Onboard Vehicle Collision Evidence Detection via Deep Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing techniques for addressing vehicular collisions are unreliable due to reliance on eyewitness reports, which are prone to misremembering and bias, and the limited coverage and mobility of traffic cameras and personal computing devices.
Innovation Solution
A system onboard a vehicle equipped with external sensors and a deep learning neural network that captures vicinity data and automatically detects vehicular collisions, records post-collision evidence, and broadcasts this evidence to emergency service computing devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If eyewitness reports are used to address vehicular collisions, then the system can operate with minimal infrastructure, but the reliability of evidence is poor due to human error and bias
Solution Approach 1:
The patent replaces human eyewitness accounts with an automated computer vision system. The sensor component captures images, the deep learning neural network analyzes them to detect collisions, and the evidence component records objective visual data. This substitution eliminates human error, bias, and misremembering while providing reliable, objective evidence of vehicular collisions.
Solution Approach 2:
The system performs self-service by automatically detecting collisions and recording evidence without requiring human intervention. The onboard computer executes the deep learning neural network to autonomously analyze sensor data, determine collision occurrence, and trigger evidence recording. This automation ensures consistent, reliable operation independent of human factors.
2Reliability
If traffic cameras are used to monitor collisions, then collision detection is possible, but coverage is limited and mobility is restricted
Solution Approach 1:
The patent applies dynamics by making the detection system mobile rather than stationary. The sensor component is integrated into a vehicle that can move freely along roads and pathways, providing dynamic coverage wherever the vehicle travels. This eliminates the fixed-location limitation of traditional traffic cameras while maintaining collision detection capability through the onboard sensor and processing systems.
3Loss of time
If personal computing devices are used for collision reporting, then any device can be used, but manual activation is required reducing responsiveness
Solution Approach 1:
The system performs preliminary action by continuously monitoring the environment through the sensor component before collisions occur. The deep learning neural network is pre-configured to automatically analyze sensor data in real-time, so when a collision happens, the evidence component can immediately trigger recording without requiring manual activation. This ensures rapid response time while the system remains easy to operate through automated functionality.
Data Source
AI summary
Systems/techniques that facilitate artificially intelligent provision of post-vehicular-collision evidence are provided. In various embodiments, a system can be onboard a vehicle and can capture, via one or more cameras or microphones of the vehicle, vicinity data associated with a vicinity of the vehicle. In various instances, the system can generate, via execution of a deep learning neural network on the vicinity data, a classification label indicating whether a vehicular collision not involving the vehicle has occurred in the vicinity of the vehicle. In various cases, the system can record, in response to the classification label indicating that the vehicular collision has occurred and via the one or more cameras or the one or more microphones, post-collision evidence associated with the vicinity of the vehicle. In various aspects, the system can broadcast the classification label and the post-collision evidence to an emergency service computing device.