Traffic Scene Prediction System with Selective Module Activation
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Solution Overview
Problem
Traditional traffic scene prediction methods, especially those using multi-modal predictions, face high computational effort and limited meaningfulness due to calculating all possible trajectories for each traffic participant without considering interactions, leading to inefficient and conditionally useful predictions for automated vehicles.
Innovation Solution
A computer-implemented system with a multi-stage architecture that aggregates scene-specific information, generates a feature set using a backbone network, evaluates different modes for future developments with a classifier, and activates only relevant prediction modules to provide meaningful predictions, considering interactions and reducing computational effort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multi-modal prediction calculates all possible trajectories for each traffic participant, then prediction completeness is improved, but computational effort increases significantly
Solution Approach 1:
The prediction system is segmented into two independent stages: a perception level that aggregates scene information and a prediction level that generates trajectories. This segmentation allows the perception level to process all scene data once, and then the prediction level to generate multiple trajectories based on this aggregated information, reducing redundant computations while maintaining prediction completeness.
Solution Approach 2:
The patent merges the perception and prediction levels into a unified architecture where the perception level outputs aggregated scene information that is directly used by the prediction level. This combining approach ensures that trajectory predictions are generated based on comprehensive scene understanding without requiring separate processing of scene information for each trajectory, thereby reducing computational effort while maintaining prediction completeness.
2Adaptability or versatility
If multi-modal prediction generates trajectories for each traffic participant independently, then prediction coverage is improved, but interaction between traffic participants is ignored
Solution Approach 1:
The patent combines independent trajectory generation with interaction consideration by using aggregated scene information from the perception level as input to the prediction level. This ensures that trajectory predictions for multiple traffic participants are generated simultaneously based on the overall scene context, allowing interactions between participants to be captured while maintaining comprehensive prediction coverage.
Solution Approach 2:
The aggregated scene information from the perception level acts as an intermediary that bridges independent trajectory generation and interaction consideration. This intermediary representation captures the overall scene state and is used to generate trajectories that reflect interactions between traffic participants, thereby maintaining prediction coverage while incorporating interaction dynamics.
3Productivity
If traditional kinematics/dynamics prediction is used, then computational effort is reduced, but prediction time horizon is limited to less than 2 seconds
Solution Approach 1:
The patent replaces traditional kinematics/dynamics-based prediction with a machine learning-based prediction system. This substitution allows the system to achieve longer prediction time horizons by using learned patterns from data rather than relying on physical models that are limited by computational complexity and time constraints, while maintaining computational efficiency through the optimized neural network architecture.
Data Source
AI summary
A computer-implemented system for predicting future developments of a traffic scene is proposed, with which a high significance of the prediction can be achieved and the computational effort for the prediction can be limited. For this purpose, the system includes a perception level for aggregating scene-specific information of an input scene, a backbone network for generating a feature set of latent features based on the scene-specific information, a classifier evaluating a specified number of different modes for the future developments of the input scene based on the feature set, and for each mode, a prediction module for generating a prediction for the future development of the input scene, wherein at least one prediction module can optionally be activated.