从概念到表现:模拟驾驶能力和任务需求,使用多模式大语言模型.
Haoran Zhou1, Alexander Carballo2,3,4, Keisuke Fujii1,5
1Graduate School of Informatics, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan.
Sensors (Basel, Switzerland)
|September 27, 2025
概括
本研究介绍了一种多式联通大型语言模型框架,用于量化驱动任务需求和能力. 它提供了任务难度的可解释度量,使智能汽车的积极风险评估成为可能.
科学领域:
- 智能运输系统 智能运输系统
- 汽车安全领域的人工智能
- 人与计算机在驾驶过程中的互动.
背景情况:
- 驾驶安全依赖于任务需求和驾驶员能力之间的平衡.
- 目前的方法缺乏统一的,可量化的方法来评估这种相互作用.
- 现有的框架在不安全驾驶发生之前,难以预测潜在的能力短缺.
研究的目的:
- 开发一个新的框架,用多式联运数据量化驾驶任务需求和能力.
- 为主动风险评估创建任务难度的可解释的测量.
- 为了能够及早发现驾驶员能力下降和潜在的安全风险.
主要方法:
- 使用多式大型语言模型 (LLM) 集成场景图像,机动描述,控制输入和交通状态.
- 将异质的驾驶信号转化为低维的嵌入式,以满足任务需求和驾驶能力.
- 预计嵌入到共享的潜在空间中,以进行可解释的任务难度测量.
- 采用定制的BLIP 2骨干,对各种模拟驾驶场景进行微调.
主要成果:
- 该框架成功地从多式联络输入中量化了任务需求和驱动能力.
- 创建了一个可解释的任务难度测量,与潜在的能力缺陷相关联.
- 该模型展示了任务中的一致性,并捕获了与损伤相关的能力退化.
- 该框架显示了有效的可转移到现实世界的高速公路数据,而不需要再培训.
结论:
- 拟议的基于LLM的框架为智能汽车的积极风险评估提供了一种简洁有效的方法.
- 它提供了驾驶困难的可解释的测量,这对于提高车辆安全至关重要.
- 这种方法为更复杂的安全系统铺平了道路,这些系统可以在风险出现之前预测和减轻风险.
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