Unified Object Tracking and State Prediction Neural Network
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Solution Overview
Problem
Conventional methods for object tracking and state prediction in autonomous vehicles perform these tasks separately, leading to less accurate results and more complex architectures, whereas the described system jointly performs both tasks using query representations to improve control and navigation planning.
Innovation Solution
The system uses an object tracking system that processes scene context data to maintain and update object tracks, jointly performing object tracking and state prediction by generating query representations for object tracks, which encode information useful for both tasks, allowing for end-to-end training and a simplified architecture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If object tracking and state prediction are performed separately using conventional methods, then the system architecture can be modular and easier to implement individually, but the overall accuracy of tracking and prediction deteriorates and the system complexity increases
Solution Approach 1:
The patent combines object tracking and state prediction into a single unified neural network model. The tracker encoder processes object detections and maintains object tracks while simultaneously generating queries for the state prediction decoder, which predicts future states. This merging eliminates the need for separate processing pipelines and enables end-to-end training, resolving the contradiction by achieving both high accuracy and simplified architecture through integration.
Solution Approach 2:
The unified model performs multiple functions within a single architecture: object tracking, state estimation, and future state prediction. The tracker encoder serves both tracking and prediction tasks by generating queries that are processed by the state prediction decoder. This multi-functionality allows the system to achieve accurate tracking and prediction simultaneously while avoiding the complexity of multiple separate systems.
2Measurement precision
If object tracking and state prediction are performed separately, then each module can be optimized independently, but the interdependence between tracking and state estimation is lost leading to less accurate results
Solution Approach 1:
The patent merges the tracking and state prediction pipelines into a unified architecture where the tracker encoder and state prediction decoder work together. The encoder processes object detections and generates queries that are simultaneously used for tracking and state prediction. This combining enables the system to capture the interdependence between tracking and state estimation, improving accuracy while maintaining a streamlined processing pipeline through shared computational components.
3Device complexity
If a unified model is used to jointly perform object tracking and state prediction, then the architecture is simplified and training is more efficient, but the model must handle multiple tasks simultaneously which increases computational demands
Solution Approach 1:
The unified model achieves multi-functionality by using a single tracker encoder to serve both tracking and state prediction tasks. The encoder processes object detections and generates queries that are fed to the state prediction decoder. This approach simplifies the overall architecture compared to separate models while managing computational demands through shared feature extraction and processing, reducing redundant computations across multiple independent systems.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for tracking objects in an environment across time. In one aspect, a method comprises: receiving a set of current object detections, each characterizing features of a respective detected object; maintaining data, including track query feature representations, that identifies one or more object tracks (each associated with respective earlier object detections classified as characterizing the same object; and, for each object track: (i) selecting a subset of the current object detections as candidate object detections for the object track, (ii) generating a respective association score for each candidate object detection based on an input derived from the candidate object detections and the track query feature representation for the object track using a track-detection interaction neural network, and (iii) determining whether to associate any of the current object detections with the object track based on the respective association scores.