Variable Prediction Time Driving Assist Controller
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Solution Overview
Problem
Existing driving assist controllers for vehicles face challenges in balancing rapid target course tracking ability with vehicle stability, particularly at high speeds and in varying road conditions, as they often prioritize either rapid tracking or stability over the other.
Innovation Solution
A driving assist controller that includes a target course setting unit, a vehicle trajectory estimation unit, and a forward gaze point calculation unit, which sets a variable prediction time based on the displacement between the target course and the vehicle's position, allowing for feed-forward and feedback control to adjust the steering angle and torque to maintain the vehicle on the target course.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If the prediction time is extended to improve vehicle stability, then the target course tracking ability is slowed down
Solution Approach 1:
The patent applies dynamics by making the prediction time variable rather than fixed. The control device dynamically adjusts the prediction time based on vehicle speed: using a longer prediction time at low speeds for stability, and a shorter prediction time at high speeds for rapid tracking. This dynamic adaptation resolves the contradiction between stability and tracking speed.
Solution Approach 2:
The patent changes the parameter of prediction time based on operating conditions (vehicle speed). By adjusting this key parameter according to speed thresholds, the system achieves both stability when needed and rapid tracking when needed, resolving the fundamental trade-off between these two opposing requirements.
2Speed
If the prediction time is shortened to improve target course tracking ability, then the vehicle behavior becomes unstable
Solution Approach 1:
The system dynamically adjusts prediction time based on real-time vehicle speed conditions. When speed is high, a shorter prediction time enables rapid tracking without causing instability. When speed is low, a longer prediction time restores stability. This dynamic adjustment resolves the contradiction.
Solution Approach 2:
The prediction time parameter is changed according to vehicle speed thresholds. This parameter adaptation allows the system to achieve rapid tracking at high speeds while maintaining stability at low speeds, resolving the trade-off between tracking performance and behavioral stability.
3Stability of the object's composition
If the forward gaze point is set farther from the vehicle to improve stability, then the ability to track the target course rapidly is slowed down
Solution Approach 1:
The forward gaze point distance is dynamically adjusted based on vehicle speed. At high speeds, the gaze point is set closer to enable rapid tracking. At low speeds, the gaze point is set farther away to improve stability. This dynamic positioning resolves the contradiction between stability and tracking speed.
Solution Approach 2:
The distance parameter of the forward gaze point is changed according to vehicle speed conditions. This adaptive parameter adjustment allows the system to achieve both stability (when gaze point is far) and rapid tracking (when gaze point is close), resolving the fundamental trade-off.
4Speed
If the forward gaze point is set closer to the vehicle to improve rapid tracking ability, then the vehicle behavior becomes nervous and unstable
Solution Approach 1:
The system dynamically adjusts the forward gaze point distance based on vehicle speed. When speed is high, the closer gaze point enables rapid tracking without causing instability. When speed is low, the farther gaze point maintains stability. This dynamic adjustment resolves the contradiction.
Solution Approach 2:
The forward gaze point distance parameter is adapted based on vehicle speed thresholds. This parameter change strategy enables the system to achieve rapid tracking at high speeds while maintaining stability at low speeds, resolving the trade-off between tracking performance and behavioral stability.
Data Source
AI summary
Feed-forward control amounts of an electric motor, which are necessary for a vehicle to travel along the target course under the feed-forward control, are calculated on the basis of the road shape. The prediction time is variably set to be shorter as the present displacement between the target course and the vehicle position becomes larger, the position after elapse of the prediction time is defined as a forward gaze point, and the feedback control amounts of the electric motor, which are necessary for the vehicle to travel along the target course under the feedback control, are calculated on the basis of the traveling state of the vehicle so as to zero the displacement between the target course and the vehicle trajectory in the forward gaze point. The electric motor current value is then calculated from the driver steering torque, feed-forward control amounts, and feedback control amounts.