Image-Level Road Element Representation for Real-Time Driving Interaction
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Solution Overview
Problem
Vehicles with autonomous driving capabilities and/or driver assistance capabilities face challenges in processing road element information efficiently and effectively, leading to suboptimal response times and resource inefficiencies.
Innovation Solution
Utilizing transformer neural networks with self-attention mechanisms and positional encoding to process multiple tokens per road element, including classification, location, and behavioral tokens, to enhance the processing of road elements, thereby improving resource efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional methods are used to process road element information, then the system is simpler to implement, but processing efficiency and accuracy deteriorate
Solution Approach 1:
The patent segments road element information into multiple distinct token types (classification tokens, location tokens, behavioral tokens). Each token type represents a specific attribute or aspect of the road element, allowing the system to process different aspects separately and efficiently. This segmentation enables the model to handle complex information in a structured manner, improving processing efficiency without overwhelming system complexity.
Solution Approach 2:
The patent introduces positional encoding to add spatial dimension information to the token representations. By embedding positional information into the token vectors, the system can process spatial relationships and locations without requiring separate processing mechanisms. This dimensional enhancement allows the model to understand both what objects are present and where they are located, improving overall processing efficiency.
2Measurement precision
If multiple tokens per road element are processed, then processing accuracy improves, but storage and processing demands increase
Solution Approach 1:
The patent extracts only the most relevant attributes of road elements into separate token types. Instead of processing all possible information about each road element, the system identifies and extracts key attributes (classification, location, behavior) into distinct tokens. This extraction approach maintains high processing accuracy by focusing on essential information while reducing overall storage requirements compared to processing complete raw data.
Solution Approach 2:
The patent transforms road element information into parameterized token representations with fixed dimensions. By converting varied road element data into standardized token vectors with consistent sizes, the system achieves efficient storage and processing. The parameterization allows different types of information to be represented in a uniform format, reducing storage variability while maintaining the precision needed for accurate processing.
3Reliability
If transformer neural networks with self-attention mechanisms are used, then information processing effectiveness improves, but computational resource consumption increases
Solution Approach 1:
The patent segments the input data into structured tokens before processing, which enables the transformer's self-attention mechanism to operate more efficiently. By pre-organizing information into meaningful token units (classification, location, behavioral), the attention mechanism can focus computational resources on relevant token interactions rather than processing raw unstructured data. This segmentation reduces the effective computational burden while maintaining processing effectiveness.
Solution Approach 2:
The patent performs preliminary processing to encode positional information and structure tokens before they enter the transformer network. This preliminary action includes adding positional encodings to tokens and organizing them in a structured sequence. By preparing the data in advance, the transformer requires fewer computational iterations to achieve accurate results, reducing overall resource consumption while maintaining high information processing effectiveness.
Data Source
AI summary
A method of using an artificial neural network to generate granular image level representations for driving, the method includes (a) obtaining a sensed information unit that captures a first element, (b) generating, by a machine learning process using the artificial neural network, a first set of tokens for the first element each representing a respective attribute characterizing the first element, (c) processing, by the machine learning process, the first set of tokens in correspondence with at least a second set of tokens generated for a second element, (d) producing, based on the processing, an image-level representation for the first element with respect to the second element, (e) determining, based on the image-level representation, an interaction between the first and second elements in real time; and (f) determining, based on the determined interaction, a driving related output with respect to the vehicle.