SafeStep: An Interactive Demonstration of Semantic Communication for Pedestrian Safety Monitoring
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Computer Science > Machine Learning
arXiv:2608.27688 (cs)
[Submitted on 27 Aug 2026]
Title:SafeStep: An Interactive Demonstration of Semantic Communication for Pedestrian Safety Monitoring
Authors:Christian McDowell, Andrea Panebianco, Jeremiah Yang, Sirin Chakraborty, Samuel Chamoun, Travis Ross, Yin Sun
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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.
| Comments: | 6 pages, 5 figures. Submitted to the Quality, Value, and Age of Information for Tactical Networks workshop at the IEEE Military Communications Conference (MILCOM). Christian McDowell, Andrea Panebianco, and Jeremiah Yang are co-primary authors |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2608.27688 [cs.LG] |
| (or arXiv:2608.27688v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2608.27688
arXiv-issued DOI via DataCite (pending registration)
|
Submission history
From: Christian McDowell [view email][v1] Thu, 27 Aug 2026 20:25:50 UTC (3,032 KB)
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