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The Intelligent Sensing Technology of Transportation System

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (20 October 2023) | Viewed by 38581

Special Issue Editors

California Partners for Advanced Transportation Technology (PATH), University of California at Berkeley, Richmond, CA 94804, USA
Interests: advanced driver assistance systems; intelligent transportation system; machine learning; neural networks; prognostic and health management; systems engineering
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Systems Engineering Laboratory, Zhangzhou Campus of Xiamen University, Zhangzhou 361005, China
Interests: intelligent transportation system; nonlinear control; robust control; mechatronics integration and signal processing.
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-sen University, Kaohsiung 804, Taiwan
Interests: artificial intelligent; automatic control; signal processing; intelligent vehicle; robotic; system engineering
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
California Partners for Advanced Transportation Technology (PATH), University of California at Berkeley, Richmond, CA 94804, USA
Interests: automatic vehicle; advanced driver assistance system; machine learning for intelligent autonomy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Due to the prevalence of artificial intelligence in recent years, new technologies have been continuously developed in transportation. Many intelligent sensing technologies are being used to improve the safety and stability of transportation systems. It is very exhilarating to see safer and more convenient transportation technologies being revolutionized to enhance the well-being of human society.

Therefore, this Special Issue aims to collect valuable and innovative sensing technologies from original research and review articles on transportation systems.

The following fields are included:

  • Intelligent sensing technology of automated vehicles, advanced driver-assistance systems (ADAS), in-vehicle environments, and traffic scenes;
  • Object identification, tracking, prediction, and navigation technology for transportation systems;
  • Image, LiDAR, radar, etc.; signal processing algorithms for transportation systems;
  • Multi-sensor fusion technology for transportation systems;
  • Safety evaluation of automated driving systems;
  • Dynamic urban traffic scene understanding;
  • Analyzing the behaviors of on-road pedestrians;
  • In-vehicle driver status evaluation;
  • Construction of 5G-based transportation systems;
  • Prognostic and health management for transportation system.

Dr. I-Hsi Kao
Dr. Yi-Horng Lai
Prof. Dr. Jau-Woei Perng
Prof. Dr. Ching-Yao Chan
Guest Editors

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Published Papers (19 papers)

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19 pages, 2720 KiB  
Article
Diagnosis and Study of Mechanical Vibrations in Cargo Vehicles Using ISO 2631-1:1997
by Alejandro Medina Santiago, Jorge Antonio Orozco Torres, Carlos Arturo Hernández Gracidas, Salvador Hernández Garduza and Javier Duarte Franco
Sensors 2023, 23(24), 9677; https://doi.org/10.3390/s23249677 - 07 Dec 2023
Viewed by 758
Abstract
This study presents the design and implementation of an electronic system aimed at capturing vibrations produced during truck operation. The system employs a graphical interface to display vibration levels, ensuring the necessary comfort and offering indicators as a solution to mitigate the damage [...] Read more.
This study presents the design and implementation of an electronic system aimed at capturing vibrations produced during truck operation. The system employs a graphical interface to display vibration levels, ensuring the necessary comfort and offering indicators as a solution to mitigate the damage caused by these vibrations. Additionally, the system alerts the driver when a mechanical vibration that could potentially impact their health is detected. The field of health is rigorously regulated by various international standards and guidelines. The case of mechanical vibrations, particularly those transmitted to the entire body of a seated individual, is no exception. Internationally, ISO 2631-1:1997/Amd 1:2010 oversees this study. The system was designed and implemented using a blend of hardware and software. The hardware components comprise a vibration sensor, a data acquisition card, and a graphical user interface (GUI). The software components consist of a data acquisition and processing library, along with a GUI development framework. The system underwent testing in a controlled environment and demonstrated stability and robustness. The GUI proved to be intuitive and could be integrated into modern vehicles with built-in displays. The findings of this study suggest that the proposed system is a viable and effective method for capturing vibrations in trucks and informing drivers about vibration levels. This system has the potential to enhance the comfort and safety of truck drivers. Full article
(This article belongs to the Special Issue The Intelligent Sensing Technology of Transportation System)
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16 pages, 3528 KiB  
Article
Social Distance Approximation on Public Transport Using Stereo Depth Camera and Passenger Pose Estimation
by Daniel Steven Bell, Philip James and Martín López-García
Sensors 2023, 23(24), 9665; https://doi.org/10.3390/s23249665 - 07 Dec 2023
Viewed by 731
Abstract
In order to effectively balance enforced guidance/regulation during a pandemic and limit infection transmission, with the necessity for public transportation services to remain safe and operational, it is imperative to understand and monitor environmental conditions and typical behavioural patterns within such spaces. Social [...] Read more.
