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Intelligent Transportation Systems II: Beyond Intelligent Vehicles
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Intelligent Transportation Systems II: Beyond Intelligent Vehicles

A topical collection in Applied Sciences (ISSN 2076-3417). This collection belongs to the section "Transportation and Future Mobility".

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Editors

Prof. Dr. Javier Alonso Ruiz

E-Mail Website
Collection Editor
Computer Engineering Department, INVETT Research Group, Universidad de Alcalá, Alcalá de Henares, Madrid, Spain
Interests: intelligent transportation systems; autonomous vehicles; control systems; driver assistance systems; artificial vision
Special Issues, Collections and Topics in MDPI journals
Dr. Jeroen Ploeg

E-Mail Website
Collection Editor
1. Lead Cooperative Driving, 2getthere B.V., Utrecht, The Netherlands;
2. Associate Professor (part-time), Mechanical Engineering Department, Dynamics and Control group, Eindhoven University of Technology, Eindhoven, The Netherlands
Interests: networked control; string stability; agent-based control; vehicle automation; platooning
Special Issues, Collections and Topics in MDPI journals
Dr. Angel Llamazares

E-Mail Website
Collection Editor
Postdoctoral Researcher, INVETT Research Group, Computer Engineering Department, Universidad de Alcalá, Alcalá de Henares, Spain
Interests: robotics; intelligent transportation systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Noelia Hernández Parra

E-Mail Website
Collection Editor
Assistant professor, Computer Engineering Department. INVETT Research Group. Universidad de Alcalá, Alcalá de Henares, Madrid, Spain
Interests: accurate indoor and outdoor global positioning; vehicle localization; autonomous vehicles; driver assistance systems; imaging and image analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Carlota Salinas

E-Mail Website
Collection Editor
Computer Engineering Department, Universidad de Alcalá, Alcalá de Henares, 28805 Madrid, Spain
Interests: computer vision; multi-sensory systems; 3D sensing; mapping and localization; autonomous vehicles and robotics
Special Issues, Collections and Topics in MDPI journals
Dr. Rubén Izquierdo

E-Mail Website
Collection Editor
INVETT Research Group, Universidad de Alcalá, Campus Universitario, Ctra, Madrid-Barcelona km, 33, 600, 28805 Alcalá de Henares, Spain
Interests: intelligent vehicles and traffic technologies; intelligent vehicles; user-based autonomous vehicle design; advanced vehicle and traffic perception and modeling systems; predictive perception systems
Special Issues, Collections and Topics in MDPI journals

Topical Collection Information

Dear Colleagues,

The development of intelligent vehicles is essential for improving urban mobility and for contributing to the development of smart cities. Also, the intelligent vehicle is the central pillar of the future of intelligent transport systems (ITS). Within the area of intelligent vehicles research there are still many challenges/areas for improvement: perception systems, scene understanding, localization and mapping, navigation, path planning, trajectory planning, vehicle control, etc.

If you look at the equipment of the vehicle, there are a variety of sensors. GPS, IMU, cameras, radars, and lidars are the most common. Lidars are the least preferred option in the industry, to avoid anti-aesthetic effects on the cars’ appearance. Cameras and lidars have experienced a small revolution thanks to the application of convolutional neural networks to the image processing. These sensors are used for localization (visual odometry, lidar odometry, 3D maps, map matching, etc.), perception (trajectory planning, scene understanding, traffic sign detection, drive-able space detection, obstacle avoidance, etc.), and so on. The aim of this Special Issue is to get a view of the latest works in these fields, and to give the reader a clear picture on the advances that are to come. Welcome topics include, but are not strictly limited to, the following:

  • Computer vision and image processing;
  • Lidar and 3D sensors;
  • Radar and other proximity sensors;
  • Advanced driver assistance systems onboard vehicles;
  • Self-driving car perception and navigation systems;
  • Navigation and path planning;
  • Automatic vehicle trajectory planning and control.

Prof. Dr. Javier Alonso Ruiz
Dr. Jeroen Ploeg
Dr. Angel Llamazares Llamazares
Prof. Dr. Noelia Hernández Parra
Dr. Carlota Salinas
Dr. Izquierdo Gonzalo Rubén
Collection Editors

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Keywords

  • computer vision
  • lidar
  • radar
  • 3D perception systems
  • convolutional neural networks
  • traffic light detection
  • collision mitigation brake systems
  • driving monitoring system
  • visual odometry
  • lidar odometry
  • 3D maps construction and localization
  • scene understanding
  • traffic sign detection
  • drivable space detection
  • obstacle detection

Related Special Issues

Published Papers (3 papers)

