V2V Blind Spot Collision Prevention Using Shared Sensor Data
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Solution Overview
Problem
Existing collision avoidance systems based on cameras or radar sensors have limited detection ranges, making it difficult to detect moving objects in blind spots, especially in adverse weather conditions, leading to potential collisions.
Innovation Solution
A vehicle-to-vehicle communication method that detects and analyzes image and ultrasound data from surrounding vehicles to predict the position and movement of objects in blind spots, using a prediction model to calculate the time to collision and adjust camera orientation and ultrasonic sensor operation for effective collision avoidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If collision avoidance is based on camera or radar sensor information collected by the own vehicle only, then the system complexity is low, but the detection range is narrow and blind spot collision cannot be predicted
Solution Approach 1:
The patent merges information from multiple sources: camera data, radar sensor data, and V2V communication data from surrounding vehicles. This combination expands the effective detection range beyond what any single sensor can achieve, allowing the system to detect objects in blind spots that would otherwise be invisible to the own vehicle's sensors alone.
Solution Approach 2:
V2V communication acts as an intermediary that transfers detection information from surrounding vehicles to the own vehicle. This mediator enables the own vehicle to access detection data from other vehicles' sensors, effectively extending the detection coverage area without requiring the own vehicle to have sensors in all possible locations.
2Reliability
If camera or radar sensor is used for collision avoidance, then the initial detection capability is provided, but detection fails in adverse weather conditions or dark environments
Solution Approach 1:
The patent applies different sensor types with different characteristics to detect objects under different conditions. By using both camera (good in clear weather) and radar sensor (good in adverse weather), the system ensures that at least one sensor type remains effective regardless of weather conditions, maintaining detection reliability across varying environments.
Solution Approach 2:
The system uses V2V communication to receive real-time information about objects detected by surrounding vehicles. This feedback mechanism allows the own vehicle to be aware of objects in areas where its own sensors may be compromised by adverse weather, compensating for the limitations of individual sensors through collective environmental awareness.
3Area of stationary object
If V2V communication is used to expand detection range, then blind spot detection capability is improved, but communication load and data processing complexity increase
Solution Approach 1:
The system extracts only the essential and relevant information from V2V communication data - specifically object position, type, and detection status - rather than transmitting and processing complete raw sensor data. This extraction approach expands detection range through V2V communication while minimizing communication load by transmitting only critical information needed for collision avoidance.
Data Source
AI summary
A device and method for preventing blind spot collision based on vehicle-to-vehicle communication. The method includes detecting a forward vehicle, requesting vehicle-to-vehicle communication with the forward vehicle, receiving image data and ultrasound data of the forward vehicle, analyzing information about a moving object in a blind spot formed by the forward vehicle, based on the image data and the ultrasound data, calculating a possibility of collision with the moving object based on the information about the moving object, and performing one or both of warning notification and collision avoidance control based on the possibility of collision.