Next Article in Journal
Feature Extraction for Bearing Fault Detection Using Wavelet Packet Energy and Fast Kurtogram Analysis
Next Article in Special Issue
The Design of Performance Guaranteed Autonomous Vehicle Control for Optimal Motion in Unsignalized Intersections
Previous Article in Journal
Influence of Pre-Turbine Small-Sized Oxidation Catalyst on Engine Performance and Emissions under Driving Conditions
Previous Article in Special Issue
Design of a Reinforcement Learning-Based Lane Keeping Planning Agent for Automated Vehicles

Design of a Low-complexity Graph-Based Motion-Planning Algorithm for Autonomous Vehicles

Department of Control for Transportation and Vehicle Systems, Budapest University of Technology and Economics, Stoczek u. 2, H-1111 Budapest, Hungary
Systems and Control Laboratory, SZTAKI Institute for Computer Science and Control, Kende u. 13-17, H-1111 Budapest, Hungary
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(21), 7716;
Received: 1 September 2020 / Revised: 13 October 2020 / Accepted: 28 October 2020 / Published: 31 October 2020
(This article belongs to the Special Issue Connected Automated Vehicles)
In the development of autonomous vehicles, the design of real-time motion-planning is a crucial problem. The computation of the vehicle trajectory requires the consideration of safety, dynamic and comfort aspects. Moreover, the prediction of the vehicle motion in the surroundings and the real-time planning of the autonomous vehicle trajectory can be complex tasks. The goal of this paper is to present low-complexity motion-planning for overtaking scenarios in parallel traffic. The developed method is based on the generation of a graph, which contains feasible vehicle trajectories. The reduction of the complexity in the real-time computation is achieved through the reduction of the graph with clustering. In the motion-planning algorithm, the predicted motion of the surrounding vehicles is taken into consideration. The prediction algorithm is based on density functions of the surrounding vehicle motion, which are developed through real measurements. The resulted motion-planning algorithm is able to guarantee a safe and comfortable trajectory for the autonomous vehicle. The effectiveness of the method is illustrated through simulation examples using a high-fidelity vehicle dynamic simulator. View Full-Text
Keywords: autonomous vehicles; motion-planning; trajectory design autonomous vehicles; motion-planning; trajectory design
Show Figures

Figure 1

MDPI and ACS Style

Hegedűs, T.; Németh, B.; Gáspár, P. Design of a Low-complexity Graph-Based Motion-Planning Algorithm for Autonomous Vehicles. Appl. Sci. 2020, 10, 7716.

AMA Style

Hegedűs T, Németh B, Gáspár P. Design of a Low-complexity Graph-Based Motion-Planning Algorithm for Autonomous Vehicles. Applied Sciences. 2020; 10(21):7716.

Chicago/Turabian Style

Hegedűs, Tamás, Balázs Németh, and Péter Gáspár. 2020. "Design of a Low-complexity Graph-Based Motion-Planning Algorithm for Autonomous Vehicles" Applied Sciences 10, no. 21: 7716.

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop