Traffic Accident Warning System Using Video Synthesis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current traffic monitoring and warning systems are inadequate in accurately predicting and preventing accidents due to dynamic and complex environments, including blind spots and poor road conditions, which can lead to increased risk of collisions.
Innovation Solution
A traffic accident warning method and apparatus that utilizes computing devices with processors to obtain location data and video feeds from vehicles, processing road status, vehicle motion, and pedestrian data to generate traffic scenes and models, predicting potential accidents and sending warnings to vehicles in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional navigation devices provide basic road information, then drivers receive simple guidance, but the system cannot accurately predict accidents due to dynamic environmental factors and blind spots
Solution Approach 1:
The patent combines multiple data sources including video feeds from multiple cameras, location data from GPS, road status data, vehicle motion data, and pedestrian motion data into a unified traffic scene model. This merging of heterogeneous data sources enables comprehensive accident prediction while managing system complexity through integrated processing.
Solution Approach 2:
The patent introduces a computing device as an intermediary that receives data from multiple vehicles and cameras, processes this information through traffic accident models, and generates prediction results. This intermediary layer coordinates the complex interactions between multiple data sources and the prediction algorithm.
2Measurement precision
If multiple video cameras are used to capture comprehensive traffic data, then prediction accuracy improves, but data processing complexity and computational requirements increase
Solution Approach 1:
The patent divides the traffic monitoring system into multiple independent video capture units distributed at different locations. Each camera captures data for a specific field of view, and the computing device processes these segmented data streams separately before integrating them into a comprehensive traffic scene model.
Solution Approach 2:
The patent synthesizes video data from multiple spatial locations and time points to create a multi-dimensional traffic scene representation. By adding temporal and spatial dimensions to the data processing, the system achieves comprehensive coverage while managing complexity through structured multi-dimensional analysis.
3Loss of time
If real-time video processing is performed for all captured footage, then accident detection timeliness improves, but computational energy consumption increases
Solution Approach 1:
The patent pre-processes video data by extracting key features such as vehicle positions, speeds, and trajectories before performing full accident prediction analysis. This preliminary feature extraction reduces the computational burden of real-time processing while maintaining detection timeliness.
Solution Approach 2:
The patent processes only the most critical portions of video data that are most likely to indicate accident risks, rather than analyzing every frame in detail. By focusing computational resources on high-risk scenarios and key motion patterns, the system achieves timely warnings with reduced energy consumption.
Data Source
AI summary
This application discloses a traffic accident warning method performed by a computing device. After obtaining location data of a first vehicle and a second vehicle respectively, and videos captured by a first video camera onboard the first vehicle and a second video camera onboard the second vehicle, the computing device generates traffic scenes based on the road status data, the vehicle motion data, the pedestrian motion data obtained from the videos, and broadcasted traffic data, and then generates traffic accident models based on past traffic accidents, synthesizes a simulation video including one or more target features based on the traffic scenes and the traffic accident models at a target area associated with the target features. Finally, the computing device performs traffic accident prediction based on the simulation videos and sends warning information to the first vehicle in accordance with a determination that the first vehicle is about have an accident in the target area.