarios such as unsignalized intersections and oncoming-traffic interactions, and also enhances fine‑grained interactive driving tasks like merging, emergency braking, and give‑way.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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