基于自然车辆轨迹的改进变道风险估计模型

IF 2.4 3区 工程技术 Q3 TRANSPORTATION
Qingwen Xue, Ke Wang, J. Lu, Yingying Xing, Xin Gu, Meng Zhang
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引用次数: 1

摘要

摘要变道行为与周围车辆相互作用复杂,对交通流和交通安全具有重要影响。为了确保安全变道,防止潜在的碰撞,实时识别变道的潜在碰撞风险是非常重要的。本文提出了一种改进的风险估计(IRE)模型来评估变道车辆群的潜在碰撞风险。引入安全裕度,考虑车辆的减速能力,衡量驾驶员在减速过程中的反应时间。然后,结合基于安全裕度测量的碰撞概率和碰撞严重程度,建立IRE模型;使用从highD数据集中提取的轨迹数据,并对1536个LC样本进行了研究。我们比较了不同背景因素下的LCR,包括车辆类型(轿车和卡车)、两个变道方向(左变道和右变道、LLC和RLC)以及交通流量(低流量和高流量)。研究发现,由于制动能力有限,货车驾驶员的LCR高于轿车驾驶员,且左侧变道导致的LCR高于右侧变道。此外,与低交通流量相比,在高交通流量下,变道与更高的撞车风险有关。了解不同环境因素下变道行为的碰撞风险,可以为实时碰撞预测和制定交通管理策略提供帮助。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An improved risk estimation model of lane change using naturalistic vehicle trajectories
Abstract Lane change (LC) behavior has critical effects on traffic flows and safety due to its complex interactions with surrounding vehicles. To ensure safe lane changes and prevent potential crashes, it is important to recognize the potential crash risk of lane change in real time. This study proposes an improved risk estimation (IRE) model to evaluate the potential collision risk of lane change (LCR) vehicle groups. The safety margin is introduced to consider the deceleration capability of vehicles to measure the reaction time of drivers during the LC. Then the IRE model is established, incorporating the collision probability and collision severity measured based on the safety margin. The trajectory data, extracted from the highD dataset, are used and 1536 LC samples are investigated. We compare the LCR under different contextual factors, including vehicle types (cars and trucks), two lane change directions (left and right lane change, LLC and RLC), and traffic flows (low and high traffic). It was found that truck drivers keep higher LCR compared with car drivers due to limited brake capacity, and the left lane change results in higher LCR compared with the right lane change. Additionally, lane change is associated with higher crash risk in high traffic flow, as compared to low traffic flow. The understanding of the crash risk of lane change behavior under different contextual factors, can be useful for real-time crash prediction and devising traffic management strategies.
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来源期刊
CiteScore
6.00
自引率
15.40%
发文量
38
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