Vehicle Control With Delayed State Simulation for Route Alignment
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Solution Overview
Problem
Vehicles often deviate from the expected driving route due to varying road conditions such as slope, inclination, and curvature, necessitating a system to maintain control and alignment.
Innovation Solution
A vehicle control system incorporating a vehicle state estimator, delay simulator, and error compensation optimizer to generate and adjust driving parameters based on estimated future vehicle states and road conditions, ensuring alignment with target states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If vehicle control system uses traditional state estimation without delay simulation, then computational complexity is reduced, but vehicle control accuracy and stability deteriorate on variable road surfaces
Solution Approach 1:
The delay simulator performs preliminary simulation of delayed vehicle states before actual control execution. By pre-calculating what the vehicle state will be after a certain delay period, the system prepares compensation values in advance, allowing the actual control to be more accurate when executed.
Solution Approach 2:
The system dynamically adjusts the delay time parameter based on vehicle speed and road conditions. The delay simulator continuously adapts the simulation parameters to match current driving conditions, making the control system both accurate and computationally efficient by using appropriate delay ranges (0.1-2 seconds).
2Manufacturing precision
If vehicle control system implements error compensation optimization, then driving route alignment is improved, but computational load increases
Solution Approach 1:
The error compensation optimizer continuously compares the simulated delayed vehicle state with the target vehicle state and generates compensation values based on the difference. This feedback mechanism systematically reduces alignment errors while maintaining computational efficiency through iterative optimization.
Solution Approach 2:
The system optimizes compensation parameters such as delay time (0.1-2 seconds) and compensation magnitude based on vehicle speed and road conditions. By dynamically adjusting these parameters, the system achieves high alignment precision without excessive computational energy consumption.
3Measurement precision
If vehicle control system uses longer delay time for simulation, then future state prediction accuracy is improved, but real-time control responsiveness deteriorates
Solution Approach 1:
The delay time parameter is dynamically adjusted based on vehicle speed and road conditions rather than using a fixed value. At higher speeds or on more variable road surfaces, longer delay times (up to 2 seconds) improve prediction accuracy, while at lower speeds, shorter delays maintain responsiveness.
Solution Approach 2:
The system changes the delay time parameter within an optimized range (0.1-2 seconds) based on driving conditions. This parameter optimization allows the system to achieve the best balance between prediction accuracy and response speed for each specific situation.
Data Source
AI summary
A vehicle control system includes a vehicle state estimator, a delay simulator and an error compensation optimizer. The vehicle state estimator is configured to generate an estimated future vehicle state at a future time point. The delay simulator is configured to determine a delay time based on the estimated future vehicle state and a current vehicle state, and obtain a delayed future vehicle state based on the delay time. The error compensation optimizer is configured to generate a driving parameter estimation compensation to the vehicle state estimator based on a difference between the delayed future vehicle state and a target vehicle state being outside the error range.