Predicting Driver Intention Using Deep Neural Network

TL;DR

This paper presents a deep neural network framework for predicting driver intentions using multiple views and timeframes, achieving high accuracy and faster performance than existing methods.

Contribution

The study introduces a novel deep learning architecture that predicts driver maneuvers from inside and outside views with improved speed and accuracy.

Findings

Achieved up to 98.96% accuracy with combined inside and outside views.

Outperformed state-of-the-art methods in speed and performance.

Effective prediction from 5 seconds before maneuver.

Abstract

To improve driving safety and avoid car accidents, Advanced Driver Assistance Systems (ADAS) are given significant attention. Recent studies have focused on predicting driver intention as a key part of these systems. In this study, we proposed new framework in which 4 inputs are employed to anticipate diver maneuver using Brain4Cars dataset and the maneuver prediction is achieved from 5, 4, 3, 2, 1 seconds before the actual action occurs. We evaluated our framework in three scenarios: using only 1) inside view 2) outside view and 3) both inside and outside view. We divided the dataset into training, validation and test sets, also K-fold cross validation is utilized. Compared with state-of-the-art studies, our architecture is faster and achieved higher performance in second and third scenario. Accuracy, precision, recall and f1-score as evaluation metrics were utilized and the result of 82.41%, 82.28%, 82,42% and 82.24% for outside view and 98.90%, 98.96%, 98.90% and 98.88% for both inside and outside view were gained, respectively.