Collision Detection by Networked Sensors

The advances in sensor technologies enable real-time collection of high-fidelity spatiotemporal data on transportation networks of major cities. We consider a set of speed sensors that are spatially distributed along a street and can communicate via an exogenously determined network. In this paper, we address the problem of detecting in real-time collisions that occur within a certain distance from each sensor. The speed sensors observe the average speed value of the cars at regular time intervals and adopt a threshold-based approach to generate local predictions. Each sensor exchanges its local predictions with its neighbors and aggregates the local predictions it receives using a weighted majority aggregation rule to generate a final prediction. Since collisions are eventually reported (e.g., by a police officer or by crowd-sourced information), we assume that the information about the real occurrence of a collision is eventually given to the sensors. We propose an online learning rule that exploits this feedback to adapt the weights that each sensor gives to different local predictions. In the realizable case, i.e., when there exist unknown weights that would allow the sensors to distinguish between collisions and normal traffic behaviors, we determine an upper bound for the worst-case misdetection and false alarm probabilities of our scheme. We evaluate our scheme with traffic datasets collected from the segment of the 405 freeway that passes through Los Angeles County and the results show the efficacy of the proposed approach.