Joint Feature Space for Driving Behavior Identification Without Labels
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Solution Overview
Problem
Current machine learning approaches for autonomous vehicles face challenges in accurately identifying driving behaviors without manually labeled training data, as supervised methods require extensive labeled examples and unsupervised methods underperform.
Innovation Solution
A system that generates a joint feature space by correlating textual descriptions of driving rules with driving log snippets, using encoders to map these into a shared vector space, enabling behavior cloning without explicit labeling, and employing a policy network to produce appropriate controls.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supervised machine learning approaches are used to train a model to identify relevant behaviors for different contexts, then the accuracy of behavior identification is improved, but the difficulty and cost of acquiring manually labeled training data increases significantly
Solution Approach 1:
The system performs self-supervised learning by automatically generating labels from the structure of the data itself and inductive biases as priors, eliminating the need for manual labeling while maintaining accurate behavior identification
Solution Approach 2:
The system pre-trains encoders using self-supervised learning on raw driving log data and textual descriptions to learn meaningful representations before fine-tuning on specific behavior identification tasks, reducing the need for labeled data
2Ease of manufacture
If unsupervised or self-supervised learning approaches are used to operate without manually labeled data, then the ease of data acquisition is improved, but the performance of the model in identifying behaviors deteriorates
Solution Approach 1:
The system introduces a joint feature space as an intermediary that aligns embeddings from different encoders (driving log encoder and textual description encoder), enabling the model to learn from unlabeled data while maintaining performance through cross-modal correspondence
Solution Approach 2:
The system changes the parameter space by creating a shared joint feature space where embeddings from different modalities are mapped, allowing the model to leverage structural relationships in unlabeled data for accurate behavior identification
3Measurement precision
If a joint feature space is generated by correlating textual descriptions with driving log snippets using encoders, then the accuracy of driving behavior identification is improved, but the computational complexity and processing time increase
Solution Approach 1:
The system segments the processing into distinct components: separate encoders for driving logs and textual descriptions, followed by a joint feature space alignment stage, allowing efficient parallel processing while maintaining accuracy
Solution Approach 2:
The system maps embeddings from different modalities into a shared joint feature space, adding a dimensional transformation layer that enables efficient comparison and alignment without exhaustive computation
Data Source
AI summary
Systems, methods, and other embodiments described herein relate to determining driving behaviors for controlling a vehicle. In one embodiment, a method includes generating, using textual descriptions in combination with driving log snippets, a joint feature space that represents a coordinated mapping between the textual descriptions and the driving log snippets. The method includes training a policy network to generate identified behaviors from the driving behaviors according to a correspondence between an observed context that is mapped onto the joint feature space and the driving behaviors defined in the joint feature space resulting from at least the textual descriptions. The method includes providing a behavior cloning model including at least an encoder, the joint feature space, and the policy network to generate control behaviors from the driving behaviors defined in the joint feature space according to acquired observations of a surrounding environment of the vehicle.