Sensors - Special Issue 2024 : Intelligent Vehicles Based on Computer Vision, Multimodal Sensing and Autonomous Systems for Complex Transportation

Dear Colleagues,

An intelligent vehicle is a vehicle enhanced with perception, reasoning, and actuating devices that enable the automation of driving tasks such as safe lane following, obstacle avoidance, overtaking slower traffic, following the vehicle ahead, assessing and avoiding dangerous situations, and determining the route. Over the past decade, deep-learning-based methods (CNN, RNN, LSTM, GAN, GNN, etc.) have been utilized with great success in intelligent vehicles, as they are comprehensively superior to traditional methods. In the field of complex transportation, the use of deep-learning-based computer vision, multimodal sensing, and autonomous systems has received extensive attention, enabling more accurate, efficient, and cheaper sensing, modelling, analysing, and decision making. These techniques make motoring safer, more convenient and more efficient, and have dramatically changed transportation systems. In the future, there will be huge demand and broad application prospects for intelligent vehicles.

Intelligent vehicle applications require knowledge on the following: 1) the state of the environment surrounding the vehicle; 2) the state of the driver and occupants; 3) communication with roadside infrastructure or other vehicles; 4) the position and the kinematic and dynamic state of the vehicle; and 5) access to digital maps and satellite data. The aim of this Special Issue is to present both original research and review articles on various disciplines of intelligent vehicle and applications, particularly computer vision, multimodal sensing, deep learning, and autonomous systems for intelligent transportation applications.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

-Traffic image/video quality enhancement in severe weather conditions;
-Traffic sign/light detection and recognition, and road/lane line detection;
-Driver monitoring;
-Vehicle forward collision warnings, blind spot monitoring, and lane departure warnings;
-Vehicle/cyclist/pedestrian detection, counting, tracking, and reidentification;
-Vehicular sensing (visible, infrared, ultrasound, radar, lidar, laser range finders, and so on);
-Simultaneous localization and mapping (SLAM);
-Behavioural decision making, path planning, and motion control;
-Congestion prediction and control, and accident prediction;

We look forward to receiving your contributions.

Prof. Dr. Jianming Zhang
Dr. Ke Gu
Guest Editors