Cognitive Agent-Based Accident Avoidance System

Abstract

Distracted driving is a growing problem that leads to many deaths in the world. Causes of distraction are speeding, eating, texting, drinking, answering phone calls, reading billboards, adjusting vehicle equipment, and attending to passengers. These deaths could be prevented by a cognitive agent-based collision detection and auto collision avoidance (CABCD-CA) system. In order to reduce accidents caused by distraction, this paper presents a (CABCD-CA) system. The research is two-fold, first designed as a fuzzy inference system, which takes distraction, speed, and distance as input and produces the chances of an accident using fuzzy logic. Then, different probabilities of accidents are provided to the cognitive agent, which, in turn, performs appropriate collision avoidance manoeuvrers. The agent-based simulation of the CABCD-CA system is validated using VOMAS agent approach. Extensive testing has proved the success of the proposed system for avoiding collisions due to the distraction of the human driver.