Behavioral Cloning with Structured World Models
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Solution Overview
Problem
Current autonomous driving methods, such as behavioral cloning and modular pipelines, face challenges in generalization, stability, and scalability due to low sample efficiency, error-prone labeling processes, and labor-intensive supervision, leading to potential false negatives in complex driving situations.
Innovation Solution
The implementation of a self-supervised learning system that generates inductive biases about the structure of driving scenes to regularize intermediate states of policies trained using behavioral cloning, combining the benefits of both technologies to enhance generalization without the laborious costs of modular methods, utilizing neural networks for real-time data processing and expert demonstrations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If behavioral cloning models are used for autonomous driving, then the system can scale well to routine driving situations by learning from expert demonstrations, but the model suffers from generalization issues and low sample efficiency requiring extensive training sets to anticipate every possible driving situation
Solution Approach 1:
The system segments the autonomous driving task into multiple components: a behavioral cloning model for routine situations and a modular pipeline with computer vision methods for complex situations. This segmentation allows each component to specialize in specific driving contexts, improving overall reliability while maintaining scalability for routine operations
Solution Approach 2:
The system performs preliminary processing through computer vision methods to create an explicit representation of the driving world before behavioral cloning takes over. This preliminary action prepares structured intermediate representations that improve the behavioral cloning model's ability to generalize to complex situations it hasn't seen during training
2Reliability
If modular pipelines with computer vision methods are used to learn explicit representation of the world, then the system generalizes well, but it is not easily scalable due to labor intensive labeling processes and error prone compounding of errors
Solution Approach 1:
The system merges behavioral cloning and modular pipelines into a hybrid architecture where computer vision methods generate intermediate representations that are then processed by behavioral cloning models. This combination reduces the need for extensive manual labeling while maintaining the generalization benefits of explicit world representation
Solution Approach 2:
The system introduces an intermediary layer of structured intermediate representations generated by computer vision methods. This intermediary structure serves as a bridge between raw sensor data and final driving decisions, reducing error propagation while requiring less manual supervision than fully modular pipelines
3Productivity
If neural networks are used to process real-time sensor data, then the system can handle information efficiently, but the planning system may blindly trust the output and make false negatives determining safety incorrectly
Solution Approach 1:
The system implements feedback mechanisms where the modular pipeline continuously validates the behavioral cloning model's output. When the structured intermediate representations indicate potential safety issues, the system overrides the neural network's decision, preventing false negatives while maintaining real-time processing capability
Solution Approach 2:
The system applies partial validation through the modular pipeline rather than exhaustive verification of every neural network output. This selective application of validation rules maintains real-time processing speed while providing sufficient checks to prevent critical false negatives in safety determinations
Data Source
AI summary
Systems, methods, computer-readable media, techniques, and methodologies are disclosed for generating vehicle controls and/or driving policies based on machine learning models that utilize intermediate representation of driving scenes as well as demonstrations (e.g. by behavioral cloning). An intermediate representation that includes inductive biases about the structure of driving scenes for a vehicle can be generated by a self-supervised first machine learning model. A driving policy for the vehicle can be determined by a second machine learning model trained by a set of expert demonstrations and based on the intermediate representation. The expert demonstrations can include labelled data. An appropriate vehicle action may then be determined based on the driving policy. A control signal indicative of this vehicle action may then be output to cause an autonomous vehicle, for example, to implement the appropriate vehicle action.