Visual Explanation by Attention Branch Network for End-to-end Learning-based Self-driving

Abstract

Self-driving decides an appropriate control considering the surrounding environment. To this end, self-driving control methods by using a convolutional neural network (CNN) have been studied, which directly input the vehicle-mounted camera image to a network and output a steering directory. However, if we need to control not only steering but also throttle, it is necessary to grasp the state of the car itself in addition to the surrounding environment. Moreover, in order to use CNNs for critical applications such as self-driving, it is important to analyze where the network focuses on the image and to understand the decision making. In this work, we propose a method to solve these problems. First, to control both steering and throttle simultaneously, we propose using the current vehicle speed as the state of the car itself. Second, we introduce an attention branch network (ABN) architecture to a self-driving model, which enables visually analyzing the reason of the self-driving decision making by using an attention map. Experimental results with a driving simulator demonstrate that our method controls a car stably, and we can analyze the decision making by using the attention map.