Advancement on Smart Vehicles and Smart Travel

Dear Colleagues,

In the last 100 years, the attitude of people towards travelling has changed dramatically. The distances that can be covered in a given time has increased exponentially, making trips previously requiring days, or even months, affordable in a few hours.

Vehicle ownership has become affordable for everyone, leading to an increase in their density, especially in urban areas. Moreover, long-range travel has become affordable to a wider range of people, making air, train, and car traffic denser. Even e-commerce, which changed the buying paradigm, requires an upward effort in delivering systems. Last but not least, autonomous driving is becoming a reality, introducing additional issues to transportation networks.

Such changes have raised a set of new problems including increasing environmental pollution, managing of traffic, street security and surveillance and the debate on autonomous driving reliability.

On the other hand, the recent astonishing development of electronics has made available high-resolution sensors and impressive computing capabilities that have enabled the use of high-performance analysis, prediction and control techniques.

In this complex setting, research is needed to provide answers and solutions exploiting frontier technology, both in terms of hardware and algorithms, in order to mitigate and, where possible, solve the above-mentioned issues.

This Special Issue has the scope to collect high-profile original research articles and reviews dealing with all the discussed points. 

More precisely, areas may include (but are not limited to) the following:

• Vehicle detection, classification and tracking;
• Pedestrian detection and tracking;
• Vehicle/pedestrian behavior;
• Traffic jam detection;
• Car/pedestrian accidents;
• Activity monitoring Systems;
• Scene understanding;
• Environment city/street monitoring;
• Visual attention and visual saliency;
• Matching vehicles across cameras;
• Smart environments;
• Safety and security ;
• Technology for cognition;
• Navigation systems;
• Sensory substitution;
• Datasets and evaluation procedures;
• Systems and control engineering for traffic monitoring;
• Ethics in autonomous driving;
• Graph-based environmental analysis.

We look forward to receiving your contributions.

Dr. Marco Del-Coco
Dr. Sergio Trilles Oliver
Dr. Pierluigi Carcagni
Dr. Ditsuhi Ditsuhi Iskandaryan
Dr. Xin Wang
Guest Editors

Manuscript Submission Information

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• artificial intelligence
• machine learning
• video analytics
• data mining
• smart environments
• Internet of Things
• geoinformatics (GIS)
• spatio-temporal analysis
• air quality prediction

Title: Processing and Integration of Multimodal Image Data Supporting the Detection of Behaviors Related to Reduced Concentration Level of Motor Vehicle Users
Authors: Anton Smoliński, Paweł Forczmański and Adam Nowosielski
Affiliation: West Pomeranian University of Technology,
Abstract: This paper introduces a comprehensive framework for the detection of behaviors indicative of reduced concentration levels among motor vehicle operators, leveraging multimodal image data. By integrating dedicated deep learning models, our approach systematically analyzes RGB images, depth maps, and thermal imagery to identify signs of driver drowsiness and distraction. Our novel contribution includes the utilization of state-of-the-art convolutional neural networks (CNNs) and bidirectional long short-term memory (Bi-LSTM) networks for effective feature extraction and classification across diverse distraction scenarios. Additionally, we explore various data fusion techniques, demonstrating their impact on improving detection accuracy. The significance of this work lies in its potential to enhance road safety by providing more reliable and efficient tools for real-time monitoring of driver attentiveness, thereby reducing the risk of accidents caused by distraction and fatigue. The proposed methods are thoroughly evaluated using a multimodal benchmark dataset, with results showing their substantial capabilities leading to the development of safety-enhancing technologies for vehicular environments.