In order to effectively balance enforced guidance/regulation during a pandemic and limit infection transmission, with the necessity for public transportation services to remain safe and operational, it is imperative to understand and monitor environmental conditions and typical behavioural patterns within such spaces. Social distancing ability on public transport as well as the use of advanced computer vision techniques to accurately measure this are explored in this paper. A low-cost depth-sensing system is deployed on a public bus as a means to approximate social distancing measures and study passenger habits in relation to social distancing. The results indicate that social distancing on this form of public transport is unlikely for an individual beyond a 28% occupancy threshold, with an 89% chance of being within 1–2 m from at least one other passenger and a 57% chance of being within less than one metre from another passenger at any one point in time. Passenger preference for seating is also analysed, which clearly demonstrates that for typical passengers, ease of access and comfort, as well as seats having a view, are preferred over maximising social-distancing measures. With a highly detailed and comprehensive set of acquired data and accurate measurement capability, the employed equipment and processing methodology also prove to be a robust approach for the application. Full article
(This article belongs to the Special Issue The Intelligent Sensing Technology of Transportation System)
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16 pages, 3196 KiB  
Article
Self-Aware Cybersecurity Architecture for Autonomous Vehicles: Security through System-Level Accountability
by Akwasi Adu-Kyere, Ethiopia Nigussie and Jouni Isoaho
Sensors 2023, 23(21), 8817; https://doi.org/10.3390/s23218817 - 30 Oct 2023
Viewed by 1377
Abstract
The inherent dynamism of recent technological advancements in intelligent vehicles has seen multitudes of noteworthy security concerns regarding interactions and data. As future mobility embraces the concept of vehicles-to-everything, it exacerbates security complexities and challenges concerning dynamism, adaptiveness, and self-awareness. It calls for [...] Read more.
The inherent dynamism of recent technological advancements in intelligent vehicles has seen multitudes of noteworthy security concerns regarding interactions and data. As future mobility embraces the concept of vehicles-to-everything, it exacerbates security complexities and challenges concerning dynamism, adaptiveness, and self-awareness. It calls for a transition from security measures relying on static approaches and implementations. Therefore, to address this transition, this work proposes a hierarchical self-aware security architecture that effectively establishes accountability at the system level and further illustrates why such a proposed security architecture is relevant to intelligent vehicles. The article provides (1) a comprehensive understanding of the self-aware security concept, with emphasis on its hierarchical security architecture that enables system-level accountability, and (2) a deep dive into each layer supported by algorithms and a security-specific in-vehicle black box with external virtual security operation center (VSOC) interactions. In contrast to the present in-vehicle security measures, this architecture introduces characteristics and properties that enact self-awareness through system-level accountability. It implements hierarchical layers that enable real-time monitoring, analysis, decision-making, and in-vehicle and remote site integration regarding security-related decisions and activities. Full article
(This article belongs to the Special Issue The Intelligent Sensing Technology of Transportation System)
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28 pages, 5492 KiB  
Article
Implementation of MEC-Assisted Collective Perception in an Integrated Artery/Simu5G Simulation Framework
by Gergely Attila Kovács and László Bokor
Sensors 2023, 23(18), 7968; https://doi.org/10.3390/s23187968 - 19 Sep 2023
Viewed by 1105
Abstract
Advanced vehicle-to-everything (V2X) safety applications must operate with ultra-low latency and be highly reliable. Therefore, they require sophisticated supporting technologies. This is especially true for cooperative applications, such as Collective Perception (CP), where a large amount of data constantly flows among vehicles and [...] Read more.
