Autonomous Race Car Feedback Control for Lap Time Optimization
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Solution Overview
Problem
Autonomous race cars lack the driving expertise of human drivers to make efficient and quick decisions for achieving the fastest lap times due to the absence of human intervention, necessitating systems and methods to optimize performance in real-time.
Innovation Solution
A system and method that utilizes a performance optimization module to detect errors between control commands and their execution, learn additional information about the race car and environment, and generate corrective actions to improve lap times by adjusting parameters in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If autonomous race car uses automated control system, then driving speed and lap time are improved, but driving decision-making capability deteriorates due to absence of human expertise
Solution Approach 1:
The system continuously monitors real-time parameter values from sensors during the race event and feeds this information back to the controller unit. The controller compares actual performance with expected performance and automatically adjusts control parameters to optimize lap time, replacing human driver feedback loops with automated sensor-controller feedback mechanisms.
Solution Approach 2:
The autonomous race car system performs self-optimization by automatically detecting errors between control commands and execution, learning from the data, and generating corrective actions without human intervention. The system serves itself by autonomously adjusting its control parameters based on real-time performance monitoring and error detection.
2Loss of time
If real-time parameter monitoring and adjustment is implemented, then lap time optimization is improved, but system complexity increases
Solution Approach 1:
The controller unit performs multiple functions: it receives sensor data, detects errors between commands and execution, learns from the data, generates corrective actions, and adjusts control parameters. This multi-functional approach consolidates what could be separate complex systems into a single integrated control unit, reducing overall system complexity while maintaining real-time optimization capabilities.
Solution Approach 2:
The performance optimization module is nested within the controller unit, which itself is part of the autonomous race car system. This hierarchical nesting allows the complex optimization functions to be organized in layers, with the innermost optimization algorithms contained within the control unit, making the overall system more manageable and less complex.
3Measurement precision
If error detection and corrective action generation is implemented, then driving precision is improved, but computational load increases
Solution Approach 1:
The system detects errors between control commands and actual execution, but only generates corrective actions when errors are detected. This partial action approach avoids continuous full-scale computational optimization, reducing computational load while maintaining driving precision through targeted error correction rather than constant recalculation.
Data Source
AI summary
A system includes a performance optimization module of an autonomous vehicle configured to: (1) receive a first and second set of real-time parameter values of the autonomous vehicle; (2) identify instability of the autonomous vehicle in response to detecting one or more errors between a control command given by a controller unit to the autonomous vehicle and an execution of the control command by the autonomous vehicle based on the first set of real-time parameter values and the second set of real-time parameter values; (3) generate additional information associated with the autonomous vehicle and an environment in which the autonomous vehicle is driving based on the one or more errors; generate a corrective course of action for reducing a duration needed to drive a given route by the autonomous vehicle; and feed back the additional information and the corrective course of action to the controller unit for execution.