Onboard AI Collision Detection for Automatic Evidence Broadcast
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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 coverage of traffic cameras, and manual activation of personal devices, which can lead to misremembering, bias, and delayed response times, especially in areas without camera coverage.
Innovation Solution
A vehicle equipped with external sensors and a deep learning neural network to continuously scan for collisions, automatically record and transmit post-collision evidence to emergency services, allowing real-time interaction and reducing response times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If eyewitness reports are used to capture collision aftermath, then human memory and testimony are utilized, but reliability deteriorates due to misremembering and bias
Solution Approach 1:
The patent replaces the human eyewitness reporting system with an automated electronic evidence collection system. Sensors, cameras, and microphones automatically detect and record collision events, eliminating human memory and testimony from the evidence chain. This mechanical/electronic substitution directly resolves the reliability issue by removing subjective human factors.
Solution Approach 2:
The system enables self-service evidence collection where the collision detection system automatically captures, processes, and transmits evidence without requiring human intervention. The deep learning neural network autonomously analyzes sensor data, identifies collision events, and initiates evidence recording, making the system self-sufficient and eliminating reliance on human witnesses.
2Area of stationary object
If traffic cameras are deployed to monitor collisions, then coverage is provided in monitored areas, but coverage remains limited to areas with camera infrastructure
Solution Approach 1:
The patent creates a universal collision detection system that can operate in any location without requiring fixed infrastructure. The mobile platform with integrated sensors and deep learning capabilities can deploy anywhere, making the system adaptable to rural areas, undeveloped roads, and locations without existing camera infrastructure. This multi-functional design resolves the coverage limitation by enabling operation across diverse environments.
Solution Approach 2:
The system transitions from static traffic camera coverage to dynamic mobile monitoring. The collision detection system can move to different locations, adjust its monitoring scope, and adapt its sensor orientation based on real-time conditions. This dynamic capability allows the system to cover areas that would be inaccessible to fixed cameras and respond flexibly to changing environmental conditions.
3Quantity of substance
If manual activation of personal devices is required, then device availability is high, but response time increases due to delayed activation
Solution Approach 1:
The system performs preliminary actions by continuously monitoring for collision events before manual intervention is needed. Sensors are always active, detecting collisions automatically, and the deep learning neural network is pre-configured to immediately process detected events and initiate evidence recording. This preliminary automated detection eliminates the time delay associated with manual device activation while maintaining high availability through continuous operation.
Solution Approach 2:
The system implements automatic feedback loops where sensor data continuously feeds the deep learning neural network, which immediately processes collision detections and triggers evidence collection. This closed-loop feedback system eliminates manual activation delays by automatically responding to collision events in real-time, ensuring rapid evidence capture while maintaining system availability through continuous monitoring.
4Measurement precision
If deep learning neural network is implemented for collision detection, then detection accuracy improves, but computational complexity and processing requirements increase
Solution Approach 1:
The patent segments the complex deep learning system into modular functional components: sensor data acquisition, neural network inference, collision classification, and evidence recording. Each module performs a specific function, making the overall complex system manageable and maintainable. This segmentation allows high detection accuracy through sophisticated neural networks while controlling system complexity through modular architecture.
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. In various aspects, the system can capture, via one or more cameras or one or more microphones of the vehicle, vicinity data associated with a vicinity of the 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 vehicle has occurred in the vicinity of the vehicle. In various cases, the system can broadcast, in response to a determination that the vehicular collision has occurred, via the one or more cameras or the one or more microphones, and to an emergency service computing device, a post-collision live stream associated with the vicinity of the vehicle.