# Vehicle Emissions Prediction with Physics-Aware AI Models: Preliminary Results

arXiv: https://arxiv.org/abs/2105.00375 · HTML: https://mlsanity.com/paper/2105.00375/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 14 references that are in the map.
- First posted on arXiv 2 May 2021.

## Builds on (most cited first, 1 of 1)

- [Theory-guided Data Science: A New Paradigm for Scientific Discovery from Data](https://mlsanity.com/paper/1612.08544/) (2016)

## Built on by (most cited first, 1 of 1)

- [Physics-based machine learning framework for predicting NOx emissions from compression ignition engines using on-board diagnostics data](https://mlsanity.com/paper/2503.05648/) (2025)

## Abstract (arXiv)

Given an on-board diagnostics (OBD) dataset and a physics-based emissions prediction model, this paper aims to develop an accurate and computational-efficient AI (Artificial Intelligence) method that predicts vehicle emissions. The problem is of societal importance because vehicular emissions lead to climate change and impact human health. This problem is challenging because the OBD data does not contain enough parameters needed by high-order physics models. Conversely, related work has shown that low-order physics models have poor predictive accuracy when using available OBD data. This paper uses a divergent window co-occurrence pattern detection method to develop a spatiotemporal variability-aware AI model for predicting emission values from the OBD datasets. We conducted a case study using real-world OBD data from a local public transportation agency. Results show that the proposed AI method has approximately 65% improved predictive accuracy than a non-AI low-order physics model and is approximately 35% more accurate than a baseline model.