Pedestrian Motion Prediction Using Map and Social Context
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Solution Overview
Problem
Existing methods for predicting pedestrian velocity vectors and poses in self-driving vehicles do not adequately consider future changes in pedestrian movement due to interactions with stationary obstacles and social dynamics, leading to potential collisions.
Innovation Solution
A method that incorporates a static map of the prediction area, current velocity vectors of other pedestrians, and social interactions to predict future pedestrian movements, grouping pedestrians and accounting for their destinations and social behaviors to enhance prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing methods only use current pedestrian position and velocity vector for prediction, then the prediction process is simple and fast, but the prediction accuracy is insufficient leading to potential collisions
Solution Approach 1:
The prediction process is segmented into multiple independent modules: a map module that processes static environmental data, a social context module that analyzes pedestrian interactions, and a prediction module that integrates these factors. This segmentation allows each module to specialize in specific aspects of prediction while maintaining overall system manageability and accuracy.
Solution Approach 2:
The system performs preliminary processing of map data and social context information before the actual prediction occurs. The map module pre-processes static environmental features, and the social context module pre-identifies interaction patterns, so that when prediction is needed, these pre-computed data are readily available to enhance accuracy without adding computational burden during critical prediction moments.
2Reliability
If the prediction considers social interactions and environmental factors, then collision avoidance capability is improved, but the computational time and processing load increase
Solution Approach 1:
The system applies different levels of analysis to different aspects of pedestrian behavior. Social interactions and environmental factors are considered with appropriate detail only where relevant to the specific prediction scenario, rather than uniformly applying complex analysis to all cases. This local quality approach maintains high reliability for critical predictions while reducing unnecessary computational overhead.
3Measurement precision
If pedestrians are tracked individually with full social context analysis, then prediction accuracy is improved, but the system complexity and data processing requirements increase significantly
Solution Approach 1:
The map module and social context module are merged into an integrated prediction framework where static environmental data and dynamic social interaction data are combined and processed together. This merging allows the system to leverage correlations between environmental features and social behaviors, improving prediction accuracy while avoiding the redundancy of separate independent analysis systems.
Data Source
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AI summary
The invention relates to a method for predicting (30) at least one future velocity vector and/or a future pose of a pedestrian in an area of prediction. A map of the surroundings of the pedestrian and current velocity vectors of other pedestrians in the area of prediction are taken into account for the prediction (30).