Springe direkt zu Inhalt

Fuzzy Logic-based Adaptive Cruise Control for Autonomous Model Car

Daniel Göhring, Raúl Rojas, Khaled Alomari, Ricardo Mendoza Carrillo, Stephan Sundermann – 2020

One of the most critical challenges for the driver during highway driving is to adjust the vehicle speed continuously to maintain safe distance in respect to the heading vehicles or highway traffic. Neglecting a safe distance can cause deadly collisions, especially at high velocities. Thus, car speed must adapt smoothly and efficiently in relation to the velocity of the vehicle in front and the headway distance. Adaptive Cruise Control (ACC) is an Advanced Driver Assistant System that is used to control both velocity and distance at the same time. The system needs either a PID controller per state or a MIMO system. In this paper, we propose an ACC using a Fuzzy Logic approach for an autonomous model car called “AutoMiny.” AutoMiny was developed at the Dahlem Center for Machine Learning and Robotics at Freie Universität Berlin. It navigates by correcting its orientation error given by a global localization system and a pre-built grid map. The proposed controller can handle two states with differently designed profiles, and we will compare the performance of our approach with that of a PID controller.

Titel
Fuzzy Logic-based Adaptive Cruise Control for Autonomous Model Car
Verlag
SciTePress Digital Library
Schlagwörter
Advanced Driver Assistance Systems, Adaptive Cruise Control, Fuzzy Logic
Datum
2020-11
Kennung
ISBN: 978-989-758-479-4; DOI: 10.5220/0010175101210130
Quelle/n
Erschienen in
Proceedings of the International Conference on Robotics, Computer Vision and Intelligent Systems - Volume 1: ROBOVIS 2020
Sprache
eng
Größe oder Länge
9 pages