Autonomous Vehicle Yaw Rate Control With Adaptive Steering Correction
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Solution Overview
Problem
Existing methods for controlling autonomous vehicles are inaccurate in complex driving scenarios due to changes in vehicle load and tire characteristics, limiting their ability to maintain desired yaw rates on non-flat roads.
Innovation Solution
A control method that corrects the steering wheel angle using real-time deviation coefficients and a closed-loop algorithm to adjust the target steering wheel angle, ensuring the vehicle's yaw rate aligns with the desired rate, improving lateral control accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If lateral dynamic modeling is performed based on fixed mass center location and standard tire data, then the control system is simple to implement, but the lateral control accuracy deteriorates in complex driving scenarios
Solution Approach 1:
The patent applies dynamics by making the mass center location and tire characteristics adjustable and adaptive rather than fixed. The system dynamically updates the mass center position based on cargo distribution detection and adapts tire characteristics based on detected driving scenarios (flat road, uphill, downhill, etc.), allowing the control model to reflect real-time vehicle conditions and maintain accuracy across varying operating conditions.
Solution Approach 2:
The patent changes key parameters including mass center location coordinates, tire lateral stiffness, and other vehicle dynamic parameters based on detected driving conditions. By adjusting these parameters according to the actual scenario (e.g., different tire characteristics for uphill vs. flat road), the system maintains accurate lateral control without requiring a completely complex reconfiguration of the control architecture.
2Ease of manufacture
If the vehicle model uses fixed parameters for mass center and tire characteristics, then the model is easy to establish, but the yaw rate control accuracy deteriorates when vehicle load changes
Solution Approach 1:
The system performs preliminary detection of cargo distribution and vehicle load conditions before executing lateral control. By提前 (in advance) identifying the mass center location and adjusting the model parameters according to the detected conditions, the system ensures accurate yaw rate control from the outset rather than requiring complex real-time adjustments during critical control moments.
Solution Approach 2:
The patent implements feedback by continuously monitoring driving scenarios and vehicle conditions, then using this information to update the mass center location and tire characteristics in the vehicle model. This closed-loop approach allows the model to adapt to changing load conditions while maintaining ease of establishment through standardized update procedures.
3Ease of operation
If the control system uses standard tire characteristics, then the system is simple to implement, but the lateral control accuracy deteriorates in different turning scenarios
Solution Approach 1:
The patent makes the tire characteristics dynamic by detecting the current driving scenario (flat road, uphill, downhill, left turn, right turn, etc.) and selecting or adjusting appropriate tire parameters for each scenario. This allows the system to maintain simple implementation through automated scenario detection while achieving high accuracy by using scenario-specific tire characteristics.
Data Source
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AI summary
The present disclosure provides a control method and apparatus for an autonomous vehicle, a computer device and a storage medium. The current steering wheel angle, vehicle speed and yaw rate are obtained, the current steering wheel angle is corrected based on the first correction deviation coefficient and the second correction deviation coefficient of the previous cycle, the corrected steering wheel angle and the current vehicle speed are input into the preset vehicle dynamic model to obtain the estimated yaw rate, the first yaw rate deviation value between the current yaw rate and the estimated yaw rate is obtained, and processed by the preset closed-loop algorithm to obtain the first correction deviation coefficient and the second correction deviation coefficient of the current cycle, and the target steering wheel angle is corrected, and the vehicle is driven based on the corrected target steering wheel angle.