Traffic Object Simulation Using Hidden Context Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional techniques for autonomous vehicles fail to accurately predict the motion of non-stationary objects like pedestrians and bicyclists, leading to unnatural vehicle movements and inadequate simulation of high-risk scenarios during testing and development.
Innovation Solution
A system that uses machine learning-based models trained by human observer responses to predict the behavior of non-stationary objects by annotating traffic entities with hidden context attributes, such as intention and awareness, and generates simulation data using generative adversarial networks to improve navigation and testing of autonomous vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional kinematics-based motion prediction is used, then the system is simple and computationally efficient, but it fails to accurately predict behavior of non-stationary objects like pedestrians and bicyclists
Solution Approach 1:
The patent replaces conventional kinematics-based mechanical prediction systems with machine learning-based models that can capture complex behavioral patterns. The system uses neural networks trained on human observer responses to predict hidden context attributes, substituting simple mathematical extrapolation with sophisticated pattern recognition that accurately models human behavior without requiring explicit mechanical rules.
Solution Approach 2:
The patent introduces hidden context attributes as intermediary representations between observable motion data and predicted future behavior. These attributes (such as intention, awareness, and goal state) serve as mediators that bridge the gap between current kinematic states and future actions, enabling more accurate predictions by capturing the underlying cognitive state of non-stationary objects.
2Reliability
If simulation scenarios are created to test high-risk situations, then testing coverage improves, but the scenarios may not reflect realistic behavior of non-stationary objects
Solution Approach 1:
The patent applies preliminary action by training the machine learning models on human observer responses before deploying them in simulation scenarios. The system pre-learns realistic behavioral patterns from annotated training data, enabling it to generate authentic non-stationary object behaviors in simulated high-risk situations. This preliminary training ensures that simulation scenarios reflect real-world behavior while maintaining comprehensive test coverage.
3Reliability
If the autonomous vehicle waits for pedestrians to cross based on current motion, then safety is improved, but productivity decreases due to unnecessary waiting
Solution Approach 1:
The patent replaces simple motion-extrapolation mechanics with machine learning-based behavioral prediction. Instead of assuming pedestrians will continue their current motion trajectory, the system uses trained models to predict whether pedestrians intend to cross based on contextual cues and hidden attributes. This substitution enables the vehicle to make more informed decisions, reducing unnecessary waiting while maintaining safety.
Data Source
AI summary
A system performs modeling and simulation of non-stationary traffic entities for testing and development of modules used in an autonomous vehicle system. The system uses a machine learning based model that predicts hidden context attributes for traffic entities that may be encountered by a vehicle in traffic. The system generates simulation data for testing and development of modules that help navigate autonomous vehicles. The generated simulation data may be image or video data including representations of traffic entities, for example, pedestrians, bicyclists, and other vehicles. The system may generate simulation data using generative adversarial neural networks.