驾驶-拉玛:通过大型语言模型在智能驾驶中预测驾驶员意图
Yi Chen1, Chengzhe Li1, Qirui Yuan1
1College of Automotive Engineering, Jilin University, Changchun 130025, China.
Sensors (Basel, Switzerland)
|January 11, 2025
概括
一个新的语言模型,Cockpit-Llama,通过分析动作和环境状态,准确地预测驾驶员的意图. 这增强了智能驾驶中的主动交互,优于现有的模型.
科学领域:
- 人与计算机的交互
- 人工智能的人工智能
- 汽车工程 汽车工程
背景情况:
- 汽车驾驶正在从反应性互动过渡到主动性互动,需要准确的驾驶员意图预测.
- 加强主动互动需要了解驾驶员的行为和驾驶环境中的认知状态.
研究的目的:
- 介绍Cockpit-Llama,一种用于预测驾驶员行为意图的新型语言模型.
- 通过利用多属性驾驶数据来提高驾驶员意图预测的准确性和合理性.
主要方法:
- 开发了Cockpit-Llama,这是一个基于动作,历史交互和环境状态的驾驶员意图预测的语言模型.
- 构建了一个多属性驾驶数据集,包括驾驶员的情绪,驾驶,车辆,身体和环境状态.
- 使用低级调整 (LoRA) 方法对Llama3-8b-Instruct模型进行了微调,以实现高效的参数优化.
主要成果:
- 在多属性驾驶数据集上,Cockpit-Llama与先进方法相比,显示出更高的预测性能.
- 获得的高分数:蓝色-4 (71.32),红色-1 (80.01),红色-2 (76.89),和红色-L (81.42).
- 与ChatGPT-4相比,显示出显著的相对改善,包括ROUGE-1的183.61%和ROUGE-L.的201.27%.
结论:
- Cockpit-Llama有效地预测驾驶员的意图,增强智能驾驶中的主动互动.
- 该模型显著提高了汽车系统的推理和解释能力.
- 开发的多属性数据集和LoRA微调有助于高效和准确的驾驶员行为建模.
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