Context2vec Neural Network for Autonomous Vehicle Control
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Solution Overview
Problem
Autonomous vehicles face challenges in processing and understanding complex environmental data to make appropriate decisions, as existing technologies lack effective methods to embed contextual information into vector space models for contextual modeling and control operations.
Innovation Solution
A system and method that uses a neural network with context2vec embeddings to process sensor data from autonomous vehicles, enabling the embedding of contextual information into a vector space model for subsequent lookup, comparison, and control operations, utilizing a computer-implemented system with a neural network that determines target context2vec words based on current, prior, and subsequent encoded context2vec words for behavior control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If contextual information is embedded into vector space models using neural networks, then the autonomous vehicle's ability to model and process contextual information for decision-making is improved, but the device complexity and computational requirements increase
Solution Approach 1:
The system segments contextual information into distinct encoded context2vec words representing different aspects (mapping, situational, behavior data), allowing the neural network to process complex contexts through modular vector representations rather than monolithic data structures
Solution Approach 2:
The patent introduces context2vec embeddings as an intermediary layer between raw sensor data and control decisions. These embeddings serve as a bridge that transforms complex contextual information into a standardized vector space format that the neural network can efficiently process
2Measurement precision
If the neural network processes multiple encoded context2vec words (current, prior, subsequent) to determine target context2vec words, then the accuracy of behavior control decisions is improved, but the processing time and computational load increase
Solution Approach 1:
The system performs preliminary encoding of contextual information into context2vec words before neural network processing. By pre-structuring sensor data into standardized encoded context2vec formats with defined temporal relationships (prior, current, subsequent), the system reduces the computational burden during real-time decision-making
Solution Approach 2:
The neural network dynamically processes variable sequences of encoded context2vec words based on the specific driving scenario. The system adapts the processing window (number of prior and subsequent words considered) to balance decision accuracy with processing speed requirements for different operational contexts
Data Source
AI summary
Systems, Apparatuses and Methods for implementing a neural network system for controlling an autonomous vehicle (AV) are provided, which includes: a neural network having a plurality of nodes with context to vector (context2vec) contextual embeddings to enable operations of the AV; a plurality of encoded context2vec AV words in a sequence of timing to embed data of context and behavior; a set of inputs which comprise: at least one of a current, a prior, and a subsequent encoded context2vec AV word; a neural network solution applied by the at least one computer to determine a target context2vec AV word of each set of the inputs based on the current context2vec AV word; an output vector computed by the neural network that represents the embedded distributional one-hot scheme of the input encoded context2vec AV word; and a set of behavior control operations for controlling a behavior of the AV.