驾驶员在不同的场景下如何表现:用于大规模数据集的与能力相关的驾驶风格提取
Yingbang Zang1, Licheng Wen2, Pinlong Cai2
1Shanghai Artificial Intelligence Laboratory, Shanghai, 200232, China; School of Mathematics and Statistics, Wuhan University, Wuhan, 430072, China.
Accident; analysis and prevention
|December 30, 2023
概括
本研究引入了一种数据驱动的方法,利用自然驾驶数据客观分析驾驶风格. 它量化了驾驶能力和性能,揭示了在各种交通场景中不同的驾驶风格.
科学领域:
- 运输科学 运输科学
- 人与计算机的交互
- 数据科学数据科学数据科学
背景情况:
- 了解人类的驾驶行为至关重要,但目前的方法往往依赖主观数据.
- 自然驾驶数据为客观,大规模分析驾驶风格提供了机会.
研究的目的:
- 提出一种定量,数据驱动的方法,从自然驾驶数据中提取和分析驾驶风格.
- 区分和客观地衡量驾驶能力,驾驶表现和驾驶风格.
主要方法:
- 使用轨迹优化定义和导出驾驶能力和性能.
- 采用高斯混合模型用于客观驾驶风格提取.
- 利用Waymo的运动数据集进行经验验证.
主要成果:
- 成功提取驾驶能力和表现,并确定驾驶风格.
- 量化统计分布来描述驾驶风格.
- 确定了驾驶风格和特定场景 (拥堵,十字路口) 之间的显著相关性.
结论:
- 拟议的数据驱动框架允许客观分析自然环境中的驾驶风格.
- 驾驶风格表现出不同的模式,受交通条件和道路环境的影响.
- 这种方法为理解和建模各种人类驾驶行为提供了坚实的基础.
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