Attention Weighting for Trajectory Prediction Across Sensor Captures
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Solution Overview
Problem
Current trajectory prediction methods in automotive applications are hindered by the reliability of single captures, which can be unreliable and fail to effectively account for temporal dependencies, leading to inaccuracies in predicting the trajectory of objects like vehicles or pedestrians.
Innovation Solution
A computer-implemented method for determining weights in an attention-based trajectory prediction system, which receives a sequence of captures, determines unnormalized and normalized weights recursively, using a merging rule with trainable parameters and exponential normalization, to propagate historical data efficiently and enhance prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If single capture is used for trajectory prediction, then computational complexity is low, but prediction reliability is poor
Solution Approach 1:
The method segments the weight determination process into two distinct stages: unnormalized weight calculation for each individual capture, and normalized weight calculation that combines unnormalized weights across multiple captures. This segmentation allows the system to process multiple captures for improved reliability while maintaining computational efficiency through the structured two-step approach.
Solution Approach 2:
The method performs preliminary calculation of unnormalized weights for all captures before conducting the normalization step. By pre-computing the unnormalized weights based on individual capture features, the system prepares data in advance for the final normalization operation, reducing computational complexity during the critical prediction phase while maintaining high prediction reliability.
2Measurement precision
If multiple captures are processed to account for temporal dependencies, then prediction accuracy is improved, but computational complexity increases
Solution Approach 1:
The method merges information from multiple captures by combining their unnormalized weights through the normalization process. The normalized weight for each capture is computed by integrating its unnormalized weight with those of other captures in the sequence, effectively merging temporal information while producing a unified weight distribution that improves prediction accuracy without requiring complex temporal modeling.
Solution Approach 2:
The method transforms the raw capture data into a different parameter space by computing unnormalized weights based on feature similarities, then further transforms these into normalized weights through exponential normalization. This parameter transformation approach enables the system to process temporal dependencies efficiently by operating in the weight space rather than directly manipulating raw sensor data from multiple captures.
3Productivity
If recursive weight determination is used, then processing efficiency is improved, but information loss may occur
Solution Approach 1:
The recursive normalization process incorporates feedback from previously computed normalized weights when calculating current normalized weights. Each normalized weight is determined by combining the unnormalized weight with feedback from the normalized weight distribution, ensuring that historical information is continuously integrated into the current prediction while maintaining processing efficiency through the recursive structure.
Solution Approach 2:
The method performs preliminary computation of unnormalized weights that encode all necessary information from individual captures before the recursive normalization process. By pre-computing these information-rich unnormalized weights, the system ensures that no historical information is lost during recursion, as all capture-specific features are already captured in the unnormalized stage before the efficient recursive combining begins.
Data Source
AI summary
A computer implemented method for determining weights for an attention based 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.