Advanced vehicle-to-everything (V2X) safety applications must operate with ultra-low latency and be highly reliable. Therefore, they require sophisticated supporting technologies. This is especially true for cooperative applications, such as Collective Perception (CP), where a large amount of data constantly flows among vehicles and between vehicles and a network intelligence server. Both low and high-level support is needed for such an operation, meaning that various access technologies and other architectural elements also need to incorporate features enabling the effective use of V2X applications with strict requirements. The new 5G core architecture promises even more supporting technologies, like Multi-access Edge Computing (MEC). To test the performance of these technologies, an integrated framework for V2X simulations with 5G network elements is proposed in the form of combining Simu5G, a standalone 5G implementation, with the go-to V2X-simulator, Artery. As a first step toward a fully functional MEC-assisted CP Service, an extension to Simu5G’s edge implementation is introduced. The edge application is responsible for dispatching the Collective Perception Messages generated by the vehicles via the 5G connectivity so that a MEC server provided by the network can process incoming data. Simulation results prove the operability of the proposed integrated system and edge computing’s potential in assisting V2X scenarios. Full article
(This article belongs to the Special Issue The Intelligent Sensing Technology of Transportation System)
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23 pages, 7109 KiB  
Article
Ultra-Wideband-Based Time Occupancy Analysis for Safety Studies
by Salah Fakhoury and Karim Ismail
Sensors 2023, 23(17), 7551; https://doi.org/10.3390/s23177551 - 31 Aug 2023
Viewed by 1306
Abstract
This study investigates the use of ultra-wideband (UWB) tags in traffic conflict techniques (TCT) for the estimation of time occupancy in a real-world setting. This study describes UWB technology and its application in the TCT framework. Many experiments were conducted to evaluate the [...] Read more.
This study investigates the use of ultra-wideband (UWB) tags in traffic conflict techniques (TCT) for the estimation of time occupancy in a real-world setting. This study describes UWB technology and its application in the TCT framework. Many experiments were conducted to evaluate the accuracy of the occupancy time measurement using a UWB-based tag. The UWB performance was measured using data from UWB tags as well as a video camera system by subtracting the time occupancy within a conflict zone. The results show that the UWB-based system can be utilized to estimate occupancy time with a mean absolute error difference from ground truth measurements of 0.43 s in the case of using two tags and 0.06 s in the case of using one tag in an 8 m × 8 m study area with double-sided two-way communication. This study also highlights the advantages and limitations of using UWB technology in TCT and discusses potential applications and future research directions. The findings of this study suggest that the UWB-based localization of multiple tags needs further improvements to enable consistent multi-tag tracking. In future work, this technology could be utilized to estimate post-encroachment time (PET) in various traffic scenarios, which could improve road safety and reduce the risk of collisions. Full article
(This article belongs to the Special Issue The Intelligent Sensing Technology of Transportation System)
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17 pages, 7008 KiB  
Article
A Framework for Optimal Navigation in Situations of Localization Uncertainty
by Charifou Orou Mousse, Mohamed Benrabah, François Marmoiton, Alexis Wilhelm and Roland Chapuis
Sensors 2023, 23(16), 7237; https://doi.org/10.3390/s23167237 - 17 Aug 2023
Viewed by 901
Abstract
The basic functions of an autonomous vehicle typically involve navigating from one point to another in the world by following a reference path and analyzing the traversability along this path to avoid potential obstacles. What happens when the vehicle is subject to uncertainties [...] Read more.
The basic functions of an autonomous vehicle typically involve navigating from one point to another in the world by following a reference path and analyzing the traversability along this path to avoid potential obstacles. What happens when the vehicle is subject to uncertainties in its localization? All its capabilities, whether path following or obstacle avoidance, are affected by this uncertainty, and stopping the vehicle becomes the safest solution. In this work, we propose a framework that optimally combines path following and obstacle avoidance while keeping these two objectives independent, ensuring that the limitations of one do not affect the other. Absolute localization uncertainty only has an impact on path following, and in no way affects obstacle avoidance, which is performed in the robot’s local reference frame. Therefore, it is possible to navigate with or without prior information, without being affected by position uncertainty during obstacle avoidance maneuvers. We conducted tests on an EZ10 shuttle in the PAVIN experimental platform to validate our approach. These experimental results show that our approach achieves satisfactory performance, making it a promising solution for collision-free navigation applications for mobile robots even when localization is not accurate. Full article
(This article belongs to the Special Issue The Intelligent Sensing Technology of Transportation System)
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27 pages, 4014 KiB  
Article
Minimal Information Data-Modelling (MID) and an Easily Implementable Low-Cost SHM System for Use on a Short-Span Bridge
by Connor O’Higgins, David Hester, Patrick McGetrick, Elizabeth J. Cross, Wai Kei Ao and James Brownjohn
Sensors 2023, 23(14), 6328; https://doi.org/10.3390/s23146328 - 12 Jul 2023
Cited by 4 | Viewed by 765
Abstract
Structural Health Monitoring (SHM) is a technique that involves gathering information to ensure that a structure is safe and behaving as expected. Within SHM, vibration-based monitoring is generally seen as one of the more cost-effective types of monitoring. However, vibration-based monitoring has mostly [...] Read more.
