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Remote Sensing
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Sensors and Artificial Intelligence in Autonomous Vehicles
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Sensors and Artificial Intelligence in Autonomous Vehicles

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 6127

Special Issue Editor

Dr. Mahdi Rezaei

E-Mail Website
Guest Editor
Institute for Transport Studies, University of Leeds, Leeds LS2 9JT, UK
Interests: computer vsion; machine learning; intelligent transportation systems; autonomous vehicles; vehicle automation; advanced driver assistance systems; sensor fusion; driver behaviour monitoring; object detection; semantic segmentation; traffic scene understanding and perception
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Autonomous vehicles (AVs), self-driving cars, and robotaxis have recently attracted a new level of attention from both academia and industry.

Different types of sensors (camera, laser scanner, radars, LiDAR, ultrasound, GPS, etc.) along with AI methodologies, machine learning, deep learning, and large datasets are playing major roles in further development of the AVs with a higher level of intelligence and mobility. Different types of sensors have led to increased safety, efficiency, capabilities, and more accuracies for object detection, 3D road modelling, traffic environment perception, and driver behaviour monitoring.

The Special Issue “Sensors and Artificial Intelligence in Autonomous Vehicles” welcomes open call submissions in the state-of-the-art current and emerging technologies and methodologies in utilisation of single or multi-sensors for vehicle automation and AVs.

Prospective authors are welcome to submit original research (not published or currently under consideration by any other publication or conference) and technical papers in the field. Using sensor(s) technologies, the expected topics include but are not limited to the following:

  • Vision, Radar, and LiDAR based perception
  • Night vision in challenging lighting conditions
  • Multi-modal and multi-sensor data fusion
  • Autonomous driving, driverless cars, and robotaxis
  • Sensors in resuming the control between human driver and automated driving mode
  • Scene understanding and object detection
  • Driver behavior monitoring/modelling
  • Pedestrian and road users monitoring/modelling
  • Advanced driver assistance systems (ADAS)
  • Sensor applications in connected and automated vehicles (CAVs)
  • AI and machine learning in AVs
  • Human factors and safety in self-driving cars

You may choose our Joint Special Issue in Sensors.

Dr. Mahdi Rezaei
Guest Editor

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.

Keywords

  • Autonomous vehicles
  • Sensor technologies
  • Sensor fusion
  • Traffic, road, and environment perception
  • Situation awareness
  • Driver behavior
  • Gesture recognition
  • Human factors and safety
  • Object detection
  • Pedestrian detection
  • Deep learning and big data
  • Autonomous navigation

Published Papers (2 papers)

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Research

14 pages, 3444 KiB  
Article
Tide-Inspired Path Planning Algorithm for Autonomous Vehicles
by Heba Kurdi, Shaden Almuhalhel, Hebah Elgibreen, Hajar Qahmash, Bayan Albatati, Lubna Al-Salem and Ghada Almoaiqel
Remote Sens. 2021, 13(22), 4644; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13224644 - 18 Nov 2021
Cited by 3 | Viewed by 1766
Abstract
With the extensive developments in autonomous vehicles (AV) and the increase of interest in artificial intelligence (AI), path planning is becoming a focal area of research. However, path planning is an NP-hard problem and its execution time and complexity are major concerns when [...] Read more.
With the extensive developments in autonomous vehicles (AV) and the increase of interest in artificial intelligence (AI), path planning is becoming a focal area of research. However, path planning is an NP-hard problem and its execution time and complexity are major concerns when searching for optimal solutions. Thus, the optimal trade-off between the shortest path and computing resources must be found. This paper introduces a path planning algorithm, tide path planning (TPP), which is inspired by the natural tide phenomenon. The idea of the gravitational attraction between the Earth and the Moon is adopted to avoid searching blocked routes and to find a shortest path. Benchmarking the performance of the proposed algorithm against rival path planning algorithms, such as A*, breadth-first search (BFS), Dijkstra, and genetic algorithms (GA), revealed that the proposed TPP algorithm succeeded in finding a shortest path while visiting the least number of cells and showed the fastest execution time under different settings of environment size and obstacle ratios. Full article
(This article belongs to the Special Issue Sensors and Artificial Intelligence in Autonomous Vehicles)
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25 pages, 9799 KiB  
Article
Robust and Efficient Trajectory Replanning Based on Guiding Path for Quadrotor Fast Autonomous Flight
by Yinghao Zhao, Li Yan, Yu Chen, Jicheng Dai and Yuxuan Liu
Remote Sens. 2021, 13(5), 972; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13050972 - 04 Mar 2021
Cited by 17 | Viewed by 2690
Abstract
Path planning is one of the key parts of unmanned aerial vehicle (UAV) fast autonomous flight in an unknown cluttered environment. However, real-time and stability remain a significant challenge in the field of path planning. To improve the robustness and efficiency of the [...] Read more.
Path planning is one of the key parts of unmanned aerial vehicle (UAV) fast autonomous flight in an unknown cluttered environment. However, real-time and stability remain a significant challenge in the field of path planning. To improve the robustness and efficiency of the path planning method in complex environments, this paper presents RETRBG, a robust and efficient trajectory replanning method based on the guiding path. Firstly, a safe guiding path is generated by using an improved A* and path pruning method, which is used to perceive the narrow space in its surrounding environment. Secondly, under the guidance of the path, a guided kinodynamic path searching method (GKPS) is devised to generate a safe, kinodynamically feasible and minimum-time initial path. Finally, an adaptive optimization function with two modes is proposed to improve the optimization quality in complex environments, which selects the optimization mode to optimize the smoothness and safety of the path according to the perception results of the guiding path. The experimental results demonstrate that the proposed method achieved good performance both in different obstacle densities and different resolutions. Compared with the other state-of-the-art methods, the quality and success rate of the planning result are significantly improved. Full article
(This article belongs to the Special Issue Sensors and Artificial Intelligence in Autonomous Vehicles)
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