Going deeper: Autonomous steering with neural memory networks

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, making their application questionable. 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 and their behaviour 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. Furthermore, this work investigates optimal feature fusion techniques to combine these multimodal information sources, without discarding the vital information that they offer. 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-theart.