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Traffic Risk Environment Impact Analysis and Complexity Assessment of Autonomous Vehicles Based on the Potential
Ying Cheng1, Zhen Liu2, Li Gao3
1School of Automobile and Transportation, Tianjin University of Technology and Education, Tianjin 300222, China.
Summary
This study quantifies traffic environment complexity for autonomous vehicles. A new model using potential field theory and AHP provides reliable risk assessment for safer self-driving.
Area of Science:
- Engineering
- Computer Science
- Robotics
Background:
- Autonomous vehicles promise improved traffic safety and efficiency.
- Ensuring autonomous vehicle safety in complex environments remains a significant challenge.
Purpose of the Study:
- To develop a method for quantifying traffic environment complexity and potential risk factors for autonomous driving systems.
- To assess the influence of environmental element depth and breadth on autonomous driving.
Main Methods:
- Utilized potential field theory to model the impact of environmental elements on autonomous driving systems.
- Employed the Analytic Hierarchy Process (AHP) to quantify the equivalent virtual electric quantity of each environmental element.
- Developed a comprehensive approach considering physical attributes and state parameters of environmental elements.
Main Results:
- Static element complexity depends on obstacle's physical attributes and shape.
- Dynamic element complexity is influenced by obstacle and autonomous vehicle movement.
- Overall traffic environment complexity is primarily determined by dynamic elements.
Conclusions:
- The proposed method offers a comprehensive and reliable quantitative evaluation of traffic environment complexity.
- Experimental validation shows high consistency and low error compared to existing methods.
- Provides theoretical support for autonomous vehicle safety and risk assessment.
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