Advances in Remote Sensing of Solving Challenges in Autonomous Driving and Safety Analysis

Dear Colleagues,

As safety is the prime priority and the key issue in commercializing autonomous vehicles, the main challenge in the research field has become to increase safety, and more in general the system performance, in critical and unique working conditions. This includes for instance: precise localization in snow–rain road conditions, generating accurate and largescale maps by SLAM technologies, far detection of construction areas for smooth path planning, maneuvering with the existence of unprotected turns, making a robust decision on classifying stationary vehicles as obstacles or temporarily stopped due to traffic jams and traffic signal recognition in sun glare. Without robustly solving these issues, autonomous driving will stay in the demonstration loop, and the deployment of autonomous vehicles will be limited to certain operating conditions. In addition, these problems may lead to deadly traffic accidents and produce a considerable negative impact on societies to accept running autonomous vehicles in streets.

Analyzing the reasons for these problems and clearly illustrating them are the cornerstone to investigating the relevant effects on the autopilot’s performance and proposing the corresponding optimal solutions. Remote sensing and image processing applications play the main role in designing optimal solutions based on sensory and observation data such as modeling the changes in the pattern distribution of LIDAR 3D point clouds in snowfall weather conditions and improving the localization accuracy by matching map observation environmental features. Therefore, this Special Issue aims to add value to the autonomous vehicle research field by demonstrating and analyzing critical and unique problems of mapping, localization, perception and path-planning modules that are rarely discussed in the literature and currently considered as futuristic matters.

Eventually, we hope to significantly contribute to increasing the safety of autonomous driving and provide prominent and robust solutions through the published papers.

Dr. Mohammad Aldibaja
Dr. Sufyan Ali Memon
Dr. Andrea Masiero
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• autonomous vehicles
• 3D point cloud analysis
• path planning with unprotected turns
• robust perception of construction areas
• SLAM-based mapping in challenging environments
• road pavement assessment for driving safety analysis
• object status classification in urban traffic conditions
• LIDAR/radar-based localization systems in adverse weather conditions
• map quality analysis and enhancement
• lane graph generation
• safe AI integration technologies into autonomous driving