Edge Device Collision Prediction for Vehicle Traffic Control
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Solution Overview
Problem
Traffic control systems face challenges in ensuring collision prevention due to unreliable data from camera sensors and GPS, leading to delayed calculations and network resource consumption, which can result in collisions and inefficient traffic flow.
Innovation Solution
Implementing an edge device at intersections for low latency, high bandwidth processing of data from sensors and vehicles, confirming data accuracy by matching vehicle-provided and sensor-provided tracking information, and providing real-time instructions to traffic control devices to prevent collisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If data is sent to offsite device for collision prediction calculations, then network resources are consumed and calculation time is extended, but collision prevention capability is reduced due to delayed responses
Solution Approach 1:
The system segments the collision prediction function from the central offsite device and places edge computing devices at strategic locations (intersections, road segments). This segmentation enables local real-time processing of tracking data, eliminating network latency while maintaining comprehensive collision detection capabilities through distributed computation nodes that independently analyze vehicle trajectories and predict potential collisions.
Solution Approach 2:
Edge computing devices serve as intermediaries between sensor/vehicle data sources and the central traffic management system. These intermediaries perform local data processing and collision prediction, filtering and preparing information before transmitting to central systems. This intermediary layer reduces network resource consumption and enables instantaneous local decision-making for collision prevention.
2Loss of information
If camera sensors and GPS are used for tracking, then vehicle position data is collected, but data reliability is insufficient leading to incorrect collision predictions
Solution Approach 1:
The system merges multiple data sources including camera sensors, GPS receivers, and radar/lidar systems to create a comprehensive tracking information framework. By combining these diverse sensing modalities, the system achieves redundant verification of vehicle positions and trajectories, filtering out unreliable data points and synthesizing a more accurate real-time picture of vehicle locations and movements for dependable collision prediction.
Solution Approach 2:
The system implements feedback mechanisms where edge computing devices continuously receive tracking data from multiple sources, compare results, and adjust their collision prediction algorithms in real-time. When discrepancies are detected between different sensing systems, the feedback loop enables dynamic weight adjustment and data fusion, improving measurement precision through iterative refinement based on actual observed vehicle behavior patterns.
Data Source
AI summary
An edge device may receive first vehicle information and first sensor information associated with a first vehicle. The edge device may receive second vehicle information or second sensor information associated with a second vehicle. The edge device may compare the first vehicle information and the first sensor information to determine a first accuracy score. The edge device may determine, based on the first vehicle information, the first sensor information, the first accuracy score, and the second vehicle information or the second sensor information, whether the first vehicle and the second vehicle are predicted to collide. The edge device may provide to one or more traffic control devices, and based on determining whether the first vehicle and the second vehicle are predicted to collide, one or more instructions to provide signals to at least one of the first vehicle or the second vehicle.