Deep Learning Liability Assessment for Car Accident Video
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Solution Overview
Problem
Current methods for determining split liability in car accidents are subjective and time-consuming, often requiring legal intervention and significant resources, as they fail to consider all environmental factors and can be influenced by personal opinions, leading to inefficiencies in processing and reliability.
Innovation Solution
A deep learning-based method and apparatus for split liability assessment using car accident video data, which involves training a split liability determination model with learning data to output objective split liability information, incorporating time and space information, and considering driver gaze direction to calculate negligence intention scores, thereby providing a more reliable and efficient assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If split liability is determined by agreement of insurance companies' staffs and concerned persons, then the process is simple and quick, but the determination is subjective and unreliable as various environmental factors cannot be considered
Solution Approach 1:
The patent introduces an automated liability determination system as an intermediary between the subjective assessment by insurance staff and the final liability decision. This system processes video data from multiple angles, extracts environmental factors (traffic lights, lane markings, road signs), and applies rules-based logic to objectively determine split liability, thereby improving reliability while maintaining processing efficiency
Solution Approach 2:
The patent replaces the manual, subjective mechanical process of liability assessment with an automated computational system. The system uses video processing algorithms to detect environmental factors and automatically calculates split liability based on predefined rules, eliminating human subjectivity while maintaining quick processing
2Reliability
If split liability is determined by legal intervention with attorneys and courts, then the determination is reliable and lawful, but a large amount of money and time are required
Solution Approach 1:
The patent segments the liability determination process into distinct modules: video data acquisition from multiple angles, environmental factor extraction (traffic lights, lane markings, road signs), rule-based liability calculation, and result output. This segmentation allows the system to handle complex determinations systematically and efficiently without requiring full legal intervention for every case
Solution Approach 2:
The patent performs preliminary automated assessment of split liability using video data and environmental factors before legal intervention is needed. This preliminary action filters out cases that can be resolved automatically, reserving legal intervention only for complex or disputed cases, thereby reducing overall processing time and costs
3Reliability
If police secure several black box videos and reconstruct accident situation, then the investigation is thorough, but it takes time to secure videos and reconstruct the accident
Solution Approach 1:
The patent implements preliminary automated video acquisition and processing immediately after an accident occurs. The system automatically captures video data from multiple angles, extracts environmental factors, and performs preliminary liability assessment without waiting for police reconstruction, significantly reducing investigation time while maintaining thoroughness
Solution Approach 2:
The patent enables the accident investigation process to be self-serving through automated video processing and environmental factor extraction. The system independently analyzes the captured videos, identifies relevant environmental factors, and generates preliminary liability assessments without requiring manual police reconstruction efforts
Data Source
AI summary
The present disclosure relates to a method and apparatus for split liability assessment of a car accident video using deep learning. The method for split liability assessment of a car accident video using deep learning according to an embodiment of the present disclosure may include: (a) obtaining car accident video data; and (b) outputting accidental split liability information by applying the obtained car accident video data to an accidental split liability determination model.