Vehicle Sensor Fusion for Collision Confidence and Severity Detection
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Solution Overview
Problem
Current collision detection systems in vehicles suffer from high false positive rates, inefficient cloud storage, and time-intensive human review processes for accident reconstruction.
Innovation Solution
The implementation of sensor fusion and logic-based algorithms for determining collision confidence, combined with edge computing and automated report generation, to enhance collision detection accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If individual sensors are used for collision detection, then the system is simple, but false positive collision detection increases
Solution Approach 1:
The patent combines multiple sensor types (accelerometers, gyroscopes, GPS receivers, cameras, radar sensors, ultrasonic sensors, and IMUs) into an integrated sensor fusion system. This merging of sensors allows cross-validation of collision signals, where multiple sensors must detect abnormal events simultaneously to confirm a collision, thereby reducing false positives while maintaining system reliability.
Solution Approach 2:
The system employs multiple sensors that serve both collision detection and other functions (navigation, environmental monitoring, vehicle control). For example, GPS and IMUs are used for both collision detection and vehicle positioning/orientation, while cameras and radar serve both collision detection and environmental awareness, optimizing resource utilization.
2Loss of information
If cloud storage is used for sensor data, then data accessibility is improved, but storage efficiency and cost increase
Solution Approach 1:
The patent extracts and processes only the most critical collision-related data locally using edge computing devices in the vehicle, filtering out unnecessary information before transmission. This selective extraction reduces the volume of data requiring cloud storage while preserving essential collision evidence, thereby improving storage efficiency and reducing costs.
Solution Approach 2:
The system performs preliminary data processing, filtering, and analysis locally in the vehicle before data is transmitted to the cloud. Edge computing devices pre-process sensor data to identify and extract only relevant collision information, reducing the burden on cloud storage infrastructure and minimizing transmission costs.
3Measurement precision
If human experts review sensor data for accident reconstruction, then analysis accuracy is improved, but time consumption increases
Solution Approach 1:
The system implements automated algorithms and AI models that independently analyze sensor data, detect collisions, determine severity, and generate accident reports without requiring manual human review. This self-service capability significantly reduces processing time while maintaining high accuracy through sophisticated computational analysis of multi-sensor data.
Solution Approach 2:
The patent replaces the mechanical process of human expert review with automated computational systems including machine learning models and algorithms that process sensor data. This substitution eliminates human time constraints and provides consistent, rapid analysis while maintaining or improving accuracy through systematic data evaluation.
4Reliability
If sensor fusion algorithms are implemented, then collision detection accuracy is improved, but device complexity increases
Solution Approach 1:
The patent divides the complex sensor fusion processing into separate functional modules: collision detection algorithms, severity determination algorithms, and automated report generation systems. Each module handles specific aspects of data processing independently, making the overall system more manageable and maintainable while achieving high detection accuracy through coordinated module operation.
Data Source
AI summary
The present disclosure discloses systems and methods that can be implemented on the edge system in a vehicle for near real-time detection of collisions with multi-level confidence (i.e., low confidence, moderate confidence, high confidence), automated gathering and report generation of collision information required for post-accident analysis in a manner suitable for insurance company process workflow and storing the confidential collision details securely on the vehicle (edge) system. Confidence is determined by preconfigured adaptive thresholds measured by sensor data.