# Uncertainty-Aware Vehicle Energy Efficiency Prediction using an Ensemble of Neural Networks

arXiv: https://arxiv.org/abs/2304.07073 · HTML: https://mlsanity.com/paper/2304.07073/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 53 references that are in the map.
- First posted on arXiv 14 Apr 2023.

## Builds on (most cited first, 9 of 9)

- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [Concrete Problems in AI Safety](https://mlsanity.com/paper/1606.06565/) (2016)
- [Adversarial Machine Learning at Scale](https://mlsanity.com/paper/1611.01236/) (2016)
- [Adversarial Training for Free!](https://mlsanity.com/paper/1904.12843/) (2019)
- [TensorFlow Distributions](https://mlsanity.com/paper/1711.10604/) (2017)
- [The Limits and Potentials of Deep Learning for Robotics](https://mlsanity.com/paper/1804.06557/) (2018)
- [Mobile Delivery Robots: Mixed Reality-Based Simulation Relying on ROS and Unity 3D](https://mlsanity.com/paper/2006.09002/) (2020)
- [Boosting Algorithms for Delivery Time Prediction in Transportation Logistics](https://mlsanity.com/paper/2009.11598/) (2020)

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

- [Conditional Diffusion Models for Energy-Efficient Driving](https://mlsanity.com/paper/2608.28142/) (2026)

## Abstract (arXiv)

The transportation sector accounts for about 25% of global greenhouse gas emissions. Therefore, an improvement of energy efficiency in the traffic sector is crucial to reducing the carbon footprint. Efficiency is typically measured in terms of energy use per traveled distance, e.g. liters of fuel per kilometer. Leading factors that impact the energy efficiency are the type of vehicle, environment, driver behavior, and weather conditions. These varying factors introduce uncertainty in estimating the vehicles' energy efficiency. We propose in this paper an ensemble learning approach based on deep neural networks (ENN) that is designed to reduce the predictive uncertainty and to output measures of such uncertainty. We evaluated it using the publicly available Vehicle Energy Dataset (VED) and compared it with several baselines per vehicle and energy type. The results showed a high predictive performance and they allowed to output a measure of predictive uncertainty.