DRPVLM:用于实时驾驶风险预测的生成型多式联运大型语言模型
Junhua Wang1, Wenhao Zhang1, Ting Fu1
1The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804, China; College of Transportation, Tongji University, 4800 Cao'an Highway, Shanghai 201804, China.
Accident; analysis and prevention
|March 13, 2026
概括
大型语言模型 (LLM) 通过分析视觉和传感器数据,显著改善驾驶风险预测. 这项技术提高了驾驶员的安全性和对交通环境的了解.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 运输安全运输安全
背景情况:
- 大型语言模型 (LLM) 具有先进的理解能力,但它们在提高驾驶员对交通环境的理解和识别驾驶风险方面的应用尚未得到充分探索.
- 目前的车载系统在实时驾驶风险评估方面的能力有限.
研究的目的:
- 提出和评估驾驶风险预测视觉语言模型 (DRPVLM),用于识别实时驾驶风险.
- 评估与传统基于传感器的方法相比,多式联运LLM在驾驶风险预测方面所提供的性能提升.
主要方法:
- 通过使用LoRA微调了几个开源多式联络LLM (Qwen-2.5-VL,Gemma-3,Llama-3.2-Vision).
- 处理了上海自然驾驶研究的视频和图像数据,以提取多维特征 (道路环境,交通状况,驾驶员状态).
- 集成的LLM提取功能与结构化轨迹数据进入长短期内存 (LSTM) 网络,用于风险预测.
主要成果:
- 多模式LLM显著提高了驾驶风险预测的准确性,Qwen2.5-VL-32B实现了0.89-0.92准确性和0.88-0.91 F1得分.
- 所有经过测试的LLM都表现优于仅依赖结构化轨迹数据的基线模型,在更长的预测时间范围内,其准确性降至0.7以下.
- 特性重要性分析证实了LLM提取的变量在补充轨迹数据方面的有意义贡献.
结论:
- 多模式LLM在增强特征提取以预测驾驶风险方面是有效的.
- 拟议的DRPVLM框架显示了实时驾驶风险预测的巨大潜力,改善了整体道路安全.
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