Enhancing mixed traffic safety assessment: A novel safety metric combined with a comprehensive behavioral modeling framework

IF 5.7 1区 工程技术 Q1 ERGONOMICS Accident; analysis and prevention Pub Date : 2024-09-06 DOI:10.1016/j.aap.2024.107766
Kangning Hou , Fangfang Zheng , Xiaobo Liu
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Abstract

In the context of future traffic systems, where automated vehicles (AVs) coexist with human-driven vehicles (HVs), ensuring road safety is of utmost importance. Existing safety assessment methods, however, are inadequate for the complex scenarios presented by mixed traffic conditions. These methods often fail to distinguish sufficiently between AVs and HVs, leading to inaccuracies in safety evaluations. To address these issues, this paper highlights the shortcomings of current surrogate safety measures (SSMs) in mixed traffic contexts and introduces a novel SSM, the Weighted Combination of Spacing and Speed Difference Rates (WS2DR). We propose a comparative analysis method to validate the effectiveness of WS2DR and to establish its safety threshold. Experiment results reveal that WS2DR outperforms traditional metrics such as time-to-collision and deceleration rate to avoid crashes, in terms of adaptability to both homogeneous and heterogeneous traffic environments and the detection of risk levels across a wider range of traffic conditions. Additionally, the paper presents a sophisticated mixed traffic modeling approach that accounts for different characteristics of AVs and HVs, incorporating factors such as errors of estimating the motion of other vehicles and the extended reaction time of HVs, as well as the perceptual and cooperative-active control capabilities of AVs. The results of the comparison analysis underscore the critical importance of considering the differences between AVs and HVs in modeling for accurate safety evaluations of mixed traffic. Simulation experiments confirm the positive impact on safety with increased AV penetration rates, emphasizing the necessity of employing refined modeling and safety assessment metrics to capture the full benefits of AV integration.

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加强混合交通安全评估:结合综合行为建模框架的新型安全指标。
在未来的交通系统中,自动驾驶车辆(AV)与人类驾驶车辆(HV)并存,确保道路安全至关重要。然而,现有的安全评估方法不足以应对混合交通条件下的复杂场景。这些方法往往无法充分区分 AV 和 HV,导致安全评估不准确。为解决这些问题,本文强调了混合交通环境下当前替代安全措施(SSM)的不足,并介绍了一种新型 SSM--间距和速度差率加权组合(WS2DR)。我们提出了一种比较分析方法来验证 WS2DR 的有效性并确定其安全阈值。实验结果表明,WS2DR 在对同质和异质交通环境的适应性以及在更广泛的交通条件下对风险水平的检测方面,优于传统的碰撞时间和避免碰撞的减速率等指标。此外,论文还提出了一种复杂的混合交通建模方法,该方法考虑到了 AV 和 HV 的不同特性,纳入了其他车辆运动估计误差和 HV 反应时间延长等因素,以及 AV 的感知和合作-主动控制能力。对比分析的结果突出表明,在建模中考虑 AV 和 HV 之间的差异对于准确评估混合交通安全至关重要。模拟实验证实,随着自动驾驶汽车渗透率的提高,其对安全产生了积极影响,这强调了采用精细建模和安全评估指标的必要性,以获取自动驾驶汽车集成的全部益处。
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来源期刊
CiteScore
11.90
自引率
16.90%
发文量
264
审稿时长
48 days
期刊介绍: Accident Analysis & Prevention provides wide coverage of the general areas relating to accidental injury and damage, including the pre-injury and immediate post-injury phases. Published papers deal with medical, legal, economic, educational, behavioral, theoretical or empirical aspects of transportation accidents, as well as with accidents at other sites. Selected topics within the scope of the Journal may include: studies of human, environmental and vehicular factors influencing the occurrence, type and severity of accidents and injury; the design, implementation and evaluation of countermeasures; biomechanics of impact and human tolerance limits to injury; modelling and statistical analysis of accident data; policy, planning and decision-making in safety.
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