Goal-Based Motion Forecasting for Multi-Actor Traffic Prediction
Find Innovative SolutionsGenerate Solutions
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.
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.
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/mathematical motion prediction models with a machine-learned forecasting model that processes state data and map data to generate probabilistic predictions. This substitution enables higher prediction accuracy through learned patterns from training data, while the modular architecture (encoder, message passing, decoder) manages the increased complexity systematically
Solution Approach 2:
The patent introduces an intermediary machine-learned model that acts as a bridge between raw sensor inputs and motion planning decisions. This intermediary processes state data and map data through multiple layers (encoder, message passing, decoder) to produce both discrete intent and continuous trajectory predictions, resolving the contradiction by adding computational complexity only where needed for accuracy
2Productivity
If simple prediction models are used, then the processing speed is fast, but the decision-making efficiency is reduced
Solution Approach 1:
The patent segments the prediction process into distinct functional modules: an encoder that processes inputs, a message passing mechanism that propagates information, and a decoder that generates outputs. This segmentation allows parallel processing of multiple actors and goals simultaneously, improving decision-making efficiency without sacrificing processing speed through optimized computational flow
Solution Approach 2:
The patent performs preliminary action by pre-processing state data and map data through the encoder to extract relevant features before the actual prediction occurs. The message passing mechanism pre-computes interaction potentials between actors and goals, so when predictions are needed, the system can quickly generate accurate results, thereby improving decision-making efficiency without significant processing overhead
3Reliability
If probabilistic goal-based predictions are generated, then the motion planning robustness is improved, but the computational complexity increases
Solution Approach 1:
The patent applies dynamics by generating multiple probabilistic predictions for different potential goals rather than a single deterministic outcome. The system dynamically adjusts prediction probabilities based on learned patterns and current scene context, allowing robust motion planning that accounts for uncertainty. The modular architecture manages computational complexity by processing each goal independently through the same encoder-decoder pipeline
4Measurement precision
If detailed state data and map data are processed, then the forecasting accuracy is improved, but the data processing time increases
Solution Approach 1:
The patent extracts only the most relevant features from detailed state data and map data through the encoder module, which processes actor positions, velocities, and map geometry to generate condensed feature representations. This extraction maintains forecasting accuracy by preserving critical information while reducing data dimensionality, thereby decreasing processing time without significant accuracy loss
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 include 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.