# Predicting Driver Intention Using Deep Neural Network

arXiv: https://arxiv.org/abs/2105.14790 · HTML: https://mlsanity.com/paper/2105.14790/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 27 references that are in the map.
- First posted on arXiv 31 May 2021.

## Builds on (most cited first, 17 of 17)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [Improved Regularization of Convolutional Neural Networks with Cutout](https://mlsanity.com/paper/1708.04552/) (2017)
- [FlowNet: Learning Optical Flow with Convolutional Networks](https://mlsanity.com/paper/1504.06852/) (2015)
- [AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty](https://mlsanity.com/paper/1912.02781/) (2019)
- [FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks](https://mlsanity.com/paper/1612.01925/) (2016)
- [A Survey of Autonomous Driving: Common Practices and Emerging Technologies](https://mlsanity.com/paper/1906.05113/) (2019)
- [DropBlock: A regularization method for convolutional networks](https://mlsanity.com/paper/1810.12890/) (2018)
- [Regularizing Deep Networks with Semantic Data Augmentation](https://mlsanity.com/paper/2007.10538/) (2020)
- [Recurrent Neural Networks for Driver Activity Anticipation via Sensory-Fusion Architecture](https://mlsanity.com/paper/1509.05016/) (2015)
- [Brain4Cars: Car That Knows Before You Do via Sensory-Fusion Deep Learning Architecture](https://mlsanity.com/paper/1601.00740/) (2016)
- [Driver Intention Anticipation Based on In-Cabin and Driving Scene Monitoring](https://mlsanity.com/paper/2006.11557/) (2020)
- [Driver Action Prediction Using Deep (Bidirectional) Recurrent Neural Network](https://mlsanity.com/paper/1706.02257/) (2017)
- [Robust and Subject-Independent Driving Manoeuvre Anticipation through Domain-Adversarial Recurrent Neural Networks](https://mlsanity.com/paper/1902.09820/) (2019)
- [Driving maneuvers prediction based on cognition-driven and data-driven method](https://mlsanity.com/paper/1805.02895/) (2018)

## Abstract (arXiv)

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.