面向自动驾驶路测驾驶能力评估的避险脱离率模型

A Risk-Avoiding Disengagement Frequency Model for Assessing Driving Ability of Autonomous Vehicles in Road Testing

摘要:

针对传统脱离率模型低估自动驾驶路测车辆驾驶能力问题，提出了避险脱离与非避险脱离概念，通过秩和检验确定脱离时长阈值，选取平均速度差、短时平均速度差、瞬时速度差为特征值，量化车辆脱离前后行为差异，利用无监督学习K-means算法辨别避险与非避险脱离类型，构建面向自动驾驶路测驾驶能力评估的避险脱离率模型。基于上海市城市道路和快速路两类场景路测数据，验证避险脱离率模型的合理性与有效性。结果表明，基于避险脱离率模型，自动驾驶路测车辆驾驶能力在城市道路与快速路场景中，分别比传统脱离率模型平均提升了4.8和7.3倍。

Abstract:

To fill the gap that the traditional autopilot disengagements （ADs） frequency model has underestimated the driving ability of autonomous vehicles in road testing， this paper first proposed the concepts of risk-avoiding disengagements and non-risk-avoiding disengagements and then determined the disengagement temporal threshold by using the nonparametric rank sum test. Three characteristic values including average speed difference （ASD）， instantaneous speed difference （ISD）， and short-term speed difference （SSD） were considered as the indicators to quantify the differences of the behavior of vehicles before and after disengagements. Thus， the K-means clustering algorithm in unsupervised learning was used to distinguish the two ADs types. The risk-avoiding disengagement frequency （RADF） model for assessing the driving ability of autonomous vehicles in road testing was finally created. Based on the empirical data covering scenarios of urban road and expressway in Shanghai， the RADF model is verified. The results show that based on the RADF model the driving ability of autonomous vehicles in road testing has averagely been increased by 4.8 and 7.3 times respectively in the urban road scenario and expressway scenario， compared with the traditional ADs frequency model.