REEEC-AGENT: human driver cognition and emotions-inspired rear-end collision avoidance method for autonomous vehicles. (September 2021)

• Record Type: Journal Article
• Authors: Butt, Muhammad A; Riaz, Faisal; Mehmood, Yasir; Akram, Somyyia
• Is Part Of: Simulation. Volume 97:Number 9(2021)
• Page Start: 601
• Page End: 617
• Publication Date: 2021-09
• Subjects: Autonomous vehicles -- affective computing -- cognitive agent -- deep learning -- emotions -- OCC model -- rear-end collisions; Computer simulation -- Periodicals; 003.3
• DOI: 10.1177/00375497211004721
• Languages: English
• ISSNs: 0037-5497

Abstract

Rear-end collision detection and avoidance is one of the most crucial driving tasks of self-driving vehicles. Mathematical models and fuzzy logic-based methods have recently been proposed to improve the effectiveness of the rear-end collision detection and avoidance systems in autonomous vehicles (AVs). However, these methodologies do not tackle real-time object detection and response problems in dense/dynamic road traffic conditions due to their complex computation and decision-making structures. In our previous work, we presented an affective computing-inspired Enhanced Emotion Enabled Cognitive Agent (EEEC_Agent), which is capable of rear-end collision avoidance using artificial human driver emotions. However, the architecture of the EEEC_Agent is based on an ultrasonic sensory system which follows three-state driving strategies without considering the neighbor vehicles types. To address these issues, in this paper we propose an extended version of the EEEC_Agent which contains human driver-inspired dynamic driving mode controls for autonomous vehicles. In addition, a novel end-to-end learning-based motion planner has been devised to perceive the surrounding environment and regulate driving tasks accordingly. The real-time in-field experiments performed using a prototype AV demonstrate the effectiveness of this proposed rear-end collision avoidance system.