Simulated Agent Feature Extraction for Unmanned Vehicle Testing
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Solution Overview
Problem
Existing methods for simulating traffic scenarios for unmanned vehicle testing rely on manually defined feature information, which may not accurately represent real scenarios, leading to incorrect test results due to incomplete or unrealistic agent characteristics.
Innovation Solution
A method and apparatus that obtain feature information from real agents participating in traffic activities, extracting representative attribute and behavior information to create more accurate simulated agents, allowing for dynamic tracking and user-modifiable playback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manually-defined feature information is used for simulated agents, then the development efficiency is improved, but the correctness of test results deteriorates
Solution Approach 1:
The patent copies feature information from real agents in traffic scenarios to create simulated agents. Instead of manually defining agent features, the system automatically extracts and replicates characteristics from real-world agents (vehicles, pedestrians, cyclists) to populate the simulation environment, ensuring the simulated agents accurately reflect real traffic patterns
Solution Approach 2:
The patent replaces the manual mechanical process of defining agent features with an automated data extraction and analysis system. The system automatically collects, processes, and extracts feature information from real agents using computational methods, substituting human manual work with automated information processing
2Ease of operation
If manually-selected feature information is used, then the ease of operation is improved, but the measurement precision of agent characteristics deteriorates
Solution Approach 1:
The system performs self-service by automatically extracting feature information from real agents without requiring manual intervention. The automated extraction process independently identifies and captures relevant agent characteristics, eliminating the need for operators to manually select or define features while maintaining high accuracy
3Reliability
If real agents are used to extract feature information, then the reliability of simulated agents is improved, but the loss of time in data collection increases
Solution Approach 1:
The patent applies preliminary action by collecting and storing feature information from real agents in advance before the actual simulation testing begins. This pre-collection approach allows the system to have a ready pool of authentic agent data, eliminating the need for real-time data collection during testing and reducing time loss
Data Source
AI summary
The present invention discloses a method and apparatus of obtaining feature information of a simulated agent. The method further comprises: for each agent class, respectively obtaining feature information of real agents belonging to the class and freely participating in traffic activities, the number of real agents belonging to each class being greater than one; for each agent class, extracting representative feature information from feature information of each real agent belonging to the class, and taking the extracted feature information as feature information of simulated agents belonging to this class. The solution of the present invention may be applied to improve correctness of testing results of unmanned vehicles.