Prediction Error Scenario Mining for Autonomous Vehicle ML Training
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Solution Overview
Problem
Conventional simulators fail to effectively test autonomous vehicles for a variety of driving scenarios, particularly error-prone and edge-case situations, leading to inadequate training of machine learning models for safe navigation.
Innovation Solution
A prediction error scenario mining framework that identifies and trains on error-prone scenarios by querying a scenario database using SQL queries, dynamically searching for and integrating new prediction errors and scenarios, ensuring comprehensive and data-driven model training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional simulators are used to test autonomous vehicles, then the testing process is simple and fast, but the ability to test error-prone and edge-case scenarios is insufficient
Solution Approach 1:
The patent introduces a scenario mining system as an intermediary between conventional simulators and the autonomous vehicle's machine learning model. This system queries a scenario database to identify error-prone scenarios, retrieves them, and feeds them back into the simulator for targeted testing. The scenario mining system acts as a mediator that enhances the simulator's capability to test edge cases without fundamentally changing the simulator's core functionality.
Solution Approach 2:
The system performs preliminary action by proactively querying the scenario database before actual testing to identify and retrieve error-prone scenarios. The scenario mining system anticipates potential failure modes by analyzing historical data and pre-selecting critical scenarios for testing, ensuring that the most important edge cases are tested before they occur in real-world operation.
2Reliability
If all driving scenarios are tested in real-world environments, then comprehensive training is achieved, but the testing process becomes dangerous and unfeasible
Solution Approach 1:
The patent creates a virtual copy of real-world driving scenarios through the scenario database, which stores replicated representations of driving conditions, road layouts, and vehicle behaviors. Instead of testing all scenarios in the physical world, the system queries and retrieves relevant scenarios from this digital copy, allowing comprehensive training without exposing vehicles to actual danger. The scenario database serves as a faithful replica that preserves the essential characteristics of real driving environments.
Solution Approach 2:
The system segments the vast space of possible driving scenarios into manageable categories by querying the scenario database with specific criteria. Rather than attempting to test every possible scenario simultaneously, the system divides the testing workload into discrete, identifiable scenario types (e.g., edge cases, error-prone situations, common driving patterns) and addresses them systematically through targeted queries and retrieval operations.
3Adaptability or versatility
If conventional simulators test only standard driving conditions, then the simulator remains simple and efficient, but edge-case and error-prone scenarios are not identified
Solution Approach 1:
The scenario mining system implements feedback by continuously querying the scenario database based on the machine learning model's performance characteristics and retrieving scenarios that are most likely to expose errors. The system analyzes which scenarios are most relevant to current model weaknesses and prioritizes those for testing, creating a feedback loop that adapts the testing focus to the model's actual needs rather than using a fixed, inefficient testing schedule.
Solution Approach 2:
The system introduces dynamics by making the scenario selection process adaptive and responsive to changing conditions. The scenario mining system dynamically queries the database based on current model performance, retrieves scenarios that are most relevant at any given time, and adjusts the testing focus as the machine learning model evolves. This dynamic approach allows the system to maintain high scenario diversity while improving testing efficiency by avoiding redundant tests of already-mastered scenarios.
Data Source
AI summary
Provided are methods for prediction error scenario mining for machine learning methods, which can include determining a prediction error indicative of a difference between a planned decision of an autonomous vehicle and an ideal decision of the autonomous vehicle. The prediction error is associated with an error-prone scenario for which a machine learning model of an autonomous vehicle is to make planned movements. The method includes searching a scenario database for the error-prone scenario based on the prediction error. The scenario database includes a plurality of datasets representative of data received from an autonomous vehicle sensor system in which the plurality of datasets is marked with at least one attribute of the set of attributes. The method further includes obtaining the error-prone scenario from the scenario database for inputting into the machine learning model for training the machine learning model. Systems and computer program products are also provided.