Automated Comparative Negligence Assessment Using Multi-Sensor Accident Reconstruction
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Solution Overview
Problem
Existing methods for assessing comparative negligence in vehicle accidents are inefficient and prone to human error, particularly in cases involving autonomous vehicles and smart city infrastructure, as they rely heavily on human judgment and lack adequate guidelines for assigning blame.
Innovation Solution
A system and method that utilize data from various vehicle sensors, such as cameras, accelerometers, and GPS units, to automatically detect accidents, determine accident types, and generate comparative negligence assessments by comparing scenarios with parametrized accident guidelines, thereby minimizing human judgment and error.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human judgment is used to assess comparative negligence, then flexibility in decision-making is maintained, but accuracy and consistency are reduced due to human error
Solution Approach 1:
The patent replaces the mechanical system of human judgment with an automated electronic system that collects data from multiple vehicle sensors (accelerometers, gyroscopes, magnetometers, cameras, radar, lidar) and processes this data through algorithms to determine comparative negligence. This substitution eliminates human error while maintaining systematic analysis through digital data processing and machine learning models.
2Reliability
If automated systems are implemented to assess negligence, then accuracy and consistency are improved, but the extent of automation increases system complexity
Solution Approach 1:
The patent creates a multi-functional automated system that performs multiple tasks: collecting data from various sensors, detecting accidents, determining accident types, reconstructing vehicle trajectories, and calculating comparative negligence percentages. This universal system handles diverse accident scenarios (collisions, pedestrian accidents, property damage) through a single integrated platform, improving consistency across different cases while managing complexity through modular architecture.
Solution Approach 2:
The system changes parameters by processing raw sensor data (acceleration, velocity, position) into meaningful metrics for negligence assessment. It transforms multiple data streams into standardized parameters such as collision force, reaction time, and trajectory deviation, which are then used to determine fault percentages. This parameter transformation enables consistent automated decision-making across varied accident scenarios.
3Measurement precision
If multiple sensor data sources are integrated, then measurement precision of accident reconstruction is improved, but device complexity increases
Solution Approach 1:
The patent merges data from multiple independent sensor sources (accelerometers, gyroscopes, magnetometers, cameras, radar, lidar, GPS) into a unified accident reconstruction model. By combining these diverse data streams, the system achieves comprehensive three-dimensional reconstruction of accident scenarios with high precision, cross-validating information across sensors to improve accuracy while managing integration complexity through standardized data processing protocols.
4Loss of time
If automated assessment is used, then time consumption is reduced, but the need for sophisticated processing algorithms increases complexity
Solution Approach 1:
The system performs preliminary actions by continuously collecting and pre-processing sensor data during normal vehicle operation, so that when an accident occurs, the data is already organized and ready for immediate analysis. Accident detection algorithms are pre-trained on extensive datasets, enabling rapid automated assessment without requiring complex real-time computations during the critical post-accident investigation phase, thus reducing time loss while managing algorithmic complexity through advance preparation.
Data Source
AI summary
A system and a method for automatic assessment of comparative negligence of vehicle(s) involved in an accident. The system receives one or more of a video input, an accelerometer data, a gyroscope data, a magnetometer data, a GPS data, a Lidar data, a Radar data, a radio navigation data and a vehicle state data for vehicle(s). The system automatically detects an occurrence of an accident and its timestamp. The system then detects an accident type of the accident, and a trajectory of the vehicle(s) based on the received data for the detected timestamp. A scenario of the accident is generated and compared with a parametrized accident guideline to generate a comparative negligence assessment for the vehicle(s) involved in the accident.