Deep Learning Approach for Aggressive Driving Behaviour Detection

TL;DR

This paper presents a deep learning method using RNNs to detect aggressive driving behavior from GPS data, achieving high accuracy with minimal data segments, to help reduce road accidents.

Contribution

It introduces a GPS-based, real-time aggressive driving detection system using RNNs that is independent of vehicle or driver specifics, with proven high accuracy.

Findings

High accuracy in detecting aggressive driving

Effective with only 3-minute GPS data segments

RNN models outperform traditional methods

Abstract

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.