Uncertainty Modeling in Multi-Modal Driving Event Retrieval
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Solution Overview
Problem
Current neural network retrieval operations lack effective methods to model uncertainty, particularly in complex driving scenarios, which hinders the improvement of autonomous vehicle decision-making by failing to accurately identify and associate driving events with corresponding actions.
Innovation Solution
The method employs a triplet loss as a regression loss to train neural networks, using multi-modal data to determine embeddings for driving events, enabling uncertainty modeling through dropout and Monte Carlo sampling, thereby improving the retrieval of similar events and actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If neural networks are trained using conventional loss functions for retrieval operations, then the network can perform basic retrieval tasks, but the system cannot effectively model uncertainty in complex driving scenarios
Solution Approach 1:
The patent transforms the retrieval task from a classification problem into a regression problem by changing the loss function parameter from categorical cross-entropy to triplet loss with Monte Carlo dropout. This parameter change enables uncertainty estimation while maintaining retrieval functionality, resolving the contradiction between reliability and complexity.
Solution Approach 2:
The patent introduces Monte Carlo dropout as an intermediary technique between the neural network and the retrieval operation. By applying dropout during inference and using the variance of predictions as uncertainty measure, it bridges the gap between conventional retrieval and uncertainty modeling without requiring a completely new system architecture.
2Measurement precision
If the system retrieves more similar driving events to improve decision-making accuracy, then the quality of autonomous vehicle decisions improves, but the computational time and resources increase
Solution Approach 1:
The patent replaces the conventional mechanical retrieval process (exact matching or simple similarity comparison) with a learned embedding space using triplet loss. This substitution allows for more accurate event identification through semantic similarity while maintaining efficient retrieval speed through the structured loss function that directly optimizes for retrieval performance.
Data Source
AI summary
Multi-modal data representing driving events and corresponding actions related to the driving events can be obtained and used to train a neural network at least in part by using a triplet loss computed for the driving events as a regression loss to determine an embedding of driving event data. In some cases, using the trained neural network, a retrieval request for an input driving event and corresponding action can be processed by determining, from the neural network, one or more similar driving events or corresponding actions in the multi-modal data.