S2O:一个整合的驾驶决策绩效评估方法,将主观的感觉与客观的评估联系起来.
Yuning Wang1, Zehong Ke2, Yanbo Jiang3
1School of Vehicle and Mobility, Tsinghua University, 100084 Beijing, China; Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
Accident; analysis and prevention
|April 15, 2025
概括
这项研究引入了S2O,这是一种通过将主观的人类感受与安全和舒适等客观驱动因素相结合来评估自动驾驶性能的新方法. 与现有方法相比,S2O显著减少了评估错误.
科学领域:
- 智能运输系统 智能运输系统
- 自动驾驶技术 自动驾驶技术
- 决策系统 决策系统
背景情况:
- 评估自动驾驶性能对于智能交通系统至关重要.
- 目前的指标如轨迹偏差和有限的客观指标缺乏全面性和精确性.
- 现有的方法无法准确地捕捉人类的主观驾驶体验.
研究的目的:
- 提出S2O,一种用于评估自动驾驶决策的新型综合方法.
- 弥合主观人类感知与客观驾驶绩效评估之间的差距.
- 为自动驾驶系统开发一个精确和全面的评估指标.
主要方法:
- 开发了安全性,时间效率,舒适性和能源效率的基本模型.
- 设计了一个细分线性拟合模型,配有SVM细分分类器来解释人类主观评级.
- 利用D2E数据集,大约有1,000个驾驶案例和40,000个人类评级,用于模型培训和验证.
主要成果:
- S2O实现了4.58%的平均绝对误差与地面真相相比.
- 与基线方法相比,拟议的方法减少了32.55%的评估错误.
- 在SUMO平台上证明了实时评估效率,并在三个自动驾驶计划算法上验证了可行性.
结论:
- 在自动驾驶性能评估的精度和全面性方面,S2O提供了显著的改进.
- 该方法有效地将主观的人类驾驶经验转化为客观的,可量化的指标.
- S2O是一个可行的和有效的工具,用于加强自动驾驶决策系统的开发和修改.
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