Going Deeper: Autonomous Steering With Neural Memory Networks

Tharindu Fernando, Simon Denman, Sridha Sridharan, Clinton Fookes; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 214-221

Although autonomous driving is an area which has been extensively explored in computer vision, current deep learning based methods such as direct image to action mapping approaches, are not able to generate accurate results. This is largely due to the lack of capacity of the current state-of-the-art architectures to capture long term dependencies which can model different human preferences under different contexts. Our work explores a new paradigm in deep autonomous driving where the model incorporates both visual input as well as the steering wheel trajectory and attains a long term planning capacity via neural memory networks. The effectiveness of the proposed architecture is illustrated using two publicly available datasets where in both cases the proposed model demonstrates human like behaviour under challenging situations including illumination variations, discontinuous shoulder lines, lane merges, and divided highways, outperforming the current state-of-the-art.