# Intelligent Traffic Monitoring with Hybrid AI

arXiv: https://arxiv.org/abs/2209.00448 · HTML: https://mlsanity.com/paper/2209.00448/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 29 references that are in the map.
- First posted on arXiv 31 Aug 2022.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning](https://mlsanity.com/paper/1909.02151/) (2019)
- [Towards Generalizable Neuro-Symbolic Systems for Commonsense Question Answering](https://mlsanity.com/paper/1910.14087/) (2019)
- [Neuro-symbolic Architectures for Context Understanding](https://mlsanity.com/paper/2003.04707/) (2020)
- [Cluster Pruning: An Efficient Filter Pruning Method for Edge AI Vision Applications](https://mlsanity.com/paper/2003.02449/) (2020)
- [Iterative Deep Learning for Road Topology Extraction](https://mlsanity.com/paper/1808.09814/) (2018)
- [Consolidating Commonsense Knowledge](https://mlsanity.com/paper/2006.06114/) (2020)
- [Urban Traffic Monitoring and Modeling System: An IoT Solution for Enhancing Road Safety](https://mlsanity.com/paper/2003.07672/) (2020)

## Built on by (most cited first, 2 of 2)

- [A Study of Situational Reasoning for Traffic Understanding](https://mlsanity.com/paper/2306.02520/) (2023)
- [Traffic-Domain Video Question Answering with Automatic Captioning](https://mlsanity.com/paper/2307.09636/) (2023)

## Abstract (arXiv)

Challenges in Intelligent Traffic Monitoring (ITMo) are exacerbated by the large quantity and modalities of data and the need for the utilization of state-of-the-art (SOTA) reasoners. We formulate the problem of ITMo and introduce HANS, a neuro-symbolic architecture for multi-modal context understanding, and its application to ITMo. HANS utilizes knowledge graph technology to serve as a backbone for SOTA reasoning in the traffic domain. Through case studies, we show how HANS addresses the challenges associated with traffic monitoring while being able to integrate with a wide range of reasoning methods