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Risk perception and the warning strategy based on safety potential field theory
Linheng Li1, Jing Gan1, Ziwei Yi1
1Jiangsu Key Laboratory of Urban ITS, School of Transportation, Southeast University, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University Road #2, Nanjing, 211189, China.
Accident; Analysis and Prevention
|October 29, 2020
Summary
This study introduces a new potential field indicator (PFI) for connected and automated vehicles (CAVs). PFI enhances driving safety by accurately assessing risks in complex traffic environments.
Area of Science:
- Intelligent Transportation Systems
- Vehicle Safety Dynamics
Background:
- Connected and automated vehicles (CAVs) generate vast amounts of traffic data.
- Existing safety indicators struggle to integrate diverse traffic information for comprehensive risk assessment in CAVs.
Purpose of the Study:
- To develop a novel methodology for risk perception and warning strategies in CAVs.
- To introduce a new driving risk indicator that improves safety evaluations.
Main Methods:
- Constructed a dynamic safety potential field model to map driving risk spatially.
- Developed the potential field indicator (PFI) to quantify driving risk levels.
- Implemented and evaluated an early warning strategy using the SUMO simulator.
Main Results:
- The proposed safety potential field model comprehensively integrates various traffic information.
- The PFI accurately reflects driving risk across different vehicle motion states.
- Simulations demonstrated PFI's superior performance compared to classic risk indicators.
Conclusions:
- The PFI is a more suitable indicator for driving risk assessment in CAV environments.
- The findings can enhance strategic decision-making in driver assistance systems for CAVs.
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