Multi-modal Traffic Accident Reconstruction System
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Solution Overview
Problem
Current traffic accident analysis methods are limited by manual processes, subjective decisions, uni-modal inputs and outputs, and privacy concerns related to sensitive data.
Innovation Solution
A system that incorporates multi-modal input data, including video and vehicle dynamics, to reconstruct traffic accidents and provide analysis through multi-modal outputs, utilizing techniques such as multi-modal prompts, reinforcement learning, hybrid training, and an edge-cloud split configuration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis processes are used for traffic accident analysis, then subjective decisions can be made by analysts, but the analysis accuracy is limited and productivity is low
Solution Approach 1:
The patent replaces manual mechanical analysis processes with an automated multi-modal deep learning system that processes video, sensor data, and text simultaneously. The system uses transformer-based models to automatically reconstruct accident scenarios and generate analysis reports, eliminating subjective human decision-making while improving both accuracy and productivity through automated multi-modal fusion and reinforcement learning optimization.
2Measurement precision
If uni-modal input data is used for accident analysis, then the system complexity is low, but the analysis comprehensiveness is limited
Solution Approach 1:
The patent merges multiple uni-modal analysis systems into a single multi-modal framework that processes video footage, vehicle sensor data (accelerometers, gyroscopes, GPS), and text reports simultaneously. The system uses modality-specific encoders to process each data type independently, then fuses their representations through cross-attention mechanisms, achieving comprehensive accident reconstruction while managing complexity through modular architecture and specialized processing pipelines for each modality.
3Measurement precision
If sensitive accident data is stored and processed centrally, then comprehensive analysis is possible, but privacy concerns arise
Solution Approach 1:
The patent extracts and removes personally identifiable information (PII) and sensitive data from accident records before processing. The system uses natural language processing to redact names, addresses, and other identifying information from text reports, and applies privacy-preserving techniques to sensor data. The multi-modal model is trained on anonymized datasets, separating identifiable information from analytical features, thus enabling comprehensive analysis while protecting individual privacy through systematic data extraction and removal of sensitive elements.
4Measurement precision
If multi-modal data processing is implemented, then analysis accuracy improves, but computational resources and processing time increase
Solution Approach 1:
The patent segments the multi-modal processing pipeline into distinct modality-specific encoders for video, sensor data, and text, each optimized for its data type. These segmented encoders process their respective inputs independently and in parallel, then fuse their outputs through efficient cross-attention mechanisms. This segmentation allows the system to leverage specialized processing for each modality while reducing overall computational overhead compared to a monolithic approach, managing energy consumption through distributed parallel processing.
Data Source
AI summary
Disclosed herein are system, method, and computer program product embodiments for a model of traffic accident analysis, which incorporates multi-modal input data to automatically reconstruct accident process video with dynamics details and further provide multi-task analysis with multi-modal outputs.