Theories and Practices of Self-Driving Vehicles

Qingguo Zhou is Professor at Lanzhou University and Deputy Director of the Engineering Research Center for Open Source Software and Real-Time Systems, at the Ministry of Education, China. He is also the Director of the School of Computer Science and Engineering and the Embedded System Laboratory at Lanzhou University. His research focuses on intelligent driving, AI, embedded and real-time systems. He has published widely. Zebang Shen is a Senior Autonomous Driving Engineer at Daimler AG and a Google Developer Expert (GDE) in machine learning. His research focusses on the development of L4 autonomous driving systems. In particular his interests include 3D perception, multi-sensor fusion, multi-sensor automatic calibration, computer vision and 3D SLAM. He is an advocate for innovation, open source and knowledge sharing. Binbin Yong received his PhD degree in Computer Science and Technology from Lanzhou University in 2017. Now he is an associate professor and master's supervisor in the School of Information Science and Engineering, Lanzhou University, China. He is mainly engaged in research on high-performance computing, neural network and deep learning Rui Zhao is a PhD student at Lanzhou University, China, who is devoted to the development of search recommendation model in the multinational company. Currently focusing on research on perception in the self-driving vehicle. Peng Zhi is currently pursuing his PhD degree in the School of Information Science and Engineering, Lanzhou University, China. His research interests include computer vision, deep learning, and autonomous driving

1. Introduction of Self-driving vehicle system 2. Overview of Robot Operating System (ROS) 3. Position modules 4. State estimation and sensor fusion 5. Machine Learning and Neural Network Fundamentals 6. Deep learning and visual perception 7. Transfer learning and end-to-end driverless driving 8. Getting Started with Autonomous Driving Planning 9. Vehicle models and advanced controls 10. Reinforcement learning and its application in autonomous driving