Structural Health Monitoring (SHM) is a technique that involves gathering information to ensure that a structure is safe and behaving as expected. Within SHM, vibration-based monitoring is generally seen as one of the more cost-effective types of monitoring. However, vibration-based monitoring has mostly been undertaken on long-span bridges using data collected with a dense network of sensors. Historically, the logistical difficulty of collecting data on short- and medium-span bridges has meant that the usefulness of vibration-based methods on these bridges is largely unknown. Therefore, this study proposes Minimal Information Data-modelling (MID). MID is an approach that utilises low-cost, easily implementable sensors that are potentially feasible for operators to purchase and operate across a network. This approach will be investigated to determine whether MID is a feasible approach for monitoring short- and medium- span bridges. The results from MID were assessed to determine whether they could detect a suitably small shift in frequency, which is indicative of damage. It was determined that the data models could reliably detect frequency shifts as low as 0.01 Hz. This magnitude of frequency shift is similar to the level of frequency shift reported for a range of bridge damage cases found by others and validated with FE models. The accuracy achieved by the data models indicates that MID could potentially be used as a damage detection method. The cost of the equipment used to collect the data was approximately £370, demonstrating that it is feasible to use MID to monitor bridges across an entire network. Full article
(This article belongs to the Special Issue The Intelligent Sensing Technology of Transportation System)
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17 pages, 3471 KiB  
Article
A Flexible Traffic Signal Coordinated Control Approach and System on Complicated Transportation Control Infrastructure
by Songhang Chen, Chunlin Shang and Fenghua Zhu
Sensors 2023, 23(13), 5796; https://doi.org/10.3390/s23135796 - 21 Jun 2023
Viewed by 1009
Abstract
The transportation control infrastructure serves as the foundation for regional traffic signal control. However, in practice, this infrastructure is often imperfect and complex, characterized by factors such as heterogeneity and uncertainty, which pose significant challenges to existing methods and systems. Therefore, this paper [...] Read more.
The transportation control infrastructure serves as the foundation for regional traffic signal control. However, in practice, this infrastructure is often imperfect and complex, characterized by factors such as heterogeneity and uncertainty, which pose significant challenges to existing methods and systems. Therefore, this paper proposes a novel approach to coordinated traffic signal control that emphasizes flexibility. To achieve this flexibility, we combine the flexible model of complex networks with robust fuzzy control methods. This approach enables us to overcome the complexity of the transportation control infrastructure and ensure efficient management of traffic signals. Additionally, to ensure long-term operational ease, we develop a regional traffic signal control system using steam computing technology, which provides high scalability and compatibility. Finally, computational experiments are performed to validate adaptability and performance of our proposed approach. Full article
(This article belongs to the Special Issue The Intelligent Sensing Technology of Transportation System)
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17 pages, 6308 KiB  
Article
Radar/INS Integration and Map Matching for Land Vehicle Navigation in Urban Environments
by Mohamed Elkholy, Mohamed Elsheikh and Naser El-Sheimy
Sensors 2023, 23(11), 5119; https://doi.org/10.3390/s23115119 - 27 May 2023
Cited by 3 | Viewed by 1432
Abstract
Autonomous navigation requires multi-sensor fusion to achieve a high level of accuracy in different environments. Global navigation satellite system (GNSS) receivers are the main components in most navigation systems. However, GNSS signals are subject to blockage and multipath effects in challenging areas, e.g., [...] Read more.
