Autonomous Race Car Error-Corrective Control for Dynamic Lap Time Gains
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Solution Overview
Problem
Autonomous race cars face challenges in optimizing lap times due to the absence of a human driver, who can make quick and efficient decisions based on various parameters like tire wear, road surface conditions, and weather.
Innovation Solution
A system and method that utilize a controller unit pre-fed with initial parameter values, coupled with sensors measuring real-time parameter values, and a performance optimization module that detects errors, learns additional information, and generates corrective actions to improve lap times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If autonomous race car uses automated control system, then racing speed and automation level are improved, but ability to make quick decisions based on dynamic conditions deteriorates
Solution Approach 1:
The system continuously monitors real-time parameters (tire wear, road surface conditions, weather, car state) and feeds this information back to the controller unit, which adjusts control commands dynamically. This closed-loop feedback mechanism enables the automated system to adapt to changing conditions without human intervention, resolving the contradiction between automation and adaptability.
Solution Approach 2:
The control parameters are made dynamic rather than static. The controller unit continuously updates control commands based on real-time parameter changes detected during the race. This dynamic adjustment capability allows the automated system to respond flexibly to varying race conditions, maintaining adaptability while fully automated.
2Loss of time
If autonomous race car optimizes performance in real-time, then lap time is improved, but system complexity increases
Solution Approach 1:
The control system is segmented into distinct functional modules: sensor units for parameter detection, a performance optimization module for analyzing real-time data, and a controller unit for generating control commands. This modular segmentation manages system complexity by dividing the optimization function into manageable, specialized components while maintaining real-time performance capability.
Solution Approach 2:
The performance optimization module acts as an intermediary between the sensors and the controller unit. It processes real-time parameter data, detects errors, and generates optimized control commands, thereby simplifying the overall system architecture while enabling complex real-time optimization functions.
3Productivity
If autonomous race car monitors multiple real-time parameters, then performance optimization capability is improved, but measurement and detection difficulty increases
Solution Approach 1:
The sensor system is designed with multi-functionality to detect diverse parameters (tire wear, road surface conditions, weather, car state) using integrated sensing capabilities. This universal approach consolidates multiple detection functions into a coordinated sensor network, reducing the overall difficulty of monitoring while maintaining comprehensive performance optimization capability.
Data Source
AI summary
A system and method for optimizing the performance of an autonomous race car in real-time during a race event are disclosed. An autonomous race car controller unit is pre-fed with a first set of initial parameter values and a second set of initial parameter values. A set of sensors is configured for measuring a first and a second set of real-time parameter values after the starting of the race event. A performance optimization module is configured to generate a corrective course by receiving the first and second sets of real-time parameters and detecting the presence of errors between a control command given by the controller unit and its execution.