A nonlinear mixed logit model of occupant severity in autonomous vehicle crashes

IF 12.5 1区 工程技术 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH
Lan Ventura , Rohan Shrestha , Narayan Venkataraman , Venkataraman Shankar , Nardos Feknssa
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引用次数: 0

Abstract

This paper presents a nonlinear mixed logit to capture heterogeneous effects of contributing factors on autonomous involved occupant severity. Autonomous level information to this point has been quite sparse in the context of real-world crash scenarios and police reporting. However, the Texas Department of Transportation (TxDOT) began reporting autonomous involvement in April of 2023. With reporting still in its early stages, this analysis incorporated three distinct vehicle technologies: non-autonomous internal combustion engine (ICE) vehicles; ICE and hybrid electric autonomous vehicles; and fully electric autonomous vehicles. Crash data included any crash in Texas from April to December of 2023 that involved at least one autonomous-indicated vehicle (either the second or third distinct vehicle technology). Random parameters were found with respect to: an indicator for occupant involvement in the first harmful crash sequence event, with that event being collision with a fixed object, for no injury; proportion of autonomous vehicles for no injury; an intersection related indicator for possible injury; total occupant count for possible injury; and total vehicle count for injury. The count and proportion variables were expressed as nonlinear relationships, for which random parameters improved prediction accuracy by 37.50 % and 30.00 %, respectively, for possible injury and injury outcomes, as compared to fixed parameters. The findings in this study highlight the applicability of the nonlinear mixed logit for severity analysis with respect to complex autonomous interactions in crashes.

自动驾驶汽车碰撞事故中乘员严重程度的非线性混合对数模型
本文提出了一种非线性混合对数法,以捕捉各种因素对自主参与乘员严重程度的不同影响。到目前为止,在真实世界的碰撞场景和警方报告中,自主水平的信息还相当稀少。不过,德克萨斯州交通部(TxDOT)已于 2023 年 4 月开始报告自动驾驶事故。由于报告仍处于早期阶段,本次分析纳入了三种不同的车辆技术:非自主内燃机 (ICE) 车辆、内燃机和混合动力电动自主车辆以及全电动自主车辆。碰撞数据包括 2023 年 4 月至 12 月在德克萨斯州发生的任何碰撞事故,其中至少涉及一辆自动驾驶车辆(第二种或第三种不同的车辆技术)。在以下方面找到了随机参数:乘员参与第一个有害碰撞序列事件(该事件为与固定物体碰撞)的指标(无伤害);自主车辆比例(无伤害);交叉路口相关指标(可能伤害);乘员总数(可能伤害);车辆总数(伤害)。计数和比例变量表现为非线性关系,与固定参数相比,随机参数对可能受伤和受伤结果的预测准确率分别提高了 37.50 % 和 30.00 %。本研究的结果凸显了非线性混合对数法在车祸中复杂的自主交互作用严重性分析中的适用性。
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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来源期刊
CiteScore
22.10
自引率
34.10%
发文量
35
审稿时长
24 days
期刊介绍: Analytic Methods in Accident Research is a journal that publishes articles related to the development and application of advanced statistical and econometric methods in studying vehicle crashes and other accidents. The journal aims to demonstrate how these innovative approaches can provide new insights into the factors influencing the occurrence and severity of accidents, thereby offering guidance for implementing appropriate preventive measures. While the journal primarily focuses on the analytic approach, it also accepts articles covering various aspects of transportation safety (such as road, pedestrian, air, rail, and water safety), construction safety, and other areas where human behavior, machine failures, or system failures lead to property damage or bodily harm.
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