Transformer Trajectory Prediction Without Multi-Object Tracking

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Solution Overview

Problem

Existing multi-object trajectory prediction systems are resource-intensive and prone to tracking errors due to noise in large-scale datasets, leading to inaccurate predictions.

Innovation Solution

Utilizing a transformer neural network with an encoder and decoder architecture to perform trajectory predictions based on object affinities and latent variables, enabling accurate trajectory forecasting without explicit tracking, by incorporating an attention mechanism to focus on relevant data sequences.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If multi-object trajectory prediction is performed using traditional tracking methods, then trajectory accuracy can be maintained, but resource consumption increases significantly

Engineering Contradiction:

Improvetrajectory prediction accuracyVSAvoidresource consumption

Core Design Contradiction:

ReliabilityVSUse of energy by moving object

Solution Approach 1:

The patent extracts and removes the explicit tracking step from the traditional trajectory prediction pipeline. By directly predicting trajectories from detections without intermediate tracking, the system eliminates the computational overhead of maintaining and updating track states while avoiding error propagation from noisy tracking data

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The patent segments the trajectory prediction task into object-specific predictions rather than processing all objects as a unified tracking problem. Each object's trajectory is predicted independently based on its detections, allowing for more efficient resource utilization while maintaining accuracy

Inventive Principle:

Principle #1Segmentation

2Reliability

If large-scale datasets are used for tracking to improve prediction accuracy, then more comprehensive data is available, but tracking errors propagate and accumulate

Engineering Contradiction:

Improveprediction accuracyVSAvoidtracking error propagation

Core Design Contradiction:

ReliabilityVSLoss of information

Solution Approach 1:

The patent performs trajectory prediction directly from detection data without requiring preliminary tracking. By predicting trajectories in advance based on detection sequences rather than relying on tracked state estimates, the system avoids accumulating errors from imperfect tracking while still utilizing comprehensive detection data

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent introduces a direct prediction mechanism that acts as an intermediary between detections and trajectory output, bypassing the tracking component that causes error propagation. This intermediary prediction layer transforms detection data directly into trajectory predictions without the intermediate tracked state that accumulates errors

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS12548331B2Techniques to perform trajectory predictions

Publication Date: 2026.02.10 NVIDIA CORP

AI summary

Apparatuses, systems, and techniques to perform trajectory predictions within one or more images. In at least one embodiment, a processor comprises one or more circuits to cause one or more neural networks to perform trajectory predictions of two or more objects detected within a plurality of frames without tracking the two or more objects based, at least in part, on processing a sequence of data of the one or more objects as a whole.