Vehicle Environmental Data Encoding for Multi-Vehicle Driving Decisions
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Solution Overview
Problem
Conventional approaches to processing environmental data for vehicles are inefficient, particularly in capturing interactions between vehicles and extracting multi-level interactions, which limits their performance in autonomous driving applications.
Innovation Solution
The method employs a self-attention mechanism to process data from surrounding vehicles and stacked residual blocks to process data from the target vehicle, generating outputs that are concatenated to inform driving decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional approaches are used to process environmental data, then the processing method is simple, but the efficiency in capturing interactions between vehicles is poor
Solution Approach 1:
The patent segments the processing of environmental data by separating the target vehicle data from surrounding vehicle data. Different neural network architectures are applied to each segment: stacked residual blocks for the target vehicle and self-attention mechanisms for surrounding vehicles. This segmentation allows each component to be optimized independently, improving overall processing efficiency while managing complexity through modular design.
Solution Approach 2:
The patent introduces multi-level interaction processing by adding temporal and spatial dimensions to the data processing. The stacked residual blocks process temporal sequences of target vehicle data, while the self-attention mechanism processes spatial relationships among multiple surrounding vehicles. This dimensional expansion enables comprehensive capture of vehicle interactions without overwhelming computational complexity.
2Loss of information
If conventional approaches are used to process environmental data, then the system is easy to implement, but the ability to extract multi-level interactions is limited
Solution Approach 1:
The patent introduces an intermediary encoding layer that transforms raw environmental data into meaningful interaction representations. The stacked residual blocks encode temporal dynamics of the target vehicle, while the self-attention mechanism encodes spatial relationships with surrounding vehicles. These intermediate encodings preserve multi-level interaction information that would be lost in conventional processing, enabling better decision-making without requiring direct complex processing of all raw data.
Solution Approach 2:
The patent employs dynamic processing architectures that adapt to varying numbers of surrounding vehicles. The self-attention mechanism dynamically adjusts to process any number of surrounding vehicles, and the stacked residual blocks adapt to temporal variations in target vehicle data. This dynamic capability ensures comprehensive interaction information capture regardless of scene complexity, while the modular design keeps implementation manageable.
3Adaptability or versatility
If the number of surrounding vehicles varies, then the system must be adaptable, but conventional methods struggle with varying input sizes
Solution Approach 1:
The patent implements a universal processing framework where the self-attention mechanism can handle any number of surrounding vehicles through its inherent ability to process variable-length sequences. The stacked residual blocks provide universal temporal processing for the target vehicle. This universal design ensures reliable decision-making across diverse scenarios with varying vehicle counts, as the architecture naturally adapts rather than requiring scenario-specific configurations.
Data Source
AI summary
Examples described herein provide a computer-implemented method for processing environmental data for at least one other vehicle relative to a target vehicle. The method includes processing, by a processing device using a self-attention mechanism, first data associated the at least one other vehicle to generate a first output associated with the at least one other vehicle. The method further includes processing, by the processing device using stacked residual blocks, second data associated the target vehicle to generate a second output associated with the target vehicle. The method further includes concatenating, by the processing device, the first output and the second output to generate a concatenated vector. The method further includes making, by the processing device, a driving decision for the target vehicle based at least in part on the concatenated vector. The method further includes controlling the target vehicle based at least in part on the decision.