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Analysis of Perception Accuracy of Roadside Millimeter-Wave Radar for Traffic Risk Assessment and Early Warning Systems

Author

Listed:
  • Cong Zhao

    (Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China)

  • Delong Ding

    (Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China)

  • Zhouyang Du

    (Shanghai Pudong Development (Group) Co., Ltd., Shanghai 201204, China)

  • Yupeng Shi

    (Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China)

  • Guimin Su

    (Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China
    Shanghai SH Intelligent Automotive Technology Co., Ltd., Shanghai 201805, China
    Shanghai SEARI Intelligent System Co., Ltd., Shanghai 200063, China)

  • Shanchuan Yu

    (National Engineering and Research Center for Mountainous Highways, China Merchants Chongqing Communications Research & Design Institute Co., Ltd., Chongqing 400067, China)

Abstract

Millimeter-wave (MMW) radar is essential in roadside traffic perception scenarios and traffic safety control. For traffic risk assessment and early warning systems, MMW radar provides real-time position and velocity measurements as a crucial source of dynamic risk information. However, due to MMW radar’s measuring principle and hardware limitations, vehicle positioning errors are unavoidable, potentially causing misperception of the vehicle motion and interaction behavior. This paper analyzes the factors influencing the MMW radar positioning accuracy that are of major concern in the application of transportation systems. An analysis of the radar measuring principle and the distributions of the radar point cloud on the vehicle body under different scenarios are provided to determine the causes of the positioning error. Qualitative analyses of the radar positioning accuracy regarding radar installation height, radar sampling frequency, vehicle location, posture, and size are performed. The analyses are verified through simulated experiments. Based on the results, a general guideline for radar data processing in traffic risk assessment and early warning systems is proposed.

Suggested Citation

  • Cong Zhao & Delong Ding & Zhouyang Du & Yupeng Shi & Guimin Su & Shanchuan Yu, 2023. "Analysis of Perception Accuracy of Roadside Millimeter-Wave Radar for Traffic Risk Assessment and Early Warning Systems," IJERPH, MDPI, vol. 20(1), pages 1-21, January.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:1:p:879-:d:1024204
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    References listed on IDEAS

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    1. Jing Chen & Cong Zhao & Shengchuan Jiang & Xinyuan Zhang & Zhongxin Li & Yuchuan Du, 2023. "Safe, Efficient, and Comfortable Autonomous Driving Based on Cooperative Vehicle Infrastructure System," IJERPH, MDPI, vol. 20(1), pages 1-18, January.
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    Cited by:

    1. Li, Chunjie & Xu, Chengcheng & Chen, Yusen & Li, Zhibin, 2024. "Development and experiment of an intelligent connected cooperative vehicle infrastructure system based on multiple V2I modes and BWM-IGR method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 635(C).
    2. Jing Chen & Cong Zhao & Shengchuan Jiang & Xinyuan Zhang & Zhongxin Li & Yuchuan Du, 2023. "Safe, Efficient, and Comfortable Autonomous Driving Based on Cooperative Vehicle Infrastructure System," IJERPH, MDPI, vol. 20(1), pages 1-18, January.
    3. Jing Cao & Haichao Ling & Tao Li & Shiyu Wang & Shengchuan Jiang & Cong Zhao, 2024. "A Graph-Based Scheme Generation Method for Variable Traffic Organization in Parking Lots," Sustainability, MDPI, vol. 16(11), pages 1-18, June.

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