Vehicle Object Association Using Geo-Motion and Appearance Embeddings
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Solution Overview
Problem
Existing autonomous vehicle tracking systems rely heavily on priors that may be incorrect, leading to unreliable predictions, and often ignore geometry and motion information, resulting in unstable and incomplete object association.
Innovation Solution
A system that uses geo-motion embeddings and appearance embeddings processed by neural networks to associate new object measurements with maintained tracks, leveraging both geometric and motion information, and appearance data without relying on prior distributions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If existing tracking systems rely heavily on priors for object association, then the association process can be simplified, but the reliability of predictions deteriorates due to incorrect priors
Solution Approach 1:
The patent replaces traditional probabilistic data association methods (mechanical/system-based approaches relying on priors) with deep learning models that directly learn object association from data. The neural networks process geo-motion embeddings and appearance embeddings to predict object identities without relying on incorrect prior distributions, thereby improving prediction reliability while maintaining computational feasibility.
Solution Approach 2:
The patent changes the fundamental parameters used for object association from prior-based probabilistic models to learned embeddings from neural networks. By transforming the association problem into a parameter space defined by geo-motion and appearance embeddings processed through deep learning, the system achieves more reliable predictions without being constrained by incorrect prior assumptions.
2Device complexity
If tracking systems ignore geometry and motion information, then the processing complexity is reduced, but the completeness and stability of object association deteriorates
Solution Approach 1:
The patent merges multiple information sources including geometry (from top-down rendered images showing bounding boxes and motion directions), motion (velocity and heading information), and appearance (visual characteristics) into unified embeddings. The geo-motion embedding neural network processes geometric and motion data together, while the appearance embedding neural network processes visual appearance, and both are combined for final object association, achieving stable and complete tracking.
Solution Approach 2:
The patent adds geometric and motion dimensions to the object representation by creating top-down rendered images that encode position, velocity, and heading information. This transforms the association problem from a 2D appearance-matching task into a multi-dimensional problem that incorporates spatial, temporal, and kinematic information, thereby improving association stability and completeness.
3Ease of manufacture
If only appearance information is used for data association, then the system is easier to implement, but the accuracy and robustness of object tracking deteriorates
Solution Approach 1:
The patent merges appearance information processed by one neural network with geo-motion information processed by another neural network. The appearance embedding neural network extracts visual characteristics while the geo-motion embedding neural network extracts geometric and motion features, and both embeddings are combined in the loss function to achieve accurate and robust object identification that leverages multiple complementary information sources.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for associating a new measurement of an object surrounding a vehicle with a maintained track. One of the methods includes receiving an object track for a particular object, receiving a new measurement characterizing a new object at a new time step, and determining whether the new object is the same as the particular object, comprising: generating a representation of the new object at the new and preceding time steps; generating a representation of the particular object at the new and preceding time steps; processing a first network input comprising the representations using a first neural network to generate an embedding of the first network input; and processing the embedding of the first network input using a second neural network to generate a predicted likelihood that the new object and the particular object are the same.