Action-Prior Trajectory Forecasting for Ego-Vehicle Motion Prediction
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Solution Overview
Problem
Existing trajectory forecasting models for autonomous vehicles fail to accurately predict the future actions and locations of traffic agents based on prior experiences, particularly from non-stationary camera views, lacking the ability to estimate intent and incorporate social norms and scene context.
Innovation Solution
A computer-implemented method and system that analyze image and dynamic data to classify agents, detect actions, and process ego motion history, using a neural network to predict future trajectories and ego motion of vehicles based on annotated actions and vehicle dynamics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If trajectory forecasting models use stationary camera views or overhead drone views, then the models can process visual data, but they fail to accurately predict future actions and locations of traffic agents based on prior experiences and intent estimation
Solution Approach 1:
The system performs preliminary action by detecting and annotating actions of traffic agents in current video frames before predicting future trajectories. Action detection modules identify what agents are doing now (e.g., turning, stopping, accelerating), and this action information is used as a prior to forecast future behavior, enabling the system to anticipate future actions based on current intent rather than just past positions
Solution Approach 2:
The system introduces action annotations as an intermediary between raw video data and trajectory predictions. Instead of directly mapping historical positions to future trajectories, the system uses detected actions as a mediating representation that captures the intent and immediate future direction of traffic agents, bridging the gap between observed behavior and predicted outcomes
2Measurement precision
If the system incorporates action priors and annotated actions from video data, then future trajectory prediction accuracy improves, but the system complexity and computational requirements increase
Solution Approach 1:
The system segments the complex task of trajectory prediction into distinct modular components: video data reception module, action detection module, annotation module, and trajectory prediction module. Each module performs a specific function and processes data in a structured pipeline, making the overall complex system more manageable and enabling independent optimization of each component
Solution Approach 2:
The action detection and annotation system serves multiple functions simultaneously: it detects current actions of traffic agents, annotates these actions for use in prediction, and provides intent information for future trajectory forecasting. This multi-functional approach reduces the need for separate specialized modules for each task
3Loss of information
If the system processes video data to detect and annotate agent actions, then the ability to estimate intent improves, but the processing time and computational resources increase
Solution Approach 1:
The system processes video data continuously rather than in discrete batches, maintaining a continuous stream of action detections and annotations. The video data reception module receives continuous video input, and the action detection module operates continuously to annotate agent actions in real-time, ensuring that intent information is always up-to-date without significant processing delays
Solution Approach 2:
The system performs action detection and annotation in advance of trajectory prediction, preparing intent information beforehand. By detecting and annotating actions as video frames are received, the system has action priors ready when needed for prediction, reducing the computational burden and time required during the actual trajectory forecasting phase
Data Source
AI summary
A system and method for future forecasting using action priors that include receiving image data associated with a surrounding environment of an ego vehicle and dynamic data associated with dynamic operation of the ego vehicle. The system and method also include analyzing the image data to classify dynamic objects as agents and to detect and annotate actions that are completed by the agents that are located within the surrounding environment of the ego vehicle and analyzing the dynamic data to process an ego motion history that is associated with the ego vehicle that includes vehicle dynamic parameters during a predetermined period of time. The system and method further include predicting future trajectories of the agents located within the surrounding environment of the ego vehicle and a future ego motion of the ego vehicle within the surrounding environment of the ego vehicle based on the annotated actions.