Vehicle Driving Control After Virtual Driving Behavior Shift
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Solution Overview
Problem
Driving safety is compromised due to the influence of virtual driving experiences, leading to rough driving operations after transitioning to actual vehicle driving.
Innovation Solution
A vehicle system that includes sensors for accelerator, brake, and steering operations, coupled with a virtual driving apparatus and an abnormal driving determination apparatus, uses Mahalanobis distance analysis to detect abnormal driving based on pre- and post-virtual driving factor changes, and adjusts engine torque and steering control to mitigate safety risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If virtual driving is performed using driving simulator equipment, then driving training and evaluation capabilities are improved, but driving safety deteriorates due to rough operations after transitioning to actual driving
Solution Approach 1:
The system determines abnormal driving based on the distance from the center position of the population, providing feedback when driving behavior deviates from normal patterns. This feedback mechanism allows the system to identify and alert operators whose driving operations have become rough or abnormal after virtual driving, thereby addressing the safety deterioration issue while maintaining the training and evaluation functionality
Solution Approach 2:
The system obtains numerical values related to multiple factors in actual driving prior to execution of virtual driving and forms a population baseline. By establishing this preliminary baseline of normal driving behavior before virtual driving begins, the system can later compare post-virtual driving behavior against this pre-established standard to detect abnormalities, thus preventing safety issues before they manifest in actual driving
2Measurement precision
If driving behavior is monitored using multiple sensors and complex analysis, then detection accuracy is improved, but device complexity increases
Solution Approach 1:
The system replaces complex mechanical monitoring and analysis mechanisms with statistical computation. Instead of using complex physical sensors and mechanical analysis systems, the invention uses numerical value collection and Mahalanobis distance calculation to detect abnormal driving, achieving high detection accuracy through computational methods rather than mechanical complexity
Solution Approach 2:
The system changes the approach from monitoring individual driving parameters separately to analyzing multiple factors simultaneously as a population of numerical values. By transforming individual sensor readings into a multivariate statistical population and using Mahalanobis distance to measure deviation from the center position, the system achieves comprehensive detection accuracy while maintaining relatively simple implementation through parameter transformation rather than complex device architecture
Data Source
AI summary
A vehicle includes one or more processors, and one or more memories coupled to the one or more processors. The one or more processors cooperate with a program included in the one or more memories to execute a process, the process including: obtaining numerical values related to multiple factors in actual driving prior to an execution of virtual driving not involving actual vehicle movement, and forming a population; detecting the execution of the virtual driving; and determining abnormal driving based on a distance from a center position of the population of the numerical values obtained in the actual driving after the execution of the virtual driving.