In-Vehicle Scenario Extraction for Privacy-Safe AV Simulation
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Solution Overview
Problem
Current approaches for collecting and analyzing critical scenarios in autonomous driving are limited by high costs, restricted data representation, and privacy concerns, as they often rely on specialized vehicles and sensors that capture limited and biased data, failing to effectively regenerate real-world driving scenarios for simulation and algorithm testing.
Innovation Solution
A lightweight in-vehicle critical scenario extraction system that detects and extracts essential scenario elements in real-time, removes privacy-sensitive information, and uploads data to a cloud server for simulation, enabling the creation of a diverse and representative scenario library for autonomous vehicle training and safety parameter adjustment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If specialized vehicles and sensors are used to capture critical scenarios, then measurement precision and reliability are improved, but device complexity and cost increase
Solution Approach 1:
The patent uses standard consumer-grade sensors to capture raw data, then creates simplified copies of critical scenario elements through extraction and reconstruction processes. The system captures full-scene data but only extracts and transmits essential scenario elements (positions, velocities, trajectories) to the cloud, avoiding the need for specialized expensive sensors while maintaining measurement precision for critical parameters.
Solution Approach 2:
The system extracts only the essential scenario elements from raw sensor data - specifically positions, velocities, and trajectories of vehicles and pedestrians - separating these critical parameters from the full raw data stream. This extraction approach maintains measurement precision for safety-critical parameters while reducing overall data complexity and transmission requirements.
2Loss of information
If all raw sensor data is stored and transmitted, then data completeness is improved, but storage and bandwidth requirements increase
Solution Approach 1:
The system extracts only essential scenario elements (positions, velocities, trajectories of relevant objects) from the complete raw sensor data, maintaining the critical information needed for safety analysis while dramatically reducing data volume. The extraction process identifies and isolates only the data elements necessary for reconstructing critical scenarios.
Solution Approach 2:
The patent segments the complete sensor data stream into distinct critical scenario elements (vehicle positions, pedestrian trajectories, collision parameters) that can be processed and transmitted separately. This segmentation allows the system to maintain data completeness for safety-critical parameters while excluding redundant information, reducing overall data volume for storage and transmission.
3Measurement precision
If detailed scenario data is collected, then simulation accuracy is improved, but privacy protection becomes more difficult
Solution Approach 1:
The system extracts only the essential geometric and kinematic parameters (positions, velocities, trajectories) needed for scenario reconstruction, deliberately excluding personal identifiable information such as license plate numbers, facial features, and other privacy-sensitive details. This extraction approach maintains simulation accuracy while inherently protecting privacy.
Solution Approach 2:
The patent creates simplified anonymous copies of real-world scenarios by reconstructing scenario elements from extracted parameters rather than using original detailed data. These reconstructed scenarios preserve the critical safety-relevant characteristics needed for simulation and training while replacing identifiable personal information with anonymized geometric representations.
Data Source
AI summary
Various aspects of methods, systems, and use cases for critical scenario identification and extraction from vehicle operations are described. In an example, an approach for lightweight analysis and detection includes capturing data from sensors associated with (e.g., located within, or integrated into) a vehicle, detecting the occurrence of a critical scenario, extracting data from the sensors in response to detecting the occurrence of the critical scenario, and outputting the extracted data. The critical scenario may be specifically detected based on a comparison of the operation of the vehicle to at least one requirement specified by a vehicle operation safety model. Reconstruction and further data processing may be performed on the extracted data, such as with the creation of a simulation from extracted data that is communicated to a remote service.