Pseudo-Random Traffic Scene Sequences for Stable Self-Supervised Learning
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Solution Overview
Problem
Autonomous vehicles face challenges in training neural networks with self-supervised learning due to difficulties in randomizing inputs without losing semantic and contextual information, which affects the accuracy and reliability of the training process.
Innovation Solution
The use of pseudo-random sequences, such as Sobol sequences, for generating randomized numerical distributions of traffic scenes to train neural networks, allowing for self-supervised training that maintains semantic meaning and context, thereby enhancing the stability and efficiency of the neural network backbone.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional randomization methods are used for self-supervised learning training inputs, then training diversity is improved, but semantic and contextual information is lost
Solution Approach 1:
The patent introduces pseudo-random sequences as an intermediary between deterministic input sequences and fully random sequences. These sequences provide statistical properties of randomness while maintaining structured relationships, thereby mediating between the need for training diversity and the need to preserve semantic information in time-series data
Solution Approach 2:
The patent changes the parameter of randomization from traditional uniform random sampling to pseudo-random sequences with controlled statistical properties. This parameter change allows the system to maintain information preservation while achieving sufficient diversity for effective self-supervised learning
2Measurement precision
If more compute resources are allocated for neural network training, then training accuracy is improved, but training time and cost increase
Solution Approach 1:
The patent implements self-supervised learning where the model learns from unlabeled data through self-generated tasks and objectives. This self-service approach eliminates the need for expensive manual labeling while achieving high training accuracy, thereby improving the accuracy-to-compute-ratio
3Adaptability or versatility
If complex neural network architectures are used, then model capability is improved, but training stability and speed deteriorate
Solution Approach 1:
The patent employs pseudo-random sequences that maintain continuous statistical properties throughout the training process. This continuity provides stable gradients and consistent learning signals, enabling complex models to train stably without sacrificing model capability
Data Source
AI summary
Systems and techniques are provided for generating self-supervised neural networks and using pseudo-random sequences of traffic scenes for self-supervised training. An example method can include obtaining sensor data collected for a scene associated with an autonomous vehicle (AV), the sensor data describing, measuring, or depicting one or more elements in the scene; generating one or more sets of numerical representations of the sensor data, wherein each numerical representation of the one or more sets of numerical representations represents at least one element of the one or more elements; and determine, via a neural network, a semantic meaning of at least one numerical representation from the one or more sets of numerical representations of the sensor data, the at least one numerical representation corresponding to the at least one element of the one or more elements.