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

Abstract

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.

arXiv comment40 pages, 4 figures. Published online in Robotics and Autonomous Systems

References in corpus (8)

• Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
• LSTM: A Search Space Odyssey
• An overview of gradient descent optimization algorithms
• A Theoretically Grounded Application of Dropout in Recurrent Neural Networks
• Statistical Pattern Recognition for Driving Styles Based on Bayesian Probability and Kernel Density Estimation
• Joint Modeling of Event Sequence and Time Series with Attentional Twin Recurrent Neural Networks
• Deep Multimodal Representation Learning from Temporal Data
• Domain Adaptation for Neural Networks by Parameter Augmentation

Cited by in corpus (2)

• ADATIME: A Benchmarking Suite for Domain Adaptation on Time Series Data
• Predicting Driver Intention Using Deep Neural Network