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车辆速度对安全预警算法影响的研究——以变道预警系统为例。

Research on the Influence of Vehicle Speed on Safety Warning Algorithm: A Lane Change Warning System Case Study.

机构信息

School of Automobile, Chang'an University, Xi'an 710064, China.

出版信息

Sensors (Basel). 2020 May 8;20(9):2683. doi: 10.3390/s20092683.

DOI:10.3390/s20092683
PMID:32397216
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7249093/
Abstract

Speed has an important impact on driving safety, however, this factor is not included in existing safety warning algorithms. This study uses lane change systems to study the influence of vehicle speed on safety warning algorithms, aiming to determine lane change warning rules for different speeds (DS-LCW). Thirty-five drivers are recruited to carry out an extreme trial and naturalistic driving experiment. The vehicle speed, relative speed, relative distance, and minimum safety deceleration (MSD) related to lane change characteristics are then analyzed and calculated as warning rule characterization parameters. Lane change warning rules for a rear vehicle in the target lane under four-speed levels of 60 ≤ < 70 km/h, 70 ≤ < 80 km/h, 80 ≤ < 90 km/h, and ≥ 90 km/h are established. The accuracy of lane change warning rules not considering speed level (NDS-LCW) and ISO 17387 are found to be 87.5% and 79.8%, respectively. Comparatively, the accuracy rate of DS-LCW under four-speed levels is 94.6%, 93.8%, 90.0%, and 92.6%, respectively, which is significantly superior. The algorithm proposed in this paper provides warning in the lane change process with a smaller relative distance, and the accuracy rate of DS-LCW is significantly superior to NDS-LCW and ISO 17387.

摘要

速度对驾驶安全有重要影响,但这一因素并未包含在现有的安全预警算法中。本研究使用变道系统研究了车辆速度对安全预警算法的影响,旨在确定不同速度(DS-LCW)下的变道预警规则。招募了 35 名驾驶员进行极限试验和自然驾驶实验。然后分析和计算与变道特征相关的车辆速度、相对速度、相对距离和最小安全减速(MSD),作为预警规则特征参数。建立了目标车道内后车在四个速度等级 60 ≤ < 70km/h、70 ≤ < 80km/h、80 ≤ < 90km/h 和 ≥ 90km/h 下的变道预警规则。发现不考虑速度等级的变道预警规则(NDS-LCW)和 ISO 17387 的准确率分别为 87.5%和 79.8%。相比之下,四个速度等级下 DS-LCW 的准确率分别为 94.6%、93.8%、90.0%和 92.6%,明显更高。本文提出的算法在变道过程中提供了更小的相对距离的预警,并且 DS-LCW 的准确率明显优于 NDS-LCW 和 ISO 17387。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e90/7249093/fc8206008dde/sensors-20-02683-g011.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e90/7249093/9867f3acbefb/sensors-20-02683-g007.jpg
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本文引用的文献

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Human-Like Lane Change Decision Model for Autonomous Vehicles that Considers the Risk Perception of Drivers in Mixed Traffic.面向混合交通中驾驶员风险感知的自主车辆类人变道决策模型。
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Lane change warning threshold based on driver perception characteristics.
基于驾驶员感知特性的变道警告阈值。
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