Autonomous navigation requires multi-sensor fusion to achieve a high level of accuracy in different environments. Global navigation satellite system (GNSS) receivers are the main components in most navigation systems. However, GNSS signals are subject to blockage and multipath effects in challenging areas, e.g., tunnels, underground parking, and downtown or urban areas. Therefore, different sensors, such as inertial navigation systems (INSs) and radar, can be used to compensate for GNSS signal deterioration and to meet continuity requirements. In this paper, a novel algorithm was applied to improve land vehicle navigation in GNSS-challenging environments through radar/INS integration and map matching. Four radar units were utilized in this work. Two units were used to estimate the vehicle’s forward velocity, and the four units were used together to estimate the vehicle’s position. The integrated solution was estimated in two steps. First, the radar solution was fused with an INS through an extended Kalman filter (EKF). Second, map matching was used to correct the radar/INS integrated position using OpenStreetMap (OSM). The developed algorithm was evaluated using real data collected in Calgary’s urban area and downtown Toronto. The results show the efficiency of the proposed method, which had a horizontal position RMS error percentage of less than 1% of the distance traveled for three minutes of a simulated GNSS outage. Full article
(This article belongs to the Special Issue The Intelligent Sensing Technology of Transportation System)
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15 pages, 3461 KiB  
Article
Accurate 3D to 2D Object Distance Estimation from the Mapped Point Cloud Data
by Saidrasul Usmankhujaev, Shokhrukh Baydadaev and Jang Woo Kwon
Sensors 2023, 23(4), 2103; https://doi.org/10.3390/s23042103 - 13 Feb 2023
Cited by 2 | Viewed by 3262
Abstract
Distance estimation is one of the oldest and most challenging tasks in computer vision using only a monocular camera. This can be challenging owing to the presence of occlusions, noise, and variations in the lighting, texture, and shape of objects. Additionally, the motion [...] Read more.
Distance estimation is one of the oldest and most challenging tasks in computer vision using only a monocular camera. This can be challenging owing to the presence of occlusions, noise, and variations in the lighting, texture, and shape of objects. Additionally, the motion of the camera and objects in the scene can affect the accuracy of the distance estimation. Various techniques have been proposed to overcome these challenges, including stereo matching, structured light, depth from focus, depth from defocus, depth from motion, and time of flight. The addition of information from a high-resolution 3D view of the surroundings simplifies the distance calculation. This paper describes a novel distance estimation method that operates with converted point cloud data. The proposed method is a reliable map-based bird’s eye view (BEV) that calculates the distance to the detected objects. Using the help of the Euler-region proposal network (E-RPN) model, a LiDAR-to-image-based method for metric distance estimation with 3D bounding box projections onto the image was proposed. We demonstrate that despite the general difficulty of the BEV representation in understanding features related to the height coordinate, it is possible to extract all parameters characterizing the bounding boxes of the objects, including their height and elevation. Finally, we applied the triangulation method to calculate the accurate distance to the objects and statistically proved that our methodology is one of the best in terms of accuracy and robustness. Full article
(This article belongs to the Special Issue The Intelligent Sensing Technology of Transportation System)
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24 pages, 11771 KiB  
Article
Map Space Modeling Method Reflecting Safety Margin in Coastal Water Based on Electronic Chart for Path Planning
by Da-un Jang and Joo-sung Kim
Sensors 2023, 23(3), 1723; https://doi.org/10.3390/s23031723 - 03 Feb 2023
Viewed by 1510
Abstract
Map space composition is the first step in ship route planning. In this study, a map modeling method for path planning is proposed. This method incorporates the safety margin based on the theory of geographic space existing in coastal waters, maneuvering space according [...] Read more.
Map space composition is the first step in ship route planning. In this study, a map modeling method for path planning is proposed. This method incorporates the safety margin based on the theory of geographic space existing in coastal waters, maneuvering space according to ship characteristics, and the psychological buffer space of a ship navigator. First, the obstacle area was segmented using the binary method—a segmentation method—based on the international standard electronic chart image. Next, the margin space was incorporated through the morphological algorithm for the obstacle area. Finally, to minimize the space lost during the route search, the boundary simplification of the obstacle area was performed through the concave hull method. The experimental results of the proposed method resulted in a map that minimized the area lost due to obstacles. In addition, it was found that the distance and path-finding time were reduced compared to the conventional convex hull method. The study shows that the map modeling method is feasible, and that it can be applied to path planning. Full article
(This article belongs to the Special Issue The Intelligent Sensing Technology of Transportation System)
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12 pages, 1240 KiB  
Article
A Cyber-Physical Framework for Optimal Coordination of Connected and Automated Vehicles on Multi-Lane Freeways
by Yuta Sakaguchi, A. S. M. Bakibillah, Md Abdus Samad Kamal and Kou Yamada
Sensors 2023, 23(2), 611; https://doi.org/10.3390/s23020611 - 05 Jan 2023
Cited by 1 | Viewed by 1328
Abstract
Uncoordinated driving behavior is one of the main reasons for bottlenecks on freeways. This paper presents a novel cyber-physical framework for optimal coordination of connected and automated vehicles (CAVs) on multi-lane freeways. We consider that all vehicles are connected to a cloud-based computing [...] Read more.
