arXiv cs.AI· Yansong Qu, Jiancong Chen, Samuel Labi·· 11 小时前AI 评分42
Jev-AD：用语言世界模型实现安全语言引导自动驾驶
Jev with Foresight: Safe Language-Guided Autonomous Driving via a Language World Model
AI 导读
Jev-AD 将类型化决策模型 Jev 与语言世界模型结合，后者预测每个候选动作下周边车辆未来两秒的运动，把预测转为可预见的后果供 Jev 比较。在 500 个模拟高速汇入场景中，该前瞻能力将同一决策模型的碰撞率从 26.0% 降至 2.4%，单次决策耗时 0.25 秒，93% 的自由形式指令试验达成乘客意图。
正文 · 原文
Abstract:Large language models understand passengers' driving commands, but as driving agents they reason in generated text for seconds and must infer each action's consequences implicitly from the scene. We present Jev-AD, which pairs Jev, a typed decision model that answers predefined questions with probabilities, not generated text, with a language world model that predicts for every candidate action how nearby vehicles move over the next two seconds. Code turns these predictions into foreseen consequences, which Jev compares instead of inferring them from coordinates. In simulated highway merging (500 car-episodes), this foresight lowers the same decision model's collision rate from 26.0% to 2.4% at 0.25 seconds per decision, and Jev-AD achieves the passenger's intent in 93% of free-form command trials. In 500 replayed real-world NGSIM episodes, foresight lowers the collision rate from 48.2% to 2.8%, and the world model, learned from this traffic, foresees 93% of dangerous actions. Project page: this https URL.
| Subjects: | Robotics (cs.RO); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.05448 [cs.RO] |
| (or arXiv:2611.05448v1 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2611.05448 arXiv-issued DOI via DataCite |
Submission history
From: Yansong Qu [view email]
[v1]
Tue, 3 Nov 2026 17:36:47 UTC (1,173 KB)
来源：arXiv cs.AI · arxiv.org