Complex Traffic Scene Perception and Understanding for Autonomous Intelligent Unmanned Systems

https://www.frontiersin.org/research-topics/56815/complex-traffic-scene-perception-and-understanding-for-autonomous-intelligent-unmanned-systems?utm_source=F-RTM&utm_medium=TED1&utm_campaign=PRD_TED1_

As we witness the rapid advancements in autonomous intelligent unmanned systems, the integration of neuroscience-inspired approaches becomes ever more critical. In the near future, these systems, comprising self-driving vehicles, drones, and smart infrastructure, will work together in a coordinated manner, emulating the complex neural networks that govern our brain's functions. Understanding the neural mechanisms underlying perception and decision-making processes offers valuable insights for the design and development of more efficient, reliable, and safe autonomous systems.  

Firstly, self-driving vehicles, equipped with artificial intelligence inspired by the human brain, will significantly reduce traffic congestion and improve overall efficiency. By utilizing advanced neural network algorithms to communicate with each other and the infrastructure, these vehicles will optimize routes, reduce travel time, and enable platooning to minimize the overall fuel consumption and carbon emissions.

Secondly, the incorporation of drones in the transportation network, leveraging bio-inspired algorithms, will transform delivery services, allowing for faster and more efficient shipments. Drones equipped with navigation systems inspired by animal brains will be able to traverse complex urban environments and reach remote locations, providing a more accessible and convenient means of transporting goods.

Moreover, the smart infrastructure, functioning as the central nervous system of this future transportation network, will enable real-time traffic monitoring, predictive maintenance, and optimal traffic flow management through the implementation of advanced neural network models. This will not only minimize accidents but also enhance the longevity of roads and other transportation assets.

In conclusion, the integration of neuroscience-inspired autonomous intelligent unmanned systems into the future transportation network will greatly enhance the overall efficiency, safety, and sustainability of travel and transportation. This technological shift, based on our understanding of the brain and neural networks, will ultimately lead to a more connected, accessible, and environmentally responsible world.

The research topic aims to foster interdisciplinary collaborations and provide a comprehensive understanding of how neuroscience-inspired approaches can contribute to the design and operation of autonomous intelligent unmanned systems in the complex traffic scenarios of future transportation networks.

We invite submissions of original research articles, reviews, perspectives, and commentaries that focus on, but are not limited to, the following topics:

- Neuroscience-inspired algorithms and models for perception, learning, and decision-making in complex traffic environments.

- Cognitive and computational neuroscience of human drivers for improved human-robot interaction and collaboration.

- Brain-computer interfaces for enhanced control and communication in autonomous intelligent unmanned systems.

- Neuromorphic computing and hardware for efficient real-time processing and adaptation in dynamic traffic scenarios.

- Ethical and social implications of incorporating neuroscience-inspired approaches in autonomous intelligent transportation systems.  

Topic Editors