Traffic Participant Motion Prediction Using Temporary Goals
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Solution Overview
Problem
Existing technologies struggle to accurately predict the actions of pedestrians and other traffic participants in dynamic traffic situations, often relying on assumptions about known destinations and failing to adapt to real-time conditions.
Innovation Solution
A device and method that utilize a hardware processor to recognize traffic participant positions, determine temporary goals based on historical data and road structures, and simulate future movements using a movement model, estimating actions by considering virtual forces and environmental influences, while controlling vehicle travel based on predicted actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a pedestrian's goal and destination are provided as known information for estimation, then the estimation process is simplified, but the prediction accuracy deteriorates in dynamic traffic situations where actual destinations are unknown or changeable
Solution Approach 1:
The system performs preliminary simulation of multiple possible movement paths before determining the actual prediction. By pre-calculating several potential trajectories based on different temporary goals and selecting the most probable one, the system achieves accurate predictions without requiring known destination information, thus resolving the contradiction between simplification and accuracy.
2Measurement precision
If multiple temporary goal candidates are evaluated through simulation for each traffic participant, then the prediction accuracy improves, but the computational complexity increases
Solution Approach 1:
The system evaluates multiple temporary goal candidates beyond what would be minimally necessary, generating more simulation options than strictly required. This excessive action ensures that the true intended path is included in the candidates, and through selection based on deviation from simulated paths, the system achieves high accuracy while managing computational load by not exhaustively simulating all possible paths.
3Productivity
If virtual forces are estimated based on fan-shaped range limitations for each traffic participant, then the computational load is reduced, but the modeling accuracy of social interactions deteriorates
Solution Approach 1:
The system applies different processing qualities to different spatial regions by limiting virtual force estimation to a fan-shaped range in front of each traffic participant. This local approach focuses computational resources on the most relevant interaction zones where social forces are strongest, achieving a balance between processing efficiency and accuracy by not uniformly applying complex modeling to all directions.
Data Source
AI summary
A device includes a storage device configured to store a program; and a hardware processor, wherein, the hardware processor executes the program stored in the storage device to: recognize positions of a plurality of traffic participants; determine a temporary goal which each of the plurality of traffic participants is trying to reach in the future, based on the recognition results; and simulate a movement process in which each of the plurality of traffic participants moves toward the temporary goal using a movement model to estimate an action in the future of each of the plurality of traffic participants.