Advances in Autonomous Driving and Smart Transportation

Dear Colleagues,

Research efforts to achieve autonomous driving capabilities have increased significantly in the last decade. The technology to empower autonomous vehicles with different sensors, including LiDAR, RADAR, ultrasonic devices, and cameras is developing at a rapid pace, and the data collected through these platforms are extremely useful for developing AI/ML models. One of the most significant advantages of autonomous cars is their potential to make roads safer by enabling them to navigate the environment safely and efficiently. This Special Issue invites contributions on the following topics.

• Perception and sensing in adverse weather conditions like traversing in fog, rain, and snow, which is an important aspect to consider for safer navigation. 
• Advances in sensor fusion, perception enhancement, classification, and localization techniques are important solutions to enhance autonomous vehicles’ capabilities.
• Deep learning models for object detection are extremely desirable in self-driving cars.
• All supervised deep learning models require labeled datasets to train the algorithms. As the labeling effort is very cumbersome and time-consuming, advancements in auto-labeling techniques are of great importance to annotate large datasets.
• Advances in vehicle connectivity solutions that connect vehicles to other vehicles (V2V), infrastructure (V2I), the electric network (V2N), and even pedestrians (V2P) are important for safer navigation.
• Navigation of unmanned ground vehicles (UGVs) in off-road/unstructured environment is of prime concern in defense applications. Advances in obstacle detection and navigating safely in this complex terrain is a major challenge in off-road autonomous driving.

Dr. Lalitha Dabbiru
Dr. Christopher T. Goodin
Guest Editors

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• automated driving
• machine learning
• perception and sensing
• object detection
• adverse weather models