Uncoordinated driving behavior is one of the main reasons for bottlenecks on freeways. This paper presents a novel cyber-physical framework for optimal coordination of connected and automated vehicles (CAVs) on multi-lane freeways. We consider that all vehicles are connected to a cloud-based computing framework, where a traffic coordination system optimizes the target trajectories of individual vehicles for smooth and safe lane changing or merging. In the proposed framework, the vehicles are coordinated into groups or platoons, and their trajectories are successively optimized in a receding horizon control (RHC) approach. Optimization of the traffic coordination system aims to provide sufficient gaps when a lane change is necessary while minimizing the speed deviation and acceleration of all vehicles. The coordination information is then provided to individual vehicles equipped with local controllers, and each vehicle decides its control acceleration to follow the target trajectories while ensuring a safe distance. Our proposed method guarantees fast optimization and can be used in real-time. The proposed coordination system was evaluated using microscopic traffic simulations and benchmarked with the traditional driving (human-based) system. The results show significant improvement in fuel economy, average velocity, and travel time for various traffic volumes. Full article
(This article belongs to the Special Issue The Intelligent Sensing Technology of Transportation System)
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21 pages, 8361 KiB  
Article
Differences in Driver Behavior between Manual and Automatic Turning of an Inverted Pendulum Vehicle
by Chihiro Nakagawa, Seiya Yamada, Daichi Hirata and Atsuhiko Shintani
Sensors 2022, 22(24), 9931; https://doi.org/10.3390/s22249931 - 16 Dec 2022
Cited by 1 | Viewed by 1508
Abstract
Personal mobility vehicles (PMVs) are compact and lightweight compared to automobiles; hence, human dynamic behavior affects a vehicle’s postural stability. In this study, the dynamic behaviors of drivers of inverted pendulum vehicles (IPV) under manual and automatic driving were investigated. One particular feature [...] Read more.
Personal mobility vehicles (PMVs) are compact and lightweight compared to automobiles; hence, human dynamic behavior affects a vehicle’s postural stability. In this study, the dynamic behaviors of drivers of inverted pendulum vehicles (IPV) under manual and automatic driving were investigated. One particular feature of applying automatic driving to IPV is constant posture stabilization control. In this study, the drivers’ center of gravity (COG)/center of foot pressure position (COP) and joint moments during turning were investigated experimentally. It was found that the drivers’ COG shifted backward during turning and deceleration. For COP, it was found that drivers maintained balance by moving their inner foot more inward and their outer foot more outward during turning. These results are significant for understanding the steps taken to withstand centrifugal forces during turning. The joint moments of the foot were more significant in automatic turning than in manual turning to prevent falling owing to centrifugal force. These findings can facilitate the development of an automatic control method that shifts the COG of a driver, as in manual turning. Full article
(This article belongs to the Special Issue The Intelligent Sensing Technology of Transportation System)
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20 pages, 8338 KiB  
Article
Urban Road Surface Discrimination by Tire-Road Noise Analysis and Data Clustering
by Carlos Ramos-Romero, César Asensio, Ricardo Moreno and Guillermo de Arcas
Sensors 2022, 22(24), 9686; https://doi.org/10.3390/s22249686 - 10 Dec 2022
Cited by 6 | Viewed by 2023
Abstract
The surface condition of roadways has direct consequences on a wide range of processes related to the transportation technology, quality of road facilities, road safety, and traffic noise emissions. Methods developed for detection of road surface condition are crucial for maintenance and rehabilitation [...] Read more.
