Robust Environmental Perception and Reliability Control for Intelligent Vehicles

• 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.

Chapter 1. Background.- Chapter 2. Robust Environmental Perception of Multi-Sensor Data Fusion.- Chapter 3. Robust Environmental Perception of Monocular 3D Object Detection.- Chapter 4. Robust Environmental Perception of Semantic Segmentation.- Chapter 5. Robust Environmental Perception of Trajectory Prediction.- Chapter 6 Robust Environmental Perception of Multi-object Tracking.- Chapter 7. Reliability Control of Intelligent Vehicles.- References.

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 beenPostdoctoral 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.