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arXiv 1809.03481eess.SYcs.SY
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纵向安全分析用于自动驾驶与人类车辆混合编队

Longitudinal Safety Analysis For Heterogeneous Platoon Of Automated And Human Vehicles

Tsinghua University(清华大学);The University of Hong Kong(香港大学)

Zi Yang, Xinpeng Wang, Xin Pei, Shuo Feng, Dajun Wang, Jianqiang Wang, S. C. WONG

AI总结:

本文研究了不同智能车辆市场渗透率下四种碰撞避让算法对混合编队安全的影响,通过定制化仿真平台分析碰撞率和严重性。

摘要:

随着环境感知、车辆控制和车际合作技术的进步,越来越多自动驾驶公司开始进行道路测试。但不久的将来,我们将面临包含智能连接车辆和人类车辆的混合交通。本文研究了四种碰撞避让算法在不同智能车辆市场渗透率下的影响。构建了一个定制化的仿真平台,可以初始化包含多个关键参数的编队。对于每个短时间间隔,车辆的动力学都会在运动学模型中更新并输入。如果发生碰撞,计算能量损失以代表碰撞严重性。四种碰撞避让算法在不同市场渗透率和编队位置下被比较。结果引发了关于混合编队安全性的有趣讨论。

英文摘要:

With the recent advancement in environmental sensing, vehicle control and vehicle-infrastructure cooperation technologies, more and more autonomous driving companies start to put their intelligent cars into road test. But in the near future, we will face a heterogeneous traffic with both intelligent connected vehicles and human vehicles. In this paper, we investigated the impacts of four collision avoidance algorithms under different intelligent connected vehicles market penetration rate. A customized simulation platform is built, in which a platoon can be initiated with many key parameters. For every short time interval, the dynamics of vehicles are updated and input in a kinematics model. If a collision occurs, the energy loss is calculated to represent the crash severity. Four collision avoidance algorithms are chosen and compared in terms of the crash rate and severity at different market penetration rate and different locations of the platoon. The results generate interesting debates on the issues of heterogeneous platoon safety.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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