Driving situation-based real-time interaction with intelligent driving assistance agent

Abstract

Driving assistance systems (DASs) can be useful to inexperienced drivers. Current DASs are composed of front rear monitoring systems (FRMSs), lane departure warning systems (LDWSs), side obstacle warning systems (SOWSs), etc. Sometimes, DASs provide unnecessary information when using unprocessed low-level data. Therefore, to provide high-level necessary information to the driver, DASs need to be improved. In this paper, we present an intelligent driving assistance robotic agent for safe driving. We recognize seven driving situations, namely, speed bump, corner, crowded area, uphill, downhill, straight, and parking space, using hidden Markov models (HMMs) based on velocity, accelerator pedal, and steering wheel. The seven situations and global positioning system information are used to generate a situation information map. The developers of a navigation system have to tag driving events by themselves. In contrast, our driving assistance agent tags situation information automatically as the vehicle is driven. The robotic agent uses the driving situation and status information to assist safe driving with motions and facial and verbal expressions.