AI Driver Simulation Training for Repeatable Autonomous Vehicle Testing
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Solution Overview
Problem
The impracticality of conducting extensive road tests for autonomous vehicles (AVs) due to the need for billions of miles of testing, cost, time consumption, and inability to replicate real-world scenarios for comparing AI driver performance with human drivers.
Innovation Solution
A simulation system that includes a memory and processing circuit to generate pixelated images from sensor data, determine actuator commands, and simulate behaviors of an ego vehicle object within a virtual environment, allowing for efficient training and comparison of AI drivers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If extensive road tests are conducted to obtain sufficient sample size for observing failures, then the reliability of AI driver training is improved, but the time consumption and cost increase significantly
Solution Approach 1:
The patent creates virtual copies of real-world driving environments, vehicles, and road conditions through simulation. Instead of conducting billions of miles of physical road tests, the system generates synthetic training data by copying and replicating driving scenarios in a virtual environment. This allows extensive testing and training to be performed on copied versions rather than requiring equivalent physical testing time.
2Reliability
If extensive road tests are conducted to discover particular points of failure, then the reliability of AI driver training is improved, but the productivity decreases due to massive mileage requirements
Solution Approach 1:
The system copies failure scenarios and edge cases into the simulation environment, allowing rapid reproduction and analysis of failure points without requiring billions of miles of physical testing. Virtual copies of problematic scenarios can be generated and tested repeatedly to identify and resolve failure points efficiently.
Solution Approach 2:
The simulation system performs preliminary testing and training in the virtual environment before any physical road tests. By conducting preliminary actions in simulation, the system identifies and resolves issues beforehand, reducing the need for extensive corrective testing in the physical world and improving overall productivity.
3Adaptability or versatility
If road tests cover a wide range of weather conditions and road conditions, then the adaptability of AI driver training is improved, but the complexity and cost of组织实施 increase
Solution Approach 1:
The simulation system allows independent adjustment of environmental parameters such as weather conditions, road surfaces, lighting, and traffic patterns. Instead of physically traveling to different locations and conditions, the system changes parameters digitally to create diverse training scenarios. This improves adaptability while reducing the organizational complexity of arranging physical tests across multiple environments.
4Reliability
If previously performed road tests are re-performed after system revisions to confirm no unintended side-effects, then the reliability is maintained, but the time consumption increases
Solution Approach 1:
The system maintains virtual copies of previously validated driving scenarios and automatically re-executes them after system revisions. Instead of manually re-performing physical road tests, the simulation copies existing test cases and runs them again to verify that revisions did not introduce unintended side-effects, maintaining reliability with minimal time investment.
Data Source
AI summary
Examples described herein relate to apparatuses and methods for or simulating and improving performance of an artificial intelligence (AI) driver, including but not limited to generating sensor data corresponding to a virtual environment, generating a pixelated image corresponding to the virtual environment based on the sensor data, determining actuator commands responsive to pixels in the pixelated image, wherein the decision module determines the actuator commands based on the AI driver, and simulating behaviors of the ego vehicle object using the actuator commands.