# Robust and Subject-Independent Driving Manoeuvre Anticipation through Domain-Adversarial Recurrent Neural Networks

arXiv: https://arxiv.org/abs/1902.09820 · HTML: https://mlsanity.com/paper/1902.09820/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2019.

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

- [Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectives](https://mlsanity.com/paper/2208.07422/) (2022)
- [ADATIME: A Benchmarking Suite for Domain Adaptation on Time Series Data](https://mlsanity.com/paper/2203.08321/) (2022)
- [CaTFormer: Causal Temporal Transformer with Dynamic Contextual Fusion for Driving Intention Prediction](https://mlsanity.com/paper/2507.13425/) (2025)
- [Predicting Driver Intention Using Deep Neural Network](https://mlsanity.com/paper/2105.14790/) (2021)
- [GeoMind: An Agentic Workflow for Lithology Classification with Reasoned Tool Invocation](https://mlsanity.com/paper/2604.21501/) (2026)
- [GeoDecider: An Evidence-Grounded Agent for Geological Interpretation via Deliberative Reasoning](https://mlsanity.com/paper/2605.03383/) (2026)

## Abstract (arXiv)

Through deep learning and computer vision techniques, driving manoeuvres can be predicted accurately a few seconds in advance. Even though adapting a learned model to new drivers and different vehicles is key for robust driver-assistance systems, this problem has received little attention so far. This work proposes to tackle this challenge through domain adaptation, a technique closely related to transfer learning. A proof of concept for the application of a Domain-Adversarial Recurrent Neural Network (DA-RNN) to multi-modal time series driving data is presented, in which domain-invariant features are learned by maximizing the loss of an auxiliary domain classifier. Our implementation is evaluated using a leave-one-driver-out approach on individual drivers from the Brain4Cars dataset, as well as using a new dataset acquired through driving simulations, yielding an average increase in performance of 30% and 114% respectively compared to no adaptation. We also show the importance of fine-tuning sections of the network to optimise the extraction of domain-independent features. The results demonstrate the applicability of the approach to driver-assistance systems as well as training and simulation environments.