Self-Driving Vehicle Yaw Rate Control With Dynamic Model Correction
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Solution Overview
Problem
Automated driving vehicles face instability due to inadequacies between vehicle model responses and real behavior, particularly in sudden and unpredictable driving conditions, leading to potential loss of control and safety risks.
Innovation Solution
A vehicle control system with a module for dynamic compensation of vehicle model parameters and a safety module that detects driving degradation by comparing estimated and measured angular speeds, and linearized lateral forces, triggering safety measures when thresholds are exceeded.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If a vehicle model is used to estimate yaw rate for automated driving control, then the control system can function in automated mode, but the model may become invalid in sudden unpredictable conditions leading to loss of control
Solution Approach 1:
The system continuously compares the estimated yaw rate from the vehicle model with the actual measured yaw rate from sensors. When the discrepancy exceeds a threshold, indicating model invalidation, the system automatically transitions from automated to manual control mode, preventing unreliable automated control actions.
Solution Approach 2:
The system proactively monitors the validity of the vehicle model before critical failures occur. By continuously validating model predictions against actual sensor data and preparing for mode transition in advance, the system prevents situations where the automated control would act on invalid model information.
2Measurement precision
If the vehicle model is corrected based on difference between estimated and measured yaw rate, then the model accuracy improves, but the system complexity increases
Solution Approach 1:
The system uses feedback from the difference between estimated and measured yaw rates to dynamically correct the vehicle model parameters. This continuous adaptation improves estimation accuracy while keeping the correction mechanism relatively simple by only adjusting parameters when discrepancies are detected.
Solution Approach 2:
The vehicle model parameters are made dynamic rather than static, allowing them to be corrected in real-time based on actual vehicle behavior. This enables the model to adapt to changing driving conditions without requiring a completely complex reconfiguration system.
Data Source
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AI summary
Control device for a motor vehicle, comprising controllers (3, 8) of the longitudinal and lateral movement of the vehicle that are able to generate command signals for actuators, so that the vehicle follows a planned route in an assisted-driving or automated-driving mode, the output from the lateral controller being based on minimizing the error between a desired angular yaw rate, obtained from the curvature of the road on the planned route, and a current angular yaw rate of the vehicle, estimated by a model of the dynamic behaviour of the vehicle on the basis of the measured longitudinal speed and the measured steering angle, said device comprising a module (11) able dynamically to correct parameters of the model in the event of a shift with the actual dynamic behaviour of the vehicle, as a function of a comparison between the estimated value and a measured value of the angular yaw rate.