Attention-Based Object Tracking Across Frames With Lower Compute
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional object tracking systems in autonomous vehicles face challenges with high computational overhead, sensitivity to detection errors, and the need for separate models to handle different sensing modalities, making them difficult to deploy on systems with limited computational resources.
Innovation Solution
The implementation of sequence-to-sequence tracking methods using state-of-the-art transformer technology, which processes an entire sequence of objects as a whole, avoiding incremental entity-by-entity analysis and surrogate tasks, and using attention blocks to identify correlations between object depictions across frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional object tracking systems process objects incrementally entity-by-entity, then detection precision can be maintained, but computational overhead increases significantly
Solution Approach 1:
The patent merges the processing of multiple objects into a single unified attention mechanism. Instead of processing each object separately through independent tracking pipelines, the system combines all object depictions across frames into a single sequence that is processed by one transformer model with attention blocks, thereby reducing computational overhead while maintaining detection precision through the attention mechanism's ability to capture correlations between all objects simultaneously.
Solution Approach 2:
The transformer model with attention blocks serves as a universal processing unit that handles multiple objects, multiple frames, and multiple sensing modalities simultaneously. This single model performs detection, tracking, and correlation identification across all objects without requiring separate specialized models for each object or frame, thereby reducing overall system complexity while maintaining high detection precision through the attention mechanism.
2Adaptability or versatility
If separate models are used for different sensing modalities, then adaptability to various sensors is improved, but device complexity increases
Solution Approach 1:
The transformer model with attention blocks is designed as a universal architecture that can process multiple sensing modalities (camera, radar, lidar) through a unified attention mechanism. The attention blocks naturally handle different data types and modalities by computing correlations between all input tokens regardless of their source, eliminating the need for separate specialized models for each sensor type while maintaining high adaptability to various sensing technologies.
3Measurement precision
If incremental entity-by-entity analysis is used, then detection accuracy is maintained, but processing speed decreases
Solution Approach 1:
The patent combines the analysis of all objects and frames into a single parallel processing operation using the transformer attention mechanism. All object depictions across all frames are processed simultaneously in one forward pass through the network, rather than sequentially processing each object one at a time. This merging of operations maintains detection accuracy through comprehensive correlation analysis while dramatically improving processing speed by eliminating sequential bottlenecks.
Solution Approach 2:
The system performs preliminary encoding of all object depictions into tokens and prepares the complete sequence before attention-based correlation analysis. By pre-processing all inputs and organizing them into a unified token sequence, the system enables parallel attention computation that simultaneously evaluates all object relationships, thereby achieving both high detection accuracy through comprehensive analysis and high processing speed through parallel execution.
4Reliability
If comprehensive object tracking is implemented, then reliability of autonomous driving is improved, but computational resources required increase
Solution Approach 1:
The patent combines tracking of multiple objects across multiple frames into a single unified attention-based processing operation. By merging all tracking tasks into one parallel computation using the transformer model, the system achieves comprehensive and reliable tracking of all objects simultaneously while reducing total computational resource consumption compared to running separate tracking algorithms for each object independently.
Solution Approach 2:
The patent replaces traditional mechanical-style sequential tracking algorithms with a neural network-based attention mechanism. The transformer model with self-attention and cross-attention blocks automatically learns and computes object correlations through differentiable attention weights, substituting hand-crafted tracking logic with a learned model that achieves higher reliability while being more computationally efficient through parallel processing capabilities.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
The described aspects and implementations enable efficient calibration of a sensing system of a vehicle. In one implementation, disclosed is a method and a system to perform the method, the system including the sensing system configured to obtain a plurality of images associated with a corresponding time of a plurality of times. The system further includes a data processing system operatively coupled to the sensing system and configured to generate a plurality of sets of feature tensors (FTs) associated with one or more objects of the environment depicted in a respective image. The data processing system is further to obtain a combined FT and process the combined FT using a neural network to identify one or more tracks characterizing motion of a respective object.