Vehicle Collision Evidence Capture Using Onboard AI Detection
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Solution Overview
Problem
Existing techniques for capturing the aftermath of vehicular collisions are unreliable due to reliance on eyewitness reports, limited ubiquity of traffic cameras, and manual activation of personal devices, which can lead to delayed or incomplete recording of evidence and increased risk of misremembering or bias.
Innovation Solution
A vehicle-equipped system with external sensors and a deep learning neural network that continuously scans for collisions, automatically records and broadcasts post-collision evidence to emergency services, providing reliable and unbiased data even in areas without traffic cameras.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If eyewitness reports are used to capture collision aftermath, then human memory and bias are involved, but reliability of evidence is reduced
Solution Approach 1:
The patent replaces the human eyewitness reporting system with an automated electronic system comprising sensors, processors, and communication devices. The sensor component captures vicinity data automatically, the inference component processes this data through a deep learning neural network to detect collisions, and the broadcast component transmits evidence to emergency services, eliminating human memory and bias from the evidence collection process.
Solution Approach 2:
The system performs self-service by automatically detecting collisions and recording evidence without requiring human intervention. The inference component continuously monitors sensor data and autonomously determines when a collision has occurred, triggering automatic evidence capture and transmission, thereby removing the need for eyewitnesses to manually report or activate recording devices.
2Area of stationary object
If traffic cameras are deployed to capture collision evidence, then coverage is improved, but ubiquity is limited due to installation requirements
Solution Approach 1:
The patent transforms the static traffic camera system into a dynamic mobile monitoring system. By mounting sensors on moving vehicles, the system gains the ability to cover multiple locations and angles as vehicles traverse the area, significantly expanding coverage while requiring no permanent installation infrastructure.
Solution Approach 2:
The system achieves universality by utilizing existing mobile vehicles as platforms for collision detection. Rather than requiring dedicated fixed camera installations, any vehicle equipped with the system can serve as a mobile evidence collection point, making the system adaptable to diverse locations and situations without specialized infrastructure.
3Quantity of substance
If manual activation of personal devices is required to record evidence, then device availability is improved, but response time is delayed
Solution Approach 1:
The system implements preliminary action by continuously capturing and buffering vicinity data before a collision occurs. The sensor component records pre-collision data, and when the inference component detects a collision event, the evidence is already captured and ready for immediate transmission, eliminating any delay associated with manual device activation.
Solution Approach 2:
The system maintains continuous monitoring and data capture operations without interruption. Sensors continuously record vicinity data, and the inference component continuously analyzes this data stream, ensuring that collision evidence is captured at the moment it occurs without requiring manual intervention to start or stop recording.
4Measurement precision
If automated deep learning detection is implemented, then accuracy is improved, but system complexity increases
Solution Approach 1:
The patent extracts the complex deep learning neural network processing from the vehicle's primary control systems and implements it as a dedicated inference component. This separation allows the complex AI processing to be isolated in a specialized module that can be optimized independently, managing system complexity while maintaining high detection accuracy.
Data Source
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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 first vehicle. In various aspects, the system can capture, via one or more first cameras or one or more first microphones of the first vehicle, vicinity data associated with a first vicinity of the first vehicle. In various instances, the system can determine, via execution of a deep learning neural network on the vicinity data, whether a vehicular collision not involving the first vehicle has occurred in the first vicinity of the first vehicle. In various cases, the system can record, in response to a determination that the vehicular collision has occurred and via the one or more first cameras or the one or more first microphones, first post-collision evidence associated with the first vicinity of the first vehicle.