# Driver Action Prediction Using Deep (Bidirectional) Recurrent Neural Network

arXiv: https://arxiv.org/abs/1706.02257 · HTML: https://mlsanity.com/paper/1706.02257/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2017.

## Built on by (most cited first, 6 of 6)

- [Driving Style Representation in Convolutional Recurrent Neural Network Model of Driver Identification](https://mlsanity.com/paper/2102.05843/) (2021)
- [Is my Driver Observation Model Overconfident? Input-guided Calibration Networks for Reliable and Interpretable Confidence Estimates](https://mlsanity.com/paper/2204.04674/) (2022)
- [Time-to-Green predictions for fully-actuated signal control systems with supervised learning](https://mlsanity.com/paper/2208.11344/) (2022)
- [Predicting Driver Intention Using Deep Neural Network](https://mlsanity.com/paper/2105.14790/) (2021)
- [Unsupervised Driver Behavior Profiling leveraging Recurrent Neural Networks](https://mlsanity.com/paper/2108.05079/) (2021)
- [Towards Safer and Understandable Driver Intention Prediction](https://mlsanity.com/paper/2510.09200/) (2025)

## Abstract (arXiv)

Advanced driver assistance systems (ADAS) can be significantly improved with effective driver action prediction (DAP). Predicting driver actions early and accurately can help mitigate the effects of potentially unsafe driving behaviors and avoid possible accidents. In this paper, we formulate driver action prediction as a timeseries anomaly prediction problem. While the anomaly (driver actions of interest) detection might be trivial in this context, finding patterns that consistently precede an anomaly requires searching for or extracting features across multi-modal sensory inputs. We present such a driver action prediction system, including a real-time data acquisition, processing and learning framework for predicting future or impending driver action. The proposed system incorporates camera-based knowledge of the driving environment and the driver themselves, in addition to traditional vehicle dynamics. It then uses a deep bidirectional recurrent neural network (DBRNN) to learn the correlation between sensory inputs and impending driver behavior achieving accurate and high horizon action prediction. The proposed system performs better than other existing systems on driver action prediction tasks and can accurately predict key driver actions including acceleration, braking, lane change and turning at durations of 5sec before the action is executed by the driver.