DSC-LLM:驾驶场景背景基于表示的轨迹预测框架与风险因素推理使用LLMs
Sunghun Kim1, Joobin Jin2, Seokjun Hong2
1Department of AI and Big Data, Soonchunhyang University, 22 Soonchunhyang-ro, Asan 31538, Republic of Korea.
Sensors (Basel, Switzerland)
|December 11, 2025
概括
本研究引入了一个多式联运框架,用于在城市地区准确预测自动驾驶轨迹. 它使用驾驶场景上下文 (DSC) 和多式联运大型语言模型 (MLLM) 进行可解释的,风险意识的运动解释.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
背景情况:
- 在复杂的城市环境中自动驾驶需要精确的轨迹预测.
- 可解释的上下文证据对于理解和信任预测的车辆运动至关重要.
研究的目的:
- 为驾驶场景环境 (DSC) 意识的轨迹预测开发一个多式联运框架.
- 通过使用上下文线索,为预测的运动提供风险意识的解释.
主要方法:
- 时间对象状态 (轨迹,速度,曲率,运动状态) 与语义相机图像的整合.
- 一个由四个模块组成的框架:行为特征提取,场景上下文提取,DSC增强预测和通过多式大型语言模型 (MLLM) 进行风险意识推理.
主要成果:
- 该框架在Rank2Tell数据集上取得了竞争性表现,其ADE为10.972,FDE为13.701,RMSE为8.782.
- 定性评估表明,DeepSeek-R1-Distill-Qwen-7B提供了最连贯和最符合背景的解释.
结论:
- 提出的方法证明了在复杂的城市环境中实现自动驾驶的可行性和适用性.
- 将DSC意识的预测与可解释的推理相结合,为城市自主导航提供了一个实用和透明的解决方案.
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