SafeStep: An Interactive Demonstration of Semantic Communication for Pedestrian Safety Monitoring
Machine Learning
Summary
The authors created SafeStep, a web platform that watches pedestrians using four live traffic cameras and sends their position and risk info through a special communication system. This system uses a method called Meta-VIB, which is a small neural network model that works well under different signal quality and speed settings without needing retraining. Their tests showed that Meta-VIB greatly reduces errors compared to other methods. SafeStep can support up to 100 users with different settings, showing the impact of data delays on the monitoring in real time, which is something new in this field.
Authors
Christian McDowell, Andrea Panebianco, Jeremiah Yang, Sirin Chakraborty, Samuel Chamoun, Travis Ross, Yin Sun
Abstract
In this paper, we develop SafeStep, an interactive browser-based semantic communication platform for live pedestrian safety monitoring. SafeStep extracts pedestrian information from four live traffic-camera feeds, transmits it through a semantic communication transceiver over an Additive White Gaussian Noise (AWGN) channel, and renders user-specific positions, trajectories, and risk labels. The platform allows to independently select the transceiver, Signal-to-Noise Ratio (SNR), codelength, and Age of Information (AoI), and demonstrates the transceiver performance of the selected configuration through live pedestrian safety monitoring to each browser. SafeStep compares a recently proposed semantic communication design called Meta-VIB with five baseline transceivers. Meta-VIB uses a compact neural model with only $4.16$ million parameters to generalize across varying SNR, codelength, and AoI values without online retraining. Experimental results show that Meta-VIB achieves mean task-loss reductions of up to $92.1\%$. On one high-end GPU server, the integrated concurrent-access workload maintains the target $5$ frames/s through $20$ users. At $100$ users, each requesting a distinct configuration, SafeStep records no request failures and a mean application response time below $1$ s, but its mean per-browser frame rate falls to approximately $1$ frame/s. To our knowledge, SafeStep is the first real-time semantic communication platform to make AoI-induced downstream degradation directly observable in live monitoring applications.