About this Research Topic
Autonomous driving is a remarkable application of artificial intelligence that has gained widespread attention in recent years. The development of autonomous driving systems (ADSs) has been a topic of intensive research, and the approaches used to achieve autonomous behavior in vehicles range from classical control theory to machine learning. The field of autonomous driving is complex and multidisciplinary, encompassing various aspects of robotics, computer vision, control systems, and artificial intelligence.
ADSs are typically composed of three main components: environment sensing and localization, path planning, and path tracking. In environment sensing and localization, the vehicle needs to perceive its surroundings and determine its position relative to the environment. Path planning involves determining the vehicle's desired trajectory, while path tracking involves ensuring that the vehicle follows the planned trajectory as closely as possible. There are many different methods and algorithms used in these components, including machine learning algorithms that can learn from data and improve over time.
Brain-inspired control is a promising approach to autonomous driving that leverages artificial and spiking neural networks. The inspiration for this approach comes from the functioning of the human brain, which can process and respond to complex stimuli in real-time. Brain-inspired control is poised to play a significant role in the development of autonomous behavior, and research in this area has already led to remarkable advances.
This research topic aims to provide a comprehensive overview of the state-of-the-art in autonomous behavior with machine learning. The aim is to provide guidelines for building brain-inspired architectures for control and to highlight the importance of machine learning in this field.
We welcome research that increases the explainability of control systems, provides enhanced performance, and proposes energy efficient frameworks. The focus is on the applications of machine learning to autonomous driving systems, including but not limited to the following:
• Path and motion planning
• Collision avoidance
• Smart vehicle-user interfaces
• Vehicle stability and safety
• Control systems
ADSs are typically composed of three main components: environment sensing and localization, path planning, and path tracking. In environment sensing and localization, the vehicle needs to perceive its surroundings and determine its position relative to the environment. Path planning involves determining the vehicle's desired trajectory, while path tracking involves ensuring that the vehicle follows the planned trajectory as closely as possible. There are many different methods and algorithms used in these components, including machine learning algorithms that can learn from data and improve over time.
Brain-inspired control is a promising approach to autonomous driving that leverages artificial and spiking neural networks. The inspiration for this approach comes from the functioning of the human brain, which can process and respond to complex stimuli in real-time. Brain-inspired control is poised to play a significant role in the development of autonomous behavior, and research in this area has already led to remarkable advances.
This research topic aims to provide a comprehensive overview of the state-of-the-art in autonomous behavior with machine learning. The aim is to provide guidelines for building brain-inspired architectures for control and to highlight the importance of machine learning in this field.
We welcome research that increases the explainability of control systems, provides enhanced performance, and proposes energy efficient frameworks. The focus is on the applications of machine learning to autonomous driving systems, including but not limited to the following:
• Path and motion planning
• Collision avoidance
• Smart vehicle-user interfaces
• Vehicle stability and safety
• Control systems
Keywords: Robotics, autonomous behavior, artificial neural networks, spiking neural networks, Neuromorphic engineering, Neuromorphic computing Neuromorphic control
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.
