Motion Forecasting for Autonomous Vehicles Using Attention Embeddings
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately predicting the trajectories of multiple actors in complex environments, such as intersections and lane changes, due to the complexity of interactions between various third-party actors, which can lead to unsafe navigation.
Innovation Solution
A computing system that includes an object detection model, an interaction model, and a recurrent model to generate motion forecast data by analyzing sensor data, object detection data, and attention embeddings, allowing for the prediction of trajectories of multiple actors and improving navigation safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex interaction models are used to predict actor trajectories in complex environments, then prediction accuracy is improved, but computational time and resources increase
Solution Approach 1:
The system segments the trajectory prediction task into multiple independent models: object detection model for identifying actors, interaction model for analyzing actor relationships, and recurrent model for predicting trajectories. This segmentation allows parallel processing of different aspects, improving both accuracy through specialized models and computational efficiency through distributed computation.
Solution Approach 2:
The system applies partial action by selectively processing only the most relevant actor interactions using attention mechanisms. The interaction model identifies key actor pairs that have significant influence on each other's trajectories, and the recurrent model focuses computational resources on predicting trajectories of actors involved in complex interactions, rather than uniformly processing all actors equally.
2Reliability
If multiple models are used to analyze actor interactions, then navigation safety is improved, but device complexity increases
Solution Approach 1:
The navigation safety function is segmented across three specialized models: object detection for actor identification, interaction modeling for relationship analysis, and recurrent prediction for trajectory forecasting. Each model handles a specific aspect of safety analysis, improving overall reliability while managing complexity through functional decomposition.
Solution Approach 2:
The recurrent model serves multiple functions: it processes output from the interaction model, generates trajectory predictions for multiple actors simultaneously, and provides inputs for navigation decision-making. This multi-functionality reduces the need for separate specialized components, managing system complexity while maintaining comprehensive safety analysis.
3Measurement precision
If detailed object detection data is processed, then trajectory prediction accuracy is improved, but computational resources required increase
Solution Approach 1:
The system extracts only the most relevant features from object detection data using the interaction model. Instead of processing all detected object attributes uniformly, the interaction model identifies and extracts key interaction features between actor pairs that have significant influence on trajectory predictions, reducing computational resource requirements while maintaining prediction accuracy.
Solution Approach 2:
The system applies local quality by processing object detection data with varying levels of detail depending on the actor's relevance. Actors involved in complex interactions receive more detailed processing through the interaction and recurrent models, while less relevant actors are processed with reduced detail, optimizing the balance between accuracy and computational resource usage.
Data Source
AI summary
A computing system can be configured to input data that describes sensor data into an object detection model and receive, as an output of the object detection model, object detection data describing features of the plurality of the actors relative to the autonomous vehicle. The computing system can generate an input sequence that describes the object detection data. The computing system can analyze the input sequence using an interaction model to produce, as an output of the interaction model, an attention embedding with respect to the plurality of actors. The computing system can be configured to input the attention embedding into a recurrent model and determine respective trajectories for the plurality of actors based on motion forecast data received as an output of the recurrent model.