# Deep Learning Approach for Aggressive Driving Behaviour Detection

arXiv: https://arxiv.org/abs/2111.04794 · HTML: https://mlsanity.com/paper/2111.04794/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 26 references that are in the map.
- First posted on arXiv 8 Nov 2021.

## Builds on (most cited first, 3 of 3)

- [sigmoidF1: A Smooth F1 Score Surrogate Loss for Multilabel Classification](https://mlsanity.com/paper/2108.10566/) (2021)
- [Characterizing Driving Styles with Deep Learning](https://mlsanity.com/paper/1607.03611/) (2016)
- [Recurrent Neural Networks: An Embedded Computing Perspective](https://mlsanity.com/paper/1908.07062/) (2019)

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

- [V-SenseDrive: A Privacy-Preserving Road Video and In-Vehicle Sensor Fusion Framework for Road Safety & Driver Behaviour Modelling](https://mlsanity.com/paper/2509.18187/) (2025)

## Abstract (arXiv)

Driving behaviour is one of the primary causes of road crashes and accidents, and these can be decreased by identifying and minimizing aggressive driving behaviour. This study identifies the timesteps when a driver in different circumstances (rush, mental conflicts, reprisal) begins to drive aggressively. An observer (real or virtual) is needed to examine driving behaviour to discover aggressive driving occasions; we overcome this problem by using a smartphone's GPS sensor to detect locations and classify drivers' driving behaviour every three minutes. To detect timeseries patterns in our dataset, we employ RNN (GRU, LSTM) algorithms to identify patterns during the driving course. The algorithm is independent of road, vehicle, position, or driver characteristics. We conclude that three minutes (or more) of driving (120 seconds of GPS data) is sufficient to identify driver behaviour. The results show high accuracy and a high F1 score.