Goal-Based Motion Forecasting With GNN Intent and Trajectory Prediction
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Solution Overview
Problem
Current autonomous systems face challenges in accurately predicting the motion of dynamic objects in traffic scenes, leading to inefficiencies in motion planning and decision-making for autonomous platforms.
Innovation Solution
A machine-learned forecasting model, specifically a graph neural network, is trained to predict the future behavior of actors in a traffic scene by processing state and map data, generating discrete intent and continuous motion outputs, including goal probabilities, interaction probabilities, and trajectories, to improve motion planning accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional motion prediction methods are used, then the system complexity is low, but the prediction accuracy and reliability are insufficient
Solution Approach 1:
The patent replaces traditional mechanical prediction methods with a machine-learned graph neural network model. The GNN processes actor observations and map geometry to generate discrete intent and continuous motion outputs, achieving superior prediction accuracy while managing system complexity through neural network architecture.
Solution Approach 2:
The patent introduces a graph neural network as an intermediary between raw sensor data and motion planning decisions. The GNN transforms actor observations and map geometry into meaningful predictions of future actor positions and interactions, serving as a bridge that enhances prediction reliability.
2Measurement precision
If complex machine-learned models are deployed, then the forecasting accuracy improves, but the computational speed and efficiency decrease
Solution Approach 1:
The patent segments the motion forecasting task into discrete components: the GNN generates separate outputs for discrete intent (goal probability, interaction probability) and continuous motion (trajectory). This segmentation allows parallel processing and optimization of different prediction aspects, improving overall computational efficiency.
Solution Approach 2:
The patent performs preliminary processing by generating discrete intent predictions (goal probability, interaction probability) before final trajectory generation. This preliminary classification of actor intentions allows the system to focus computational resources on relevant prediction aspects, improving speed without sacrificing accuracy.
3Reliability
If more actor observations and map geometry data are processed, then the prediction reliability improves, but the data processing time increases
Solution Approach 1:
The patent creates a universal GNN model that simultaneously processes multiple data types (actor observations, map geometry) and generates multiple prediction outputs (discrete intent, continuous motion) in a single unified framework. This multi-functionality reduces redundant processing and improves efficiency.
Solution Approach 2:
The patent transforms raw actor observations and map geometry into standardized feature representations suitable for GNN processing. By changing the parameter representation of input data into appropriate formats, the model can efficiently process diverse information without excessive computation time.
Data Source
AI summary
Example aspects of the present disclosure relate to an example computer-implemented method for predicting the intent of actors within an environment. The example method includes obtaining state data associated with a plurality of actors within the environment and map data indicating a plurality of lanes of the environment. The method includes determining a plurality of potential goals each actor based on the state data and the map data. The method includes processing the state data, the map data, and the plurality of potential goals with a machine-learned forecasting model to determine (i) a forecasted goal for a respective actor of the plurality of actors, (ii) a forecasted interaction between the respective actor and a different actor of the plurality of actors based on the forecasted goal, and (iii) a continuous trajectory for the respective actor based on the forecasted goal.