# Passive and Active Learning of Driver Behavior from Electric Vehicles

arXiv: https://arxiv.org/abs/2203.02179 · HTML: https://mlsanity.com/paper/2203.02179/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 49 references that are in the map.
- First posted on arXiv 4 Mar 2022.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [On Statistical Bias In Active Learning: How and When To Fix It](https://mlsanity.com/paper/2101.11665/) (2021)
- [Practical Obstacles to Deploying Active Learning](https://mlsanity.com/paper/1807.04801/) (2018)
- [Human Activity Recognition from Wearable Sensor Data Using Self-Attention](https://mlsanity.com/paper/2003.09018/) (2020)
- [A Deep Learning Framework for Generation and Analysis of Driving Scenario Trajectories](https://mlsanity.com/paper/2007.14524/) (2020)
- [Efficient Online Learning for Optimizing Value of Information: Theory and Application to Interactive Troubleshooting](https://mlsanity.com/paper/1703.05452/) (2017)
- [An Online Learning Framework for Energy-Efficient Navigation of Electric Vehicles](https://mlsanity.com/paper/2003.01416/) (2020)
- [Active Learning of Driving Scenario Trajectories](https://mlsanity.com/paper/2108.03217/) (2021)
- [Online Learning of Energy Consumption for Navigation of Electric Vehicles](https://mlsanity.com/paper/2111.02314/) (2021)
- [Driver Behavior Recognition via Interwoven Deep Convolutional Neural Nets with Multi-stream Inputs](https://mlsanity.com/paper/1811.09128/) (2018)
- [Model-Centric and Data-Centric Aspects of Active Learning for Deep Neural Networks](https://mlsanity.com/paper/2009.10835/) (2020)

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

- [A Unified Active Learning Framework for Annotating Graph Data with Application to Software Source Code Performance Prediction](https://mlsanity.com/paper/2304.13032/) (2023)

## Abstract (arXiv)

Modeling driver behavior provides several advantages in the automotive industry, including prediction of electric vehicle energy consumption. Studies have shown that aggressive driving can consume up to 30% more energy than moderate driving, in certain driving scenarios. Machine learning methods are widely used for driver behavior classification, which, however, may yield some challenges such as sequence modeling on long time windows and lack of labeled data due to expensive annotation. To address the first challenge, passive learning of driver behavior, we investigate non-recurrent architectures such as self-attention models and convolutional neural networks with joint recurrence plots (JRP), and compare them with recurrent models. We find that self-attention models yield good performance, while JRP does not exhibit any significant improvement. However, with the window lengths of 5 and 10 seconds used in our study, none of the non-recurrent models outperform the recurrent models. To address the second challenge, we investigate several active learning methods with different informativeness measures. We evaluate uncertainty sampling, as well as more advanced methods, such as query by committee and active deep dropout. Our experiments demonstrate that some active sampling techniques can outperform random sampling, and therefore decrease the effort needed for annotation.