Next Article in Journal
Semi-Supervised Speech Recognition Acoustic Model Training Using Policy Gradient
Previous Article in Journal
Special Issue on Body Area Networks
Previous Article in Special Issue
Autonomous Navigation Framework for Intelligent Robots Based on a Semantic Environment Modeling
Open AccessArticle

A Hierarchical Control System for Autonomous Driving towards Urban Challenges

Intelligent Robotics Laboratory, Department of Control and Robot Engineering, Chungbuk National University, Cheongju-si 28644, Korea
Author to whom correspondence should be addressed.
Current Address: Chungdae-ro 1, Seowon-Gu, Cheongju, Chungbuk 28644, Korea.
Appl. Sci. 2020, 10(10), 3543;
Received: 23 April 2020 / Revised: 14 May 2020 / Accepted: 18 May 2020 / Published: 20 May 2020
(This article belongs to the Special Issue Intelligent Control and Robotics)
In recent years, the self-driving car technologies have been developed with many successful stories in both academia and industry. The challenge for autonomous vehicles is the requirement of operating accurately and robustly in the urban environment. This paper focuses on how to efficiently solve the hierarchical control system of a self-driving car into practice. This technique is composed of decision making, local path planning and control. An ego vehicle is navigated by global path planning with the aid of a High Definition map. Firstly, we propose the decision making for motion planning by applying a two-stage Finite State Machine to manipulate mission planning and control states. Furthermore, we implement a real-time hybrid A* algorithm with an occupancy grid map to find an efficient route for obstacle avoidance. Secondly, the local path planning is conducted to generate a safe and comfortable trajectory in unstructured scenarios. Herein, we solve an optimization problem with nonlinear constraints to optimize the sum of jerks for a smooth drive. In addition, controllers are designed by using the pure pursuit algorithm and the scheduled feedforward PI controller for lateral and longitudinal direction, respectively. The experimental results show that the proposed framework can operate efficiently in the urban scenario. View Full-Text
Keywords: autonomous vehicle; motion planning; local path planning; control system autonomous vehicle; motion planning; local path planning; control system
Show Figures

Figure 1

MDPI and ACS Style

Van, N.D.; Sualeh, M.; Kim, D.; Kim, G.-W. A Hierarchical Control System for Autonomous Driving towards Urban Challenges. Appl. Sci. 2020, 10, 3543.

Show more citation formats Show less citations formats
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

Search more from Scilit
Back to TopTop