Download All Papers

2023

Jump to: 2021

12 pages, 4286 KiB  
Article
Sliding Mode Controller for Autonomous Tractor-Trailer Vehicle Reverse Path Tracking
by Yasser Bin Salamah
Appl. Sci. 2023, 13(21), 11998; https://doi.org/10.3390/app132111998 - 03 Nov 2023
Viewed by 680
Abstract
In the past few years, there has been a growing interest among researchers in developing control systems for autonomous vehicles, specifically for tractor-trailer systems. This newfound interest is driven by the potential benefits of enhancing safety, reducing costs, and addressing labor shortages in [...] Read more.
In the past few years, there has been a growing interest among researchers in developing control systems for autonomous vehicles, specifically for tractor-trailer systems. This newfound interest is driven by the potential benefits of enhancing safety, reducing costs, and addressing labor shortages in the industry. Two industries that could reap the rewards of these systems’ advancements are cargo and agriculture transportation. One of the challenging tasks for the truck trailer vehicle is driving in reverse. Backward path tracking of tractor-trailers is a complex control problem with practical applications. The difficulty in controlling the vehicle arises due to its unstable internal dynamics, coupled nonlinear terms, and the under-actuated nature of the system. There is also a limit to the angle at which the steering can be turned before the risk of a jackknife accident increases significantly. In response to these challenges, this paper introduces a robust sliding mode controller designed for path tracking in reverse-driving tractor-trailer systems. The novelty of our work lies in addressing these challenges, which have not been extensively studied in the past. The proposed controller is analyzed, and its performance is tested and verified using different scenarios. The simulation examples show superior control performance, and we anticipate that this novel controller holds the potential to be widely adopted as a fundamental component in the path-tracking algorithms of autonomous truck trailer systems. Full article
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14 pages, 4694 KiB  
Article
A Long-Term Traffic Flow Prediction Model Based on Variational Mode Decomposition and Auto-Correlation Mechanism
by Kaixin Guo, Xin Yu, Gaoxiang Liu and Shaohu Tang
Appl. Sci. 2023, 13(12), 7139; https://doi.org/10.3390/app13127139 - 14 Jun 2023
Cited by 4 | Viewed by 1562
Abstract
Traffic flow forecasting, as an integral part of intelligent transportation systems, plays a critical part in traffic planning. Previous studies have primarily focused on short-term traffic flow prediction, paying insufficient attention to long-term prediction. In this study, we propose a hybrid model that [...] Read more.
Traffic flow forecasting, as an integral part of intelligent transportation systems, plays a critical part in traffic planning. Previous studies have primarily focused on short-term traffic flow prediction, paying insufficient attention to long-term prediction. In this study, we propose a hybrid model that utilizes variational mode decomposition (VMD) and the auto-correlation mechanism for long-term prediction. In view of the periodic and stochastic characteristics of traffic flow, VMD is able to decompose the data into intrinsic mode functions with different frequencies, which in turn helps the model extract the internal features of the data and better capture the changes of traffic flow data in the cycle. Additionally, we improve the residual structure by adding a convolutional layer to propose a correction module and use it together with the auto-correlation mechanism to jointly build an encoder and decoder to extract features from different data components (intrinsic mode functions) and fuse the extracted features for output. To meet the requirements of long-term forecasting, we set the traffic flow forecast length to 4 levels: 96, 192, 336, and 720. We validated our model using the departure statistics dataset of a taxi parking lot at Beijing Capital International Airport and achieved the best prediction performance in terms of mean squared error and mean absolute error, compared to the baseline model. Full article
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2021

Jump to: 2023

42 pages, 1045 KiB  
Systematic Review
Shared Mobility Problems: A Systematic Review on Types, Variants, Characteristics, and Solution Approaches
by Kien Hua Ting, Lai Soon Lee, Stefan Pickl and Hsin-Vonn Seow
Appl. Sci. 2021, 11(17), 7996; https://doi.org/10.3390/app11177996 - 29 Aug 2021
Cited by 16 | Viewed by 4562
Abstract
The Shared Mobility Problems (SMP) with the rideshare concept based on sharing a vehicle are fast becoming a trend in many urban cities around the world. Examples of these problems are like ridesharing, carpooling, taxisharing, buspooling, vanpooling, and multi-modal ridesharing. This is the [...] Read more.
The Shared Mobility Problems (SMP) with the rideshare concept based on sharing a vehicle are fast becoming a trend in many urban cities around the world. Examples of these problems are like ridesharing, carpooling, taxisharing, buspooling, vanpooling, and multi-modal ridesharing. This is the new way to access transportation services by those who are propelling the sharing economy, where access rather than ownership is the new norm. This paper provides a systematic review of SMP using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) method. A total of 110 papers from the last decade are selected from 12 abstracts and citation databases to be reviewed and classified. This is done based on the problem types, variants, characteristics, and solution approaches. The current trends and analysis of the survey findings are also summarised. From this systematic review, it is observed that both the time window and multi-objective problems are popular among the researchers, while the minimisation of the total cost is the main concern in the literature of the SMP. Both static and dynamic cases of the SMP are the most researched where heuristic and metaheuristic approaches are widely adopted by the researchers in the literature. Finally, challenges and suggestions for future work are discussed and highlighted. Full article
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