On-Vehicle Danger Forecast System based on Knowledge-based Artificial Intelligence

Abstract

Danger forecast and its avoidance are highly important for the design of automated driving system and the function is expected to be solved by artificial intelligences (AIs). However the reasoning process is unclear in the conventional scheme such as machine learning and as deep neural network models. In this paper, we focus on the ability of the logical reasoning based on the Semantic Web techniques, which is called knowledge-based AI, and demonstrate successfully its implementation into a module of Robot Operating System (ROS) to control a real vehicle. The processing speed of proposed system is evaluated using the real vehicle in the situation of the pedestrian avoidance in the crossroad.