Autonomous Driving: Advancements in Cognitive Perception Systems for Increased Level Autonomy

Dear Colleagues,

In recent decades, the research on autonomous driving technologies enabled the automotive industry to introduce vehicles endorsed with Advanced Driver-Assistance Systems (ADAS) to the market. These systems render the daily driving experience safer and more attractive to humans. The majority of the existing assistive driving solutions are ranked between Level-1 and Level-3 autonomy, which involves mainly supportive systems. However, no one in the industry is even close to attaining Level-4 and Level-5 autonomy, a challenge that demands vehicles’ operation in any road network and weather condition. It is evident that reaching the ultimate autonomous operation (Level-5) necessitates future vehicles to be endorsed with advanced perception and cognition capabilities that will allow them to cope with urban, rural and semi-structured environments. Recent advancements in sensors provided 3D data that further augment the perception capabilities of autonomous vehicles, while the machine learning society released powerful neural networks that amplify the vehicle’s awareness regarding its surroundings. However, few such approaches reached mass production—mainly due to the fact that such solutions typically require the existence of an abundance of data to train the respective models and powerful computational units, which hinders their adoption from the automotive industry.

This Special Issue focuses on the recent advances in perception and cognition technologies of autonomous vehicles. Scientists are encouraged to submit their outstanding work towards addressing this issue, focusing on (but not limited to) the topics listed below:

• Semantic segmentation of urban and/or rural environments.
• Efficient navigation in urban environments.
• Navigation in semi-structured and GPS-denied areas.
• Trajectory planning and following for narrow areas.
• Vehicle cognitive reaction methods.
• Pedestrian detection.
• Localization and navigation in parking areas.
• Hybrid SLAM applications.
• Collision avoidance and safe vehicles reactions.
• Adaptive decision making and dependable behavior.
• Light deep learning for autonomous driving.
• Dicentralised control schemes for driverless vehicles.
• Fleet management systems for self-driving vehicles.
• Driverless vehicles in sypply chain and logistics.

Research, position and survey papers are welcome, in addition to papers proposing new datasets and novel evaluation methods.

Prof. Dr. Antonios Gasteratos
Dr. Ioannis Kostavelis
Guest Editors

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