Discriminator Network for Autonomous Vehicle ODD Detection
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Solution Overview
Problem
Existing solutions for autonomous vehicles to navigate out of operational design domain (ODD) scenarios are cumbersome and difficult to scale, relying on sensor models and statistical analysis, which are not nimble or easily retrainable as the scope of ODD conditions expands.
Innovation Solution
Implementing a generative adversarial network (GAN) with a discriminator network to process sensor data directly from the perception system of an autonomous vehicle, allowing for the detection of ODD scenarios and enabling responsive motion control by retraining the GAN on new driving logs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensor models and statistical analysis are used to detect ODD scenarios, then detection capability is provided, but the system becomes cumbersome and difficult to scale
Solution Approach 1:
The patent replaces traditional sensor models and statistical analysis methods with a neural network-based perception system. The neural network directly processes sensor data to identify ODD scenarios, eliminating the need for complex sensor modeling and statistical computations. This substitution of mechanical/mathematical systems with an intelligent learning system resolves the contradiction by providing reliable detection while reducing system complexity and improving scalability.
Solution Approach 2:
The patent changes the fundamental parameters of the detection system by transitioning from fixed sensor models and statistical thresholds to adaptive neural network parameters that learn from training data. The neural network's weights and biases are optimized through training on labeled driving scenarios, enabling the system to detect ODD conditions without requiring complex manual configuration or scaling adjustments.
2Reliability
If traditional sensor models and statistical analysis are used, then ODD detection is achieved, but the system is not nimble or easily retrainable
Solution Approach 1:
The patent implements a dynamic perception system using neural networks that can be retrained on new driving logs to adapt to expanding ODD conditions. Unlike static sensor models, the neural network parameters are dynamically adjustable through continued training on additional data. This enables the system to maintain reliable ODD detection while being nimble and easily retrainable as operational domains expand.
Solution Approach 2:
The neural network-based perception system performs self-improvement through automated training on new driving logs. The system can retrain itself on additional data without requiring complete system redesign or complex recalibration procedures. This self-service capability resolves the contradiction by providing both reliable detection and easy retrainability through automated learning processes.
Data Source
AI summary
Provided are methods for detecting when a vehicle is encountering an out of operational design domain (ODD) scenario, which can include training a generative adversarial network (GAN) including a generator network and a discriminator network. The generator network may be trained to generate synthesized scenarios. The discriminator network may be trained to distinguish between true scenarios and the synthesized scenarios generated by the generator network. The trained discriminator network may be applied to detect when a vehicle encounters an out of operational design domain (ODD) scenario. Some methods described also include controlling the motion of the vehicle in response to an output of the trained discriminator network indicating that the vehicle is encountering the out of operational design domain (ODD) scenario. Systems and computer program products are also provided.