The surface condition of roadways has direct consequences on a wide range of processes related to the transportation technology, quality of road facilities, road safety, and traffic noise emissions. Methods developed for detection of road surface condition are crucial for maintenance and rehabilitation plans, also relevant for driving environment detection for autonomous transportation systems and e-mobility solutions. In this paper, the clustering of the tire-road noise emission features is proposed to detect the condition of the wheel tracks regions during naturalistic driving events. This acoustic-based methodology was applied in urban areas under nonstop real-life traffic conditions. Using the proposed method, it was possible to identify at least two groups of surface status on the inspected routes over the wheel-path interaction zone. The detection rate on urban zone reaches 75% for renewed lanes and 72% for distressed lanes. Full article
(This article belongs to the Special Issue The Intelligent Sensing Technology of Transportation System)
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20 pages, 1419 KiB  
Article
Bimodal Extended Kalman Filter-Based Pedestrian Trajectory Prediction
by Chien-Yu Lin, Lih-Jen Kau and Ching-Yao Chan
Sensors 2022, 22(21), 8231; https://doi.org/10.3390/s22218231 - 27 Oct 2022
Cited by 3 | Viewed by 1833
Abstract
We propose a pedestrian trajectory prediction algorithm based on the bimodal extended Kalman filter. With this filter, we are able to make full use of the dual-state nature of the pedestrian movement, i.e., the pedestrian is either moving or remains stationary. We apply [...] Read more.
We propose a pedestrian trajectory prediction algorithm based on the bimodal extended Kalman filter. With this filter, we are able to make full use of the dual-state nature of the pedestrian movement, i.e., the pedestrian is either moving or remains stationary. We apply the dual-mode probability model to describe the state of the pedestrian. Based on this model, we construct the proposed bimodal extended Kalman filter to estimate pedestrian state distribution. The filter obtains the state distribution for each pedestrian in the scene, respectively, and use that state distribution to predict the future trajectories of all the people in the scene. This prediction method estimates the prior probability of each parameter of the model through the dataset and updates the individual posterior probability of the pedestrian state through the bimodal extended Kalman filter. Our model can predict the trajectory of every individual, by taking the social interaction of pedestrians as well as the surrounding physical obstacles into account, with less than fifty model parameters being used, while with the limited parameter, our model could be nearly accurate as other deep learning models and still be comprehensible for model users. Full article
(This article belongs to the Special Issue The Intelligent Sensing Technology of Transportation System)
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19 pages, 2140 KiB  
Article
Fuzzy Ontology-Based System for Driver Behavior Classification
by Susel Fernandez, Takayuki Ito, Luis Cruz-Piris and Ivan Marsa-Maestre
Sensors 2022, 22(20), 7954; https://doi.org/10.3390/s22207954 - 19 Oct 2022
Cited by 3 | Viewed by 1578
Abstract
Intelligent transportation systems encompass a series of technologies and applications that exchange information to improve road traffic and avoid accidents. According to statistics, some studies argue that human mistakes cause most road accidents worldwide. For this reason, it is essential to model driver [...] Read more.
Intelligent transportation systems encompass a series of technologies and applications that exchange information to improve road traffic and avoid accidents. According to statistics, some studies argue that human mistakes cause most road accidents worldwide. For this reason, it is essential to model driver behavior to improve road safety. This paper presents a Fuzzy Rule-Based System for driver classification into different profiles considering their behavior. The system’s knowledge base includes an ontology and a set of driving rules. The ontology models the main entities related to driver behavior and their relationships with the traffic environment. The driving rules help the inference system to make decisions in different situations according to traffic regulations. The classification system has been integrated on an intelligent transportation architecture. Considering the user’s driving style, the driving assistance system sends them recommendations, such as adjusting speed or choosing alternative routes, allowing them to prevent or mitigate negative transportation events, such as road crashes or traffic jams. We carry out a set of experiments in order to test the expressiveness of the ontology along with the effectiveness of the overall classification system in different simulated traffic situations. The results of the experiments show that the ontology is expressive enough to model the knowledge of the proposed traffic scenarios, with an F1 score of 0.9. In addition, the system allows proper classification of the drivers’ behavior, with an F1 score of 0.84, outperforming Random Forest and Naive Bayes classifiers. In the simulation experiments, we observe that most of the drivers who are recommended an alternative route experience an average time gain of 66.4%, showing the utility of the proposal. Full article
(This article belongs to the Special Issue The Intelligent Sensing Technology of Transportation System)
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20 pages, 4372 KiB  
Article
Comparison of Eye and Face Features on Drowsiness Analysis
by I-Hsi Kao and Ching-Yao Chan
Sensors 2022, 22(17), 6529; https://doi.org/10.3390/s22176529 - 30 Aug 2022
Cited by 5 | Viewed by 2728
Abstract
Drowsiness is one of the leading causes of traffic accidents. For those who operate large machinery or motor vehicles, incidents due to lack of sleep can cause property damage and sometimes lead to grave consequences of injuries and fatality. This study aims to [...] Read more.
