Vehicle Control Intervention Training for Specific Driving Maneuvers
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Solution Overview
Problem
Current test methods for vehicle dynamics control systems in motor vehicles are largely manual and cannot be fully automated, making them error-prone and resource-intensive, especially when evaluating driver interventions during specific driving maneuvers.
Innovation Solution
A method and system that utilize a machine learning algorithm to automatically detect and evaluate driver interventions, such as changes in steering angle, vehicle acceleration, and yaw rate, allowing for the training of algorithms to specify interventions in the control system during specific driving maneuvers, thereby enabling automated evaluation of control system behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual test methods are used to evaluate driver interventions during specific driving maneuvers, then the evaluation can capture complex driver behavior, but the testing process becomes error-prone and resource-intensive
Solution Approach 1:
The patent replaces manual mechanical testing processes with an automated machine learning-based system. The machine learning algorithm automatically detects driving maneuvers and evaluates driver interventions by processing sensor data (steering angle, acceleration, yaw rate), eliminating the need for manual observation and recording while maintaining evaluation accuracy.
Solution Approach 2:
The machine learning algorithm is trained on historical driving data to autonomously identify specific driving maneuvers and evaluate driver interventions without human intervention. The system self-calibrates and improves through continuous learning from training data, reducing the need for elaborate test protocols and manual assessment.
2Extent of automation
If manual test methods are used for evaluating control system behavior, then detailed driver behavior can be recorded, but full automation cannot be achieved
Solution Approach 1:
The patent replaces manual testing operations with automated machine learning algorithms that process vehicle sensor data to detect driving maneuvers and evaluate driver interventions. This substitution enables full automation while ensuring consistent, repeatable testing across different vehicles and conditions, eliminating human variability.
Solution Approach 2:
The machine learning algorithm incorporates feedback mechanisms where test results and evaluation outcomes are used to continuously improve the algorithm's performance. The system learns from each test case, refining its ability to detect maneuvers and evaluate interventions, thereby enhancing both automation capability and reliability over time.
3Measurement precision
If elaborate test methods and systems are developed for each vehicle type, then accurate evaluation of control system behavior can be achieved, but resource consumption increases
Solution Approach 1:
The patent develops a universal machine learning algorithm that can evaluate control system behavior across different vehicle types without requiring separate elaborate test methods for each vehicle. The algorithm processes standardized sensor data (steering angle, acceleration, yaw rate) from various vehicles, enabling accurate evaluation while reducing resource consumption through reusability.
Solution Approach 2:
The machine learning algorithm creates a virtual model of driver behavior and vehicle response that can be replicated across different vehicle types. Instead of developing physical test systems for each vehicle, the algorithm copies and adapts its evaluation framework to different vehicles, maintaining accuracy while significantly reducing resource requirements.
Data Source
AI summary
A method for training at least one machine learning algorithm used to output specifications for interventions in the control system of a motor vehicle during specific driving maneuvers is disclosed. The method includes (i) during an operation of the motor vehicle, determining whether at least one specific driving maneuver is being performed; (ii) in the event that a specific driving maneuver is being performed, detecting interventions by a driver of the motor vehicle in the control system of the motor vehicle during the performance of the driving maneuver; and (iii) training the at least one machine learning algorithm based on the specific driving maneuver and the detected interventions by the driver of the motor vehicle in the control system of the motor vehicle.