Accuracy requirements for the road friction coefficient estimation of a friction-adaptive automatic emergency steer assist (ESA)

Tim Ahrenhold, Jannes Iatropoulos, Roman Henze
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Abstract

The number of traffic accidents resulting in personal injury and property damage is increasingly being reduced by effective advanced driver assistance systems (ADAS). Nevertheless, many traffic accidents still cannot be prevented today because they are due to wet, snow- and ice-covered roads. For this reason, the Institute of Automotive Engineering (IAE) of the Technical University of Braunschweig is investigating the road friction coefficient sensitivity and adaptation of advanced driver assistance systems (ADAS) currently in series production from 2018 to 2021 as part of the ‘Road Condition Cloud’ research project funded by the German Research Foundation (DFG) to increase driving safety, particularly on wet, snow- and ice-covered roads. In this article, the road friction coefficient sensitivity and adaptation of an automatic emergency steer assist is simulatively investigated. This assist overrides the driver to automatically execute an evasive maneuver. The driving maneuver used is a standardized obstacle-avoidance maneuver that is simulatively repeated on a dry, wet, snow- and ice-covered road. The road friction coefficient sensitivity shows that this test is already failed on a wet road because the simulated vehicle does not pass the second lane without errors. Subsequently, a road friction coefficient adaptation of the emergency steer assist is investigated. This adaptation varies the maximum lateral acceleration of the evasive trajectory depending on an estimated value of the road friction coefficient in order not to exceed the maximum adhesion coefficient of the wheels during the evasive maneuver. Ideally, the estimated value matches the true road friction coefficient so that the second lane is passed without errors even on a wet, snow- and ice-covered road. In contrast, an existing difference determines whether the second lane is reached. Finally, the necessary accuracy requirements of the road friction coefficient estimation are determined in an novel estimation error diagram. A road friction coefficient adaptation increases the driving safety of driver advanced assistance systems (ADAS) that are in series production today and future highly automated driving functions (HAF) and is necessary for automated driving because the driver is not present as a fallback level. The described results were presented before in [1].

摩擦自适应自动紧急转向辅助系统(ESA)道路摩擦系数估算的精度要求
有效的高级驾驶员辅助系统(ADAS)越来越多地减少了导致人身伤害和财产损失的交通事故数量。尽管如此,许多交通事故今天仍然无法预防,因为它们是由于潮湿、冰雪覆盖的道路造成的。由于这个原因,作为德国研究基金会(DFG)资助的“路况云”研究项目的一部分,布伦瑞克工业大学汽车工程研究所(IAE)正在调查目前于2018年至2021年批量生产的高级驾驶员辅助系统(ADAS)的道路摩擦系数灵敏度和自适应性,以提高驾驶安全性,尤其是在潮湿、冰雪覆盖的道路上。本文模拟研究了自动紧急转向辅助系统的道路摩擦系数灵敏度和自适应性。该辅助超越驾驶员以自动执行规避机动。所使用的驾驶机动是一种标准化的避障机动,在干燥、潮湿、冰雪覆盖的道路上模拟重复。道路摩擦系数灵敏度表明,该测试在潮湿的道路上已经失败,因为模拟车辆没有错误地通过第二条车道。随后,对紧急转向辅助系统的道路摩擦系数自适应进行了研究。这种适应根据道路摩擦系数的估计值来改变规避轨迹的最大横向加速度,以便在规避机动过程中不超过车轮的最大附着力系数。理想情况下,估计值与真实的道路摩擦系数相匹配,这样即使在潮湿、冰雪覆盖的道路上,第二条车道也能顺利通过。相反,现有的差异确定是否到达第二车道。最后,在一个新的估计误差图中确定了道路摩擦系数估计的必要精度要求。道路摩擦系数自适应提高了驾驶员高级辅助系统(ADAS)的驾驶安全性,该系统目前和未来都在批量生产高度自动化驾驶功能(HAF),并且对于自动驾驶是必要的,因为驾驶员不是后备级别。上述结果已在[1]中介绍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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