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Safety benefit assessment of autonomous emergency braking and steering systems for the protection of cyclists and pedestrians based on a combination of computer simulation and real-world test results

机译:基于计算机模拟和真实测试结果的结合,用于保护骑自行车者和行人的自主紧急制动和转向系统的安全效益评估

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摘要

Cyclists and pedestrians account for a significant share of fatalities and serious injuries in the road transport system. In order to protect them, advanced driver assistance systems are being developed and introduced to the market, including autonomous emergency braking and steering systems (AEBSS) that autonomously perform braking or an evasive manoeuvre by steering in case of a pending collision, in order to avoid the collision or mitigate its severity.This study proposes a new prospective framework for quantifying safety benefit of AEBSS for the protection of cyclists and pedestrians in terms of saved lives and reduction in the number of people suffering serious injuries. The core of the framework is a novel application of Bayesian inference in such a way that prior information from counterfactual simulation is updated with new observations from real-world testing of a prototype AEBSS.As an illustration of the method, the framework is applied for safety benefit assessment of the AEBSS developed in the European Union (EU) project PROSPECT. In this application of the framework, counterfactual simulation results based on the German In-Depth Accident Study Pre-Crash Matrix (GIDAS-PCM) data were combined with results from real-world tests on proving grounds.The proposed framework gives a systematic way for the combination of results from different sources and can be considered for understanding the real-world benefit of new AEBSS. Additionally, the Bayesian modelling approach used in this paper has a great potential to be used in a wide range of other research studies.
机译:在道路运输系统中,骑自行车的人和行人在死亡和重伤中占很大比例。为了保护它们,正在开发先进的驾驶员辅助系统并将其推向市场,包括自动紧急制动和转向系统(AEBSS),该系统可在发生未决碰撞的情况下通过转向自动执行制动或回避操作,以避免这项研究提出了一个新的前瞻性框架,用于量化AEBSS的安全性收益,从挽救生命和减少严重受伤的人数方面保护骑自行车者和行人。该框架的核心是贝叶斯推理的一种新颖应用,其方式是利用真实AEBSS原型测试中的新观察结果更新来自反事实模拟的先验信息。欧盟(PRO)计划中开发的AEBSS的效益评估。在该框架的应用中,将基于德国深度事故研究的碰撞前矩阵(GIDAS-PCM)数据的反事实模拟结果与来自真实世界的试验结果相结合。来自不同来源的结果的组合,可以用来理解新AEBSS的现实利益。此外,本文中使用的贝叶斯建模方法具有广阔的潜力,可用于其他广泛的研究。

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