Robust Environmental Perception and Reliability Control for Intelligent Vehicles (eBook)

This book presents the most recent state-of-the-art algorithms on robust environmental perception and reliability control for intelligent vehicle systems. By integrating object detection, semantic segmentation, trajectory prediction, multi-object tracking, multi-sensor fusion, and reliability control in a systematic way, this book is aimed at guaranteeing that intelligent vehicles can run safely in complex road traffic scenes.

• Adopts the multi-sensor data fusion-based neural networks to environmental perception fault tolerance algorithms, solving the problem of perception reliability when some sensors fail by using data redundancy.
• Presents the camera-based monocular approach to implement the robust perception tasks, which introduces sequential feature association and depth hint augmentation, and introduces seven adaptive methods.
• Proposes efficient and robust semantic segmentation of traffic scenes through real-time deep dual-resolution networks and representation separation of vision transformers.
• Focuses on trajectory prediction and proposes phased and progressive trajectory prediction methods that is more consistent with human psychological characteristics, which is able to take both social interactions and personal intentions into account.
Puts forward methods based on conditional random field and multi-task segmentation learning to solve the robust multi-object tracking problem for environment perception in autonomous vehicle scenarios.    
• Presents the novel reliability control strategies of intelligent vehicles to optimize the dynamic tracking performance and investigates the completely unknown autonomous vehicle tracking issues with actuator faults.

Huihui Pan received a Ph.D. in control science and engineering from the Harbin Institute of Technology, China, in 2017, and a Ph.D. in mechanical engineering from The Hong Kong Polytechnic University, Hong Kong, in 2018. Since December 2017, he has been with the Research Institute of Intelligent Control and Systems, Harbin Institute of Technology, China. His research interests include intelligent vehicles, vehicle dynamic control, and nonlinear control theories with industrial applications. He currently serves as Associate Editor of IEEE Transactions on Intelligent Vehicles, IEEE Transactions on Systems, Man, and Cybernetics: Systems, and Mechatronics.

Jue Wang received a B.S. degree in automation and a M.S. degree in pattern recognition and intelligent systems from Huaqiao University, Xiamen, China, in 2016 and 2019, respectively, and a Ph.D. degree from the Harbin Institute of Technology, Harbin, China, in 2023. Since September 2023, she has been Postdoctoral Fellow with the University of Science and Technology of China, Hefei, China, and Ningbo Institute of Intelligent Equipment Technology Company Ltd., Ningbo, China. Her current research interests include intelligent vehicles, mechatronics, and adaptive control theories with industrial applications.

Xinghu Yu received an M.M. in osteopathic medicine from Jinzhou Medical University, China, and a Ph.D. in control science and engineering from the Harbin Institute of Technology, in 2016 and 2020, respectively. He is currently Chief Executive Officer of the Ningbo Institute of Intelligent Equipment Technology Co., Ltd., China. He has authored over 80 technical papers for conference proceedings and refereed journals, including the IEEE Transactions journals, and has over 20 invention patents. His research interests include vehicle dynamic systems, intelligent control, and image processing.

Weichao Sun received a B.S. degree in automation from Central South University, Changsha, China, in 2007, and the M.S. and Ph.D. degrees in control science and engineering from the Harbin Institute of Technology, Harbin, China, in 2009 and 2013, respectively. He is currently a Professor with the Research Institute of Intelligent Control Systems, Harbin Institute of Technology, China. His research interests include autonomous vehicles, robotics, and high performance motion control. He currently serves as Associate Editor for IEEE Transactions on Systems, Man, and Cybernetics: Systems, Mechatronics, and Journal of Dynamic Systems, Measurement and Control.

Huijun Gao received a Ph.D. in control science and engineering from the Harbin Institute of Technology, China, in 2005. From 2005 to 2007, he was Postdoctoral Researcher with the Department of Electrical and Computer Engineering at the University of Alberta, Canada. Since 2004, he has been with the Harbin Institute of Technology, where he is currently Chair Professor and Director of the Research Institute of Intelligent Control and Systems. His research interests include intelligent and robust control, robotics, mechatronics, and their engineering applications. He is Vice President of the IEEE Industrial Electronics Society and a Council Member of IFAC. He currently serves and has held editorial positions with several respected international journals. He is Fellow of the IEEE and Distinguished Lecturer of the IEEE Systems, Man, and Cybernetics Society. He is Member of the Academia Europaea.

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This book presents the most recent state-of-the-art algorithms on robust environmental perception and reliability control for intelligent vehicle systems. By integrating object detection, semantic segmentation, trajectory prediction, multi-object tracking, multi-sensor fusion, and reliability control in a systematic way, this book is aimed at guaranteeing that intelligent vehicles can run safely in complex road traffic scenes.Adopts the multi-sensor data fusion-based neural networks to environmental perception fault tolerance algorithms, solving the problem of perception reliability when some sensors fail by using data redundancy.Presents the camera-based monocular approach to implement the robust perception tasks, which introduces sequential feature association and depth hint augmentation, and introduces seven adaptive methods.Proposes efficient and robust semantic segmentation of traffic scenes through real-time deep dual-resolution networks and representation separation of vision transformers.Focuses on trajectory prediction and proposes phased and progressive trajectory prediction methods that is more consistent with human psychological characteristics, which is able to take both social interactions and personal intentions into account.Puts forward methods based on conditional random field and multi-task segmentation learning to solve the robust multi-object tracking problem for environment perception in autonomous vehicle scenarios.    Presents the novel reliability control strategies of intelligent vehicles to optimize the dynamic tracking performance and investigates the completely unknown autonomous vehicle tracking issues with actuator faults.