Demo: A Vehicular-Network Based Speed-Bump Detection Approach

Timely and accurate detection of speed bumps poses a significant challenge, particularly in large-scale vehicular networks where maintaining a low false positive detection rate is crucial to minimize manual verification efforts. In this paper, we propose an accurate speed bump detection algorithm with a low false positive rate, which is essential for large-scale crowd -sourcing systems like connected vehicular networks. Our demonstration showcases the algorithm's pipeline, encompassing the data collection and processing of the in-vehicle system, data fusion and store in the network backend, and visualization of speed-bumps. The dataset utilized in this demonstration comprises real-world driving data stored in Robot Operating System (ROS) bag files. Our demonstration illustrates that the proposed speed bump detection and sharing system can reliably detect speed bumps and irregular road surfaces with zero false positive detections across all recorded data.