In-Vehicle Mixed Reality Driver Training for Predicted Road Scenarios
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Solution Overview
Problem
Autonomous vehicles at level 0 require human drivers to navigate scenarios they have no experience with, such as inclement weather or animals on the road, posing safety risks due to lack of training in specific driving contexts.
Innovation Solution
An in-vehicle predicted context-based proactive driving training system that identifies experience gaps by analyzing the driver's past experiences and provides mixed reality driving simulations to prepare them for upcoming scenarios, evaluating their performance and determining their suitability to safely navigate the route.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If autonomous vehicles operate in manual driving mode without training, then ease of operation is improved, but driver safety deteriorates due to lack of experience in specific driving contexts
Solution Approach 1:
The system performs experience gap analysis and provides driving training simulations before the driver encounters the actual challenging driving context. By predicting upcoming driving contexts and proactively training the driver in advance, the system ensures the driver is prepared without interfering with manual operation convenience.
2Reliability
If driving training simulations are provided proactively based on predicted contexts, then driver safety is improved, but loss of time increases due to training interruptions
Solution Approach 1:
The system predicts driving contexts ahead of time using route information and weather data, allowing training to be scheduled proactively rather than reactively. This reduces unnecessary training interruptions while ensuring drivers are prepared for anticipated challenging scenarios.
Solution Approach 2:
The system performs experience gap analysis to determine whether training is actually needed based on the driver's historical performance data. By providing feedback-driven, targeted training only when experience gaps are identified, the system minimizes time loss while maintaining safety improvements.
3Manufacturing precision
If experience gap analysis is performed to identify training needs, then manufacturing precision is improved in terms of training accuracy, but device complexity increases due to additional analysis requirements
Solution Approach 1:
The system automatically collects driving data, performs experience gap analysis, and generates personalized training simulations without requiring external intervention. The autonomous vehicle's existing sensors and processors are utilized to maintain accuracy while avoiding additional hardware complexity.
4Reliability
If mixed reality driving training simulation is provided in-vehicle, then driver safety is improved through targeted training, but device complexity increases due to mixed reality system requirements
Solution Approach 1:
The mixed reality system leverages the autonomous vehicle's existing display, camera, and sensor infrastructure to deliver training simulations. By making the vehicle's hardware serve multiple functions (both autonomous operation and training simulation), the system improves driver safety without proportionally increasing device complexity.
Data Source
AI summary
Embodiments of the present invention provide an approach for providing in-vehicle predicted context-based proactive driving training using an autonomous vehicle. A knowledge corpus is established from a driver's previous driving experience. A potential driving context (or scenario) is identified for a forthcoming driving route. An experience gap analysis is performed between the driver's experience and the potential driving context. If an experience gap exists, an in-vehicle mixed reality driving training simulation is provided in a selected location by the autonomous vehicle. The driver's responses to the training simulation can optionally be monitored and a determination can made based on the driver responses as to the suitability of the driver to safely address the potential driving context.