CAV driving safety monitoring and warning via V2X-based edge computing system

doi:10.1007/s42524-023-0293-x

Front. Eng

2024, Vol. 11

Issue (1) : 107-127 https://doi.org/10.1007/s42524-023-0293-x

CAV driving safety monitoring and warning via V2X-based edge computing system

Cheng CHANG¹, Jiawei ZHANG¹, Kunpeng ZHANG², Yichen ZHENG³, Mengkai SHI³, Jianming HU¹, Shen LI⁴(

), Li LI¹(

)

¹. Department of Automation, Tsinghua University, Beijing 100084, China
². Department of Automation, Tsinghua University, Beijing 100084, China; College of Electrical Engineering, Henan University of Technology, Zhengzhou 450001, China
³. Nebula Link Technology Co., Ltd., Beijing 100080, China
⁴. Department of Civil Engineering, Tsinghua University, Beijing 100084, China

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Abstract

Driving safety and accident prevention are attracting increasing global interest. Current safety monitoring systems often face challenges such as limited spatiotemporal coverage and accuracy, leading to delays in alerting drivers about potential hazards. This study explores the use of edge computing for monitoring vehicle motion and issuing accident warnings, such as lane departures and vehicle collisions. Unlike traditional systems that depend on data from single vehicles, the cooperative vehicle-infrastructure system collects data directly from connected and automated vehicles (CAVs) via vehicle-to-everything communication. This approach facilitates a comprehensive assessment of each vehicle’s risk. We propose algorithms and specific data structures for evaluating accident risks associated with different CAVs. Furthermore, we examine the prerequisites for data accuracy and transmission delay to enhance the safety of CAV driving. The efficacy of this framework is validated through both simulated and real-world road tests, proving its utility in diverse driving conditions.

Keywords driving safety accident prevention connected and automated vehicles edge computing

Corresponding Author(s): Shen LI,Li LI

Just Accepted Date: 17 January 2024 Online First Date: 26 February 2024 Issue Date: 13 March 2024

Cite this article:

Cheng CHANG,Jiawei ZHANG,Kunpeng ZHANG, et al. CAV driving safety monitoring and warning via V2X-based edge computing system[J]. Front. Eng, 2024, 11(1): 107-127.

URL:

https://academic.hep.com.cn/fem/EN/10.1007/s42524-023-0293-x
https://academic.hep.com.cn/fem/EN/Y2024/V11/I1/107

Fig.1 The illustration for data flow in the framework.

Tab.1 Data fields contained in packages from CAV to RSU

Fig.2 The types of road environment data.

Fig.3 Illustration of Bezier curve prediction method.

Fig.4 An attention-based model for trajectory prediction.

Fig.5 Accident detection with predicted positions.

Fig.6 Examples of accident scenes in our experiment.

Fig.7 A visual illustration of system timer parameters.

Fig.8 Variation of warning TPR and FPR metrics with check time interval and prediction time interval parameters (the check time intervals are annotated near the data points, and the left and right plots share the same legend).

Tab.2 Average processing time (ms) of each sub-module for per 1 s data stream

Fig.9 TPR changes with noises of different deviations for warnings in different scenario datasets (the σ standard deviation scales are different in the first and second prediction algorithms).

Tab.3 Comparison for lane/road widths in different road environment

Fig.10 Distributions for time delay margin of correctly-triggered dynamic warnings.

Tab.4 Comparison for different prediction methods

Fig.11 Error distributions of different trajectory prediction algorithms over time.

Fig.12 Sensing range comparison for single vehicle and CAV.

Tab.5 Warning performances comparison for CAVs and single vehicles

Fig.13 (a) The distribution about whether single vehicles trigger timely warning for dynamic objects; (b) indicators comparisons for the positive and negative warning cases.

Fig.14 A typical example of risky warnings by CAVs and single vehicles.

Fig.15 The devices that support real-world vehicle testing.

Fig.16 The scenarios that under real-world vehicle testing.

Fig.17 The motion process and spatial temporal trajectories of the two vehicles.

Fig.18 The warning signals that transmit to the vehicle.

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