Drowsiness is one of the leading causes of traffic accidents. For those who operate large machinery or motor vehicles, incidents due to lack of sleep can cause property damage and sometimes lead to grave consequences of injuries and fatality. This study aims to design learning models to recognize drowsiness through human facial features. In addition, this work analyzes the attentions of individual neurons in the learning model to understand how neural networks interpret drowsiness. For this analysis, gradient-weighted class activation mapping (Grad-CAM) is implemented in the neural networks to display the attention of neurons. The eye and face images are processed separately to the model for the training process. The results initially show that better results can be obtained by delivering eye images alone. The effect of Grad-CAM is also more reasonable using eye images alone. Furthermore, this work proposed a feature analysis method, K-nearest neighbors Sigma (KNN-Sigma), to estimate the homogeneous concentration and heterogeneous separation of the extracted features. In the end, we found that the fusion of face and eye signals gave the best results for recognition accuracy and KNN-sigma. The area under the curve (AUC) of using face, eye, and fusion images are 0.814, 0.897, and 0.935, respectively. Full article
(This article belongs to the Special Issue The Intelligent Sensing Technology of Transportation System)
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25 pages, 2437 KiB  
Review
A Comprehensive Survey on Multi-Agent Reinforcement Learning for Connected and Automated Vehicles
by Pamul Yadav, Ashutosh Mishra and Shiho Kim
Sensors 2023, 23(10), 4710; https://doi.org/10.3390/s23104710 - 12 May 2023
Cited by 5 | Viewed by 3443
Abstract
Connected and automated vehicles (CAVs) require multiple tasks in their seamless maneuverings. Some essential tasks that require simultaneous management and actions are motion planning, traffic prediction, traffic intersection management, etc. A few of them are complex in nature. Multi-agent reinforcement learning (MARL) can [...] Read more.
Connected and automated vehicles (CAVs) require multiple tasks in their seamless maneuverings. Some essential tasks that require simultaneous management and actions are motion planning, traffic prediction, traffic intersection management, etc. A few of them are complex in nature. Multi-agent reinforcement learning (MARL) can solve complex problems involving simultaneous controls. Recently, many researchers applied MARL in such applications. However, there is a lack of extensive surveys on the ongoing research to identify the current problems, proposed methods, and future research directions in MARL for CAVs. This paper provides a comprehensive survey on MARL for CAVs. A classification-based paper analysis is performed to identify the current developments and highlight the various existing research directions. Finally, the challenges in current works are discussed, and some potential areas are given for exploration to overcome those challenges. Future readers will benefit from this survey and can apply the ideas and findings in their research to solve complex problems. Full article
(This article belongs to the Special Issue The Intelligent Sensing Technology of Transportation System)
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30 pages, 2444 KiB  
Review
A Survey on Ground Segmentation Methods for Automotive LiDAR Sensors
by Tiago Gomes, Diogo Matias, André Campos, Luís Cunha and Ricardo Roriz
Sensors 2023, 23(2), 601; https://doi.org/10.3390/s23020601 - 05 Jan 2023
Cited by 11 | Viewed by 7963
Abstract
In the near future, autonomous vehicles with full self-driving features will populate our public roads. However, fully autonomous cars will require robust perception systems to safely navigate the environment, which includes cameras, RADAR devices, and Light Detection and Ranging (LiDAR) sensors. LiDAR is [...] Read more.
In the near future, autonomous vehicles with full self-driving features will populate our public roads. However, fully autonomous cars will require robust perception systems to safely navigate the environment, which includes cameras, RADAR devices, and Light Detection and Ranging (LiDAR) sensors. LiDAR is currently a key sensor for the future of autonomous driving since it can read the vehicle’s vicinity and provide a real-time 3D visualization of the surroundings through a point cloud representation. These features can assist the autonomous vehicle in several tasks, such as object identification and obstacle avoidance, accurate speed and distance measurements, road navigation, and more. However, it is crucial to detect the ground plane and road limits to safely navigate the environment, which requires extracting information from the point cloud to accurately detect common road boundaries. This article presents a survey of existing methods used to detect and extract ground points from LiDAR point clouds. It summarizes the already extensive literature and proposes a comprehensive taxonomy to help understand the current ground segmentation methods that can be used in automotive LiDAR sensors. Full article
(This article belongs to the Special Issue The Intelligent Sensing Technology of Transportation System)
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