Mixed Reality Traffic Object Generation for Adaptive Driver Training
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Solution Overview
Problem
There is a need for improved techniques to generate mixed reality virtual objects for vehicle operators to enhance their training in reacting to various traffic scenarios, addressing the lack of experience and apprehension in real-life driving situations.
Innovation Solution
A neural network-based system generates virtual objects for mixed reality displays, adjusting difficulty levels based on user comfort and performance, using a generator and discriminator network to simulate realistic traffic environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If virtual objects are generated for mixed reality display to enhance training, then user training effectiveness is improved, but system complexity increases
Solution Approach 1:
A neural network model acts as an intermediary between the real-world environment captured by cameras and the virtual objects displayed to the user. The generator network processes real environment images and difficulty level inputs to produce augmented images with synthesized virtual objects, mediating the complex task of training scenario generation through learned patterns rather than explicit programming
Solution Approach 2:
The system creates virtual copies of traffic objects (vehicles, pedestrians, obstacles) by generating synthetic images that replicate real-world scenarios. These copied objects are overlaid onto real environment captures, allowing users to train on virtual representations without requiring physical replicas or real dangerous situations
2Measurement precision
If virtual objects are generated to simulate challenging traffic scenarios, then user reaction skills are improved, but the difficulty of generating realistic virtual objects increases
Solution Approach 1:
A discriminator network provides feedback by evaluating generated virtual objects and distinguishing them from real objects. This feedback mechanism guides the generator network to improve the realism of virtual objects through iterative training, where the discriminator's ability to detect fakes drives the generator to create more convincing simulations
Solution Approach 2:
The system dynamically adjusts the difficulty level of generated scenarios based on user performance and comfort. The neural network model adapts to different skill levels, transforming static generation into a dynamic system that evolves with the user's training progress, making the complexity management adaptive rather than fixed
3Ease of operation
If the system adapts to user comfort levels, then ease of operation is improved, but the time required to assess and adjust difficulty increases
Solution Approach 1:
The system performs preliminary assessments of user comfort and skill level before full training begins. By evaluating user responses to initial virtual objects and adjusting difficulty in advance, the system prepares the appropriate training level beforehand, reducing the time needed for continuous adjustment during operation
Solution Approach 2:
The neural network model automatically monitors user performance metrics and self-adjusts the difficulty level without requiring manual intervention. The system serves itself by continuously evaluating user responses and autonomously modifying the generation parameters, eliminating the need for external assessors or complex manual calibration processes
Data Source
AI summary
Provided are a computer program product, system, and method for generating virtual objects for a mixed reality display to render in a field of view of a user operating a vehicle. A view environment image of a user field of view of a user operating the vehicle is received. The view environment image and a user difficulty level for the user are inputted into a generator, comprising a neural network model, to generate an augmented environment image including virtual objects added to the view environment image. The virtual objects are transmitted to a mixed reality display to render in the field of view of the user.