About this Research Topic
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
Keywords: automated vehicles, signal processing, driving behavior decision, trajectory planning, dynamics control, automotive chassis design and execution, human-vehicle interaction
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.