Unified Token Neural Network for Vehicle Environment Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional vehicle data processing systems face limitations in scalability and adaptability due to separate sub-systems being trained individually, leading to error accumulation and difficulty in handling complex tasks in novel environments, especially when trained on limited end-to-end training scenarios.
Innovation Solution
A token processing neural network, such as a language or multi-modal model, is trained end-to-end to perform various prediction tasks directly on sensor data and queries, using a diverse set of training data to enhance adaptability and accuracy, and can be pre-trained for reduced computational costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate sub-systems are trained individually, then each sub-system can be optimized for its specific task, but error accumulation occurs and adaptability to novel environments deteriorates
Solution Approach 1:
The patent merges multiple separate sub-systems into a single unified end-to-end trained system. Instead of having independent modules for detection, tracking, and prediction that process data sequentially, the invention integrates them into one cohesive neural network architecture that processes sensor data through all stages simultaneously, eliminating error accumulation at interface points between subsystems.
Solution Approach 2:
The unified system performs multiple functions within a single architecture, handling detection, tracking, trajectory prediction, and various prediction tasks (collision risk, time to collision, etc.) simultaneously. This multi-functional approach allows the system to adapt to novel environments more effectively while maintaining high precision across all tasks through end-to-end training.
2Ease of manufacture
If separate sub-systems are used, then system modularity is maintained, but scalability and adaptability to complex tasks deteriorate
Solution Approach 1:
The invention combines multiple specialized sub-systems into a unified architecture that maintains ease of deployment while significantly improving scalability. The end-to-end trained system can be scaled to handle increasingly complex tasks by expanding the training data and adjusting the network architecture, rather than requiring integration of multiple separate modular components.
3Loss of time
If limited end-to-end training data is used, then training time is reduced, but prediction accuracy and adaptability deteriorate
Solution Approach 1:
The system performs preliminary actions by pre-processing sensor data into structured representations (such as embedding sequences or feature vectors) before feeding them to the prediction network. This preliminary structuring allows the model to achieve high accuracy with fewer training examples, as the difficult feature extraction and data alignment work is done in advance during the pre-processing stage rather than requiring extensive training to learn.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for processing sensor data characterizing an environment of a vehicle to generate predictions regarding the environment of the vehicle. In one aspect, a method comprises obtaining training data comprising a plurality of training examples, wherein each training example comprises (i) example sensor data comprising one or more observations of a driving environment of an example vehicle for the training example, (ii) an example query for the training example, and (iii) a target prediction for the training example; processing the example sensor data and the example query for each training example to generate a respective network input comprising a plurality of input tokens for each training example; and training a token processing neural network to optimize a likelihood of the token processing neural network generating the target predictions for the training examples by processing the corresponding network inputs.