Autonomous Driving and Control: Case Studies with Self-Driving Platforms
Pei-heng Hong, Meikang Qiu, Yuehua Wang · 2020
In this paper, we have developed a laboratory testbed with low-cost self-driving platforms for providing new, feasible means for students to learn, test, validate, and demonstrate the feasibility of new techniques in data mining, artificial intelligence, and autonomous driving. In this paper, we describe the design and structure of the testbed with self-driving platforms and drones. It can easily support further expansion with new sensors as well as robots or support needs to make rapid development of artificial intelligence applications and self-driving applications. We also discuss two case studies in which we successfully applied feature detection and matching to generate panoramic views and conducted CNN-based self-driving on the testbed. Our results indicate the effectiveness of our testbed. Both the testbed and individual platforms can be easily duplicated and extended in low cost to foster student learning interests and improve their technical skills.