# SafeStep: An Interactive Demonstration of Semantic Communication for Pedestrian Safety Monitoring

arXiv: https://arxiv.org/abs/2608.27688 · HTML: https://mlsanity.com/paper/2608.27688/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 15 references that are in the map.
- First posted on arXiv 27 Aug 2026.

## Builds on (most cited first, 8 of 8)

- [FiLM: Visual Reasoning with a General Conditioning Layer](https://mlsanity.com/paper/1709.07871/) (2017)
- [Deep Variational Information Bottleneck](https://mlsanity.com/paper/1612.00410/) (2016)
- [Semantics-Empowered Communication for Networked Intelligent Systems](https://mlsanity.com/paper/2007.11579/) (2020)
- [Deep Joint Source-Channel Coding for Semantic Communications](https://mlsanity.com/paper/2211.08747/) (2022)
- [Demo: Real-Time Semantic Communications with a Vision Transformer](https://mlsanity.com/paper/2205.03886/) (2022)
- [Age of Information in Deep Learning-Driven Task-Oriented Communications](https://mlsanity.com/paper/2301.04298/) (2023)
- [DeepJSCC-l++: Robust and Bandwidth-Adaptive Wireless Image Transmission](https://mlsanity.com/paper/2305.13161/) (2023)
- [Aligning Task- and Reconstruction-Oriented Communications for Edge Intelligence](https://mlsanity.com/paper/2502.15472/) (2025)

## Abstract (arXiv)

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 a software-emulated Additive White Gaussian Noise (AWGN) channel, and renders user-specific positions, trajectories, and risk labels. The platform allows each user to select the transceiver, Signal-to-Noise Ratio (SNR), codelength, and Age of Information (AoI) and view the resulting pedestrian reconstruction. 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. 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.