Automated vehicles (AVs) have shown dramatic potential to revolutionize human transportation and further improve mobility safety. As a promising substitution to replace human drivers, AVs must be capable of driving like experienced drivers and navigating safely in highly dynamic traffic flows. Much interesting research has emerged in recent decades to attempt to address essential issues of automated driving including environment perception, decision-making and planning, and adaptive control, etc. However, AVs are still far from mass production and launch because their performance is limited in various aspects. Therefore, crucial technologies of AVs need to be further improved to make reasonable driving behavior actions and flexibly plan motion in complicated traffic situations, to enhance stability, safety, and meet occupant driving performance expectations.
In light of current open challenges posed by highly dynamic traffic situations, vehicle dynamic nonlinearity, and coordination of multiple control objectives and actuators, this Research Topic aims to investigate new methods for signal processing and communication, decision-making, planning, and control of automated driving in real-world traffic scenarios. Artificial intelligence, which has been developed extensively in data modeling and decision inference, provides a powerful tool to enhance AVs' performance, especially in complex situations. Hence, we are also looking for studies focusing on the application of AI technologies to improve the intelligence and capability of AVs. The goal of this Research Topic is to offer a technical forum for researchers and developers in AV research fields.
This Research Topic seeks review and original research articles focusing on the framework design, signal communication and fault diagnosis, algorithm development, and application of AVs.
Potential themes include, but are not limited to:
• Communication and connection technology for AVs
• Sensor modelling and fusion methodology
• Environment perception system solutions
• Intelligent driving behavior, prediction and decision-making
• Vehicle motion planning and control
• Trajectory tracking
• Vehicle dynamics control
• Automotive chassis design, modeling, and control for AVs
• Human collaboration with automated driving systems
• Human-vehicle interaction
• AI application to AVs' control
Automated vehicles (AVs) have shown dramatic potential to revolutionize human transportation and further improve mobility safety. As a promising substitution to replace human drivers, AVs must be capable of driving like experienced drivers and navigating safely in highly dynamic traffic flows. Much interesting research has emerged in recent decades to attempt to address essential issues of automated driving including environment perception, decision-making and planning, and adaptive control, etc. However, AVs are still far from mass production and launch because their performance is limited in various aspects. Therefore, crucial technologies of AVs need to be further improved to make reasonable driving behavior actions and flexibly plan motion in complicated traffic situations, to enhance stability, safety, and meet occupant driving performance expectations.
In light of current open challenges posed by highly dynamic traffic situations, vehicle dynamic nonlinearity, and coordination of multiple control objectives and actuators, this Research Topic aims to investigate new methods for signal processing and communication, decision-making, planning, and control of automated driving in real-world traffic scenarios. Artificial intelligence, which has been developed extensively in data modeling and decision inference, provides a powerful tool to enhance AVs' performance, especially in complex situations. Hence, we are also looking for studies focusing on the application of AI technologies to improve the intelligence and capability of AVs. The goal of this Research Topic is to offer a technical forum for researchers and developers in AV research fields.
This Research Topic seeks review and original research articles focusing on the framework design, signal communication and fault diagnosis, algorithm development, and application of AVs.
Potential themes include, but are not limited to:
• Communication and connection technology for AVs
• Sensor modelling and fusion methodology
• Environment perception system solutions
• Intelligent driving behavior, prediction and decision-making
• Vehicle motion planning and control
• Trajectory tracking
• Vehicle dynamics control
• Automotive chassis design, modeling, and control for AVs
• Human collaboration with automated driving systems
• Human-vehicle interaction
• AI application to AVs' control
