The Application of Deep Learning in Intelligent Vehicle Perception Systems

Dear Colleagues,

Deep learning algorithms have achieved state-of-the-art performance in numerous domains, including computer vision, natural language processing, speech recognition, recommendation systems, and so on. The continuous breakthroughs of deep learning have driven the development of various industries. In recent years, a large number of researchers have applied deep learning to the field of intelligent driving, especially intelligent vehicle perception systems, which enable vehicles to perceive, understand, and interpret their surroundings autonomously or with minimal human intervention. These researchers play a crucial role in the development of autonomous vehicles and advanced driver assistance systems (ADASs), enhancing safety, efficiency, and overall driving experience.

This Special Issue is focused on the study, research, and discovery related to the application of deep learning algorithms to intelligent vehicle perception systems, which mainly includes identifying various objects, such as pedestrians, vehicles, traffic signs, and obstacles, through object detection and sematic segmentation. This allows the vehicle to make informed decisions based on its surroundings; to provide a comprehensive understanding of the environment by fusing the data from various sensors such as cameras, LiDAR, and radar; to detect driver fatigue, distraction, or impairment and alert the driver or initiate corrective actions when necessary; to predict the whether the maintenance of vehicles is necessary by analyzing sensor data and detecting anomalies or signs of potential equipment failure; to predict traffic flow patterns and congestion levels based on historical data, real-time traffic updates, and environmental factors; and to train and simulate various driving scenarios in virtual environments. Therefore, deep learning algorithms could be used to enable autonomous vehicles to learn from diverse situations and improve their decision-making capabilities without real-world risks.

Dr. Lie Yang
Dr. Xiangkun He
Guest Editors

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• deep learning
• intelligent vehicle perception systems
• sensors
• autonomous vehicles
• decision-making