Autonomous Vehicle Collision Risk Determination Using Yaw Rate and Friction
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Solution Overview
Problem
Autonomous vehicles lack the ability to determine risk situations where collisions with preceding vehicles are unavoidable by either turning or braking, as existing technologies assume collisions can be prevented through braking, leading to potential accidents.
Innovation Solution
A method that includes recognizing a preceding vehicle and measuring variables like distance and relative speed, determining if collisions can be avoided by turning or braking, and calculating yaw rate and distance requirements based on road friction and vehicle states to assess the feasibility of collision avoidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the autonomous vehicle uses existing emergency braking determination technology that assumes collisions can be prevented by braking, then the system can determine emergency braking situations based on obstacle detection, but the system fails to identify situations where collisions are unavoidable by either turning or braking
Solution Approach 1:
The risk situation determination unit divides the collision avoidance assessment into separate evaluation modules: one for turning capability (comparing γreq with γmax) and another for braking capability (comparing Sreq with Sbrk). This segmentation allows the system to independently evaluate each avoidance strategy and identify situations where both fail, improving reliability without excessive complexity.
Solution Approach 2:
The system transitions from a single-dimensional braking-only assessment to a multi-dimensional evaluation that includes both turning (yaw rate γ) and braking (stopping distance S) capabilities. By adding the turning dimension with yaw rate requirements and limits, the system comprehensively assesses collision avoidance possibilities that were previously undetectable.
2Measurement precision
If the autonomous vehicle calculates yaw rate requirements and compares with yaw rate limits based on road friction, then the system can determine turning capability for collision avoidance, but the calculation complexity increases
Solution Approach 1:
The system uses parameter changes in road friction coefficient (μ) to dynamically adjust the yaw rate limit (γmax) calculation. By incorporating friction-based parameter adjustments, the system achieves precise turning capability assessment that adapts to road conditions, maintaining measurement precision while managing calculation complexity through established physical relationships.
3Measurement precision
If the autonomous vehicle calculates braking distance requirements based on relative speed and friction coefficient, then the system can determine braking capability for collision avoidance, but the calculation and determination process becomes more complex
Solution Approach 1:
The system dynamically calculates braking distance requirements (Sreq) by incorporating variable parameters such as relative speed (ΔV), friction coefficient (μ), and gravitational acceleration (g). This parameter-based approach enables precise braking capability assessment that adapts to real-time conditions, achieving high measurement precision while managing complexity through standardized physical calculations.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This method effectively identifies situations where collisions are unavoidable, reducing accident risk and improving determination accuracy by considering road conditions, thereby enhancing driving stability and safety.
Implementation Method 1
recognizing, by a sensor unit, a preceding vehicle located in front of a traveling vehicle, and measuring a variable factor including a distance or a relative speed
Implementation Method 2
calculating, by a calculating unit, the γmax based on a frictional coefficient of the road surface, on which the traveling vehicle is traveling
Data Source
AI summary
A method of determining a risk situation of a collision of an autonomous vehicle is provided. The method includes recognizing a preceding vehicle and measuring a variable factor including a distance or a relative speed between a traveling vehicle and the preceding vehicle. Whether a collision is capable of being avoided by turning or braking is determined based on the variable factor.