Cumulatively Anticipative Car-Following Model with Enhanced Safety for Autonomous Vehicles in Mixed Driver Environments

IF 7 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Xinyi Yang, Hafiz Usman Ahemd, Ying Huang, Pan Lu
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引用次数: 0

Abstract

The contribution of autonomous vehicles to traffic is one of the key aspects of future ground transportation in smart cities. Autonomous vehicles are able to provide many benefits, but some benefits can only provide advantages if these vehicles comprise a large percent of on the road/driven vehicles, which may take decades. Until then, the robotic drivers in autonomous vehicles will share the same road system with human divers in a mixed-driver environment where the majority of road accidents for autonomous vehicles are associated with the operational inconsistency of human drivers. In this paper, a cumulatively anticipative car-following model (which considers cumulative influences from multiple preceding vehicles) is developed to potentially improve the safety of autonomous vehicles in mixed-driver environments that benefit from enhanced communication between the autonomous vehicles and other components in the transportation system. Through intensive simulations (200 simulations), this study comprehensively evaluates four models including the cumulative anticipative car-following model, the Wiedemann 99 model, adaptive cruise control, and the cooperative adaptive cruise control model. Across 10 scenarios and five speed limits (24.59–33.53 m/s), the cumulative anticipative car-following model consistently demonstrates superior conflict reduction, with average, maximum, and minimum conflict percentages ranging from 77.69% to 91.97% against the Wiedemann 99 model, 67.00% to 93.94% against the adaptive cruise control model, and 69.17% to 93.25% against the cooperative adaptive cruise control model. Notably, the cooperative adaptive cruise control model exhibits suboptimal performance, especially in mixed-driver settings. The cumulative anticipative car-following model also enhances vehicle mobility, reducing average stops by up to 93.54%, 91.74%, 92.04%, 88.60%, and 91.35% in comparison to the other three models at speeds of 24.59 m/s, 26.82 m/s, 29.06 m/s, 31.29 m/s, and 33.53 m/s. Overall, the cumulative anticipative car-following model holds significant potential for conflict reduction and traffic enhancement.
混合驾驶环境下自动驾驶汽车安全性增强的累积预测跟车模型
自动驾驶汽车对交通的贡献是未来智慧城市地面交通的关键方面之一。自动驾驶汽车能够提供许多好处,但只有当这些车辆占道路/驾驶车辆的很大比例时,一些好处才能提供优势,这可能需要几十年的时间。在此之前,自动驾驶汽车中的机器人驾驶员将在混合驾驶环境中与人类驾驶员共享相同的道路系统,在这种环境中,自动驾驶汽车的大多数道路事故都与人类驾驶员的操作不一致有关。本文开发了一种累积预测汽车跟随模型(考虑了多辆前车的累积影响),以潜在地提高混合驾驶环境中自动驾驶汽车的安全性,从而受益于自动驾驶汽车与交通系统中其他组件之间增强的通信。通过200次密集仿真,综合评价了累积预期跟车模型、Wiedemann 99模型、自适应巡航控制和协同自适应巡航控制四种模型。在10个场景和5个速度限制(24.59 ~ 33.53 m/s)下,累积预期跟车模型始终表现出较好的冲突减少效果,与Wiedemann 99模型相比,其平均、最大和最小冲突百分比为77.69% ~ 91.97%,与自适应巡航控制模型相比为67.00% ~ 93.94%,与合作自适应巡航控制模型相比为69.17% ~ 93.25%。值得注意的是,合作自适应巡航控制模型表现出次优性能,特别是在混合驾驶员设置下。累积预期跟车模型在车速为24.59 m/s、26.82 m/s、29.06 m/s、31.29 m/s和33.53 m/s时,平均停车次数比其他三种模型分别减少了93.54%、91.74%、92.04%、88.60%和91.35%。总的来说,累积预期车辆跟随模型在减少冲突和改善交通方面具有重要的潜力。
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
Smart Cities
Smart Cities Multiple-
CiteScore
11.20
自引率
6.20%
发文量
0
审稿时长
11 weeks
期刊介绍: Smart Cities (ISSN 2624-6511) provides an advanced forum for the dissemination of information on the science and technology of smart cities, publishing reviews, regular research papers (articles) and communications in all areas of research concerning smart cities. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible, with no restriction on the maximum length of the papers published so that all experimental results can be reproduced.
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