Neural Network Training for Autonomous Vehicle Perception Degradation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Autonomous vehicles face challenges in identifying and handling scenarios that cause perception degradation at the edge of their operational envelope, such as object flicker, which can lead to safety concerns due to unmodeled situations in their software.
Innovation Solution
A neural network-based system processes state information to determine actions that simulate perception degradation, adjusting weights based on rewards to learn safety-critical scenarios, allowing for more accurate training and holistic safety analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the AV operates at the edge of its operational envelope, then it can handle more diverse scenarios, but perception degradation occurs leading to safety concerns
Solution Approach 1:
The system performs preliminary actions by simulating perception degradation scenarios during training before actual deployment. The neural network is trained on synthetically generated edge cases including object flicker, occlusion, and sensor failures, enabling the AV to learn appropriate responses in advance rather than encountering these scenarios for the first time during operation.
Solution Approach 2:
The system converts the harmful effect of perception degradation into a beneficial training mechanism. By intentionally introducing perception degradation scenarios during training and using reinforcement learning to identify safety-critical situations, the system transforms potential failure modes into opportunities for improving robustness and safety.
2Reliability
If traditional training methods are used, then the system is simpler to implement, but it cannot identify safety-critical scenarios at the edge of the operational envelope
Solution Approach 1:
The system introduces an intermediary reinforcement learning component that mediates between the neural network and the training data. This intermediary layer processes states and actions, assigns rewards based on safety-critical criteria, and adjusts training priorities, enabling sophisticated scenario identification without requiring complete system redesign.
Solution Approach 2:
The system replaces traditional rule-based safety verification with a learning-based approach. Instead of manually encoding safety rules and operational envelopes, the system uses neural networks trained with reinforcement learning to automatically identify safety-critical scenarios, transitioning from deterministic mechanical verification to adaptive learning-based verification.
3Measurement precision
If reinforcement learning is used to identify safety-critical scenarios, then training accuracy improves, but computational resources and training time increase
Solution Approach 1:
The system uses synthetic copies of real-world scenarios generated through simulation rather than requiring extensive real-world data collection and processing. By copying realistic driving scenarios including edge cases and perception degradation conditions in a virtual environment, the system achieves high training accuracy without the time cost of equivalent real-world experimentation.
Data Source
AI summary
Provided are methods for learning to identify safety-critical scenarios for autonomous vehicles. First state information representing a first state of a driving scenario is received. The information includes a state of a vehicle and a state of an agent in the vehicle's environment. The first state information is processed with a neural network to determine at least one action to be performed by the agent, including a perception degradation action causing misperception of the agent by a perception system of the vehicle. Second state information representing a second state of the driving scenario is received after performance of the at least one action. A reward for the action is determined. First and second distances between the vehicle and the agent are determined and compared to determine the reward for the at least one action. At least one weight of the neural network is adjusted based on the reward.