Attention Weighting for Trajectory Prediction Across Time Steps
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Solution Overview
Problem
Existing trajectory prediction methods for vehicles, cyclists, and pedestrians in automotive applications lack efficiency and reliability, particularly due to the unreliability of single captures and the need to account for temporal dependencies and interactions among multiple agents.
Innovation Solution
A method for determining weights in an attention-based trajectory prediction system that utilizes a sequence of captures, including recursive normalization and exponential normalization techniques, to integrate temporal information and improve prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional trajectory prediction methods are used, then computational complexity is reduced, but prediction reliability and accuracy deteriorate due to inability to account for temporal dependencies
Solution Approach 1:
The patent segments the trajectory prediction problem into distinct temporal components by processing individual captures separately with unique attention weights. Each capture is treated as an independent observation that contributes differently to the final prediction, allowing temporal dependencies to be modeled without requiring complex global processing of the entire sequence simultaneously.
Solution Approach 2:
The patent applies preliminary normalization to individual capture weights before combining them in the attention mechanism. By pre-processing each capture's weight contribution and normalizing it independently, the system simplifies the overall computational process while maintaining the ability to account for temporal variations in capture quality and relevance.
2Reliability
If single capture methods are used, then processing speed is improved, but prediction accuracy deteriorates due to unreliability of single captures
Solution Approach 1:
The patent merges multiple individual capture assessments into a unified trajectory prediction by combining their respective attention weights. Each capture provides a partial assessment of the trajectory, and the attention mechanism merges these assessments with appropriate weighting, achieving higher accuracy than any single capture could provide alone while maintaining efficient processing.
Solution Approach 2:
The patent changes the parameter of capture reliability by assigning different attention weights to different captures based on their individual qualities and temporal contexts. This allows the system to dynamically adjust the contribution of each capture to the final prediction, improving overall accuracy by emphasizing reliable captures and downweighting unreliable ones without requiring equal processing time for all captures.
3Reliability
If comprehensive sequence analysis is performed, then temporal dependency modeling is improved, but computational efficiency deteriorates
Solution Approach 1:
The patent extracts the essential temporal dependency information from the sequence of captures by focusing on individual capture weights and their normalized contributions. Rather than analyzing complex temporal relationships between all captures simultaneously, the method extracts and processes each capture's individual weight contribution separately, maintaining temporal modeling capability while significantly reducing computational complexity.
Solution Approach 2:
The patent applies partial action by processing only the necessary weight normalization for each capture individually rather than performing comprehensive sequence analysis. This partial processing approach captures the essential temporal dependencies needed for accurate prediction while avoiding the excessive computational burden of analyzing all possible temporal relationships in the sequence.
Data Source
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AI summary
A computer implemented method for determining weights for an attention based method for trajectory prediction comprises the following steps carried out by computer hardware components: receiving a sequence of a plurality of captures taken by a sensor; determining an unnormalized weight for a first capture of the sequence based on the first capture of the sequence; and determining a normalized weight for the first capture of the sequence based on the unnormalized weight for the first capture of the sequence and a normalized weight for a second capture of the sequence.