Actor Motion Forecasting with Interaction Transformers for Autonomous Vehicles
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Solution Overview
Problem
Autonomous vehicles face challenges in predicting the complex interactions and trajectories of multiple actors in their environment, such as vehicles and pedestrians, which complicates safe navigation and decision-making.
Innovation Solution
A computing system utilizing an interaction transformer model and prediction model to generate motion forecast data based on relative location embeddings, iteratively refining trajectory predictions to account for anticipated interactions between actors, incorporating sensor data and map information to enhance navigation and safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex interaction models are used to predict actor trajectories, then prediction accuracy is improved, but computational complexity increases
Solution Approach 1:
The system segments the trajectory prediction task into two distinct components: an interaction transformer model that processes relative location embeddings to generate motion forecast data, and a prediction model that converts motion forecasts into trajectories. This segmentation allows each model to specialize in specific aspects of the prediction task, improving overall accuracy while managing computational complexity through modular design.
Solution Approach 2:
Motion forecast data serves as an intermediary between the interaction transformer model and the prediction model. The interaction transformer model generates motion forecasts based on relative location embeddings, which then feed into the prediction model to produce final trajectories. This intermediary representation simplifies the computational flow and enables more efficient processing.
2Measurement precision
If iterative refinement of trajectory predictions is performed, then prediction accuracy is improved, but computational resources increase
Solution Approach 1:
The system performs preliminary action by generating motion forecast data before final trajectory prediction. The interaction transformer model creates motion forecasts that capture interaction dynamics in advance, which then guide the prediction model to generate more accurate trajectories with fewer iterative refinements needed, reducing overall computational resource consumption.
3Reliability
If relative location embeddings are used to capture actor interactions, then interaction modeling is improved, but data processing complexity increases
Solution Approach 1:
The system transforms raw relative location data into relative location embeddings through parameter changes in the interaction transformer model. This transformation process converts spatial relationships into a standardized embedding format that captures interaction dynamics effectively, improving reliability while managing data processing complexity through consistent transformation rules.
Data Source
AI summary
A computing system can input first relative location embedding data into an interaction transformer model and receive, as an output of the interaction transformer model, motion forecast data for actors relative to a vehicle. The computing system can input the motion forecast data into a prediction model to receive respective trajectories for the actors for a current time step and respective projected trajectories for the actors for a subsequent time step. The computing system can generate second relative location embedding data based on the respective projected trajectories from the second time step. The computing system can produce second motion forecast data using the interaction transformer model based on the second relative location embedding. The computing system can determine second respective trajectories for the actors using the prediction model based on the second forecast data.