Diffusion-Model Latent Variables for Efficient Trajectory Prediction
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Solution Overview
Problem
Existing technologies face challenges in accurately predicting the trajectories of objects in an environment for autonomous vehicles, which is crucial for safe navigation, due to limitations in processing and training machine learned models.
Innovation Solution
A system utilizing a codebook and multiple machine learned models, including a Transformer model and a Generative Adversarial Network, processes token sequences to predict object trajectories, enabling safer vehicle operations by considering potential interactions and dependencies between objects and the vehicle.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing machine learned models are used to predict object trajectories, then trajectory prediction capability is provided, but accuracy is insufficient and computational resources are excessive
Solution Approach 1:
The patent segments the trajectory prediction task into multiple discrete time steps, where the model predicts positions at each time step sequentially. This segmentation allows the model to focus on short-term predictions with higher accuracy while reducing the computational burden of predicting entire trajectories in one step. The codebook further segments the continuous trajectory space into discrete token representations, enabling efficient processing.
Solution Approach 2:
The patent changes the parameter representation from continuous coordinates to discrete token indices from a codebook. This parameter transformation compresses the information required to represent trajectories, reducing computational resources while maintaining prediction accuracy. The diffusion model operates in this discrete token space, making the computation more efficient.
2Reliability
If detailed environmental representations are used to improve safety, then decision-making quality improves, but processing complexity increases
Solution Approach 1:
The patent introduces token sequences as an intermediary representation between the raw environmental data and the trajectory predictions. The codebook acts as a mediator that maps continuous environmental features to discrete tokens, simplifying the data structure while preserving essential information for safety-critical decisions. This intermediary layer reduces processing complexity by working with compact token representations rather than raw sensor data.
Data Source
AI summary
A computing device can implement techniques for predicting an object trajectory or scene information. For example, the techniques may include inputting latent variable data into a machine learned model. The machine learned model may output an object trajectory (e.g., position data, velocity data, acceleration data, etc.) for one or more objects in the environment based on the latent variable data. The object trajectory can be sent to a vehicle computing device for consideration during vehicle planning, which may include simulation.