sensors-logo

Journal Browser

Journal Browser

Special Issue "Artificial Intelligence and Internet of Things in Autonomous Vehicles"

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

Deadline for manuscript submissions: 28 October 2022 | Viewed by 8353
Please contact the Guest Editor or the Section Managing Editor at ([email protected]) for any queries.

Special Issue Editors

Dr. Saber Fallah
E-Mail Website
Guest Editor
University of Surrey, UK
Interests: AI and deep reinforcement learning; advanced control systems; optimization and prediction
Prof. Dr. Dimitrie C. Popescu
E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Old Dominion University, 231 Kaufman Hall, Norfolk, VA, USA
Interests: wireless communications and networking (software defined radio platforms for implementing versatile communication systems, spectrum sensing and spectral shaping for cognitive radio, interference avoidance/suppression, transmitter/receiver optimization to support quality of service); cyber-physical systems (vehicular networking and vehicle-to-infrastructure communications and information exchange for intelligent transportation systems, integration of cyber- and physical components for small size spacecraft, CubeSats, sounding rocket payloads).

Special Issue Information

Dear Colleagues,

The continuous progress of automated vehicle driving functions as well as the global deployment of Cooperative Intelligent Transport Systems (C-ITS) both motivate the research, innovation, and development of new traffic control strategies and ITS services. These activities will truly enable the transition from fully conventional traffic flows towards fully automated vehicle traffic. However, fully automated operation remains a challenge due to the inherent difficulties and safety concerns of real-world driving, especially in an urban setting, which is characterised by uncertainty over the intentions of other road users. Integration of V2X communication has the potential to reduce uncertainty by sharing information between road users and government-owned road infrastructure. However, traditional manual vehicles will dominate road use in the short-to-medium-term future, and unconnected road users like pedestrians and cyclists will always remain a challenge for V2X solutions.

The safety impacts of automated vehicle technology occur primarily due to the fundamental change in the driving principle, i.e., a shift of perception and decision-making from the human to the machine. Further, the connectivity among vehicles and between vehicle infrastructure will influence the performance of the technology as well as its user acceptance.

This Special Issue encourages authors from academia and industry to submit new research results about technological innovations and novel ideas for connected automated vehicles (CAVs), with special interest for artificial intelligence and Internet of Things and their safety impacts. 

Prof. Dr. Saber Fallah
Prof. Dr. Dimitrie Popescu
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. Sensors 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 2400 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

The Special Issue topics include, but are not limited to:

  • V2X communications and networks
  • AI and deep learning
  • Reinforcement learning
  • Control, optimisation, and prediction
  • Vision and environment perception
  • Big data and data analysis
  • AI transparency
  • Vehicle localisation and autonomous navigation
  • Human factors and HMI
  • Security, privacy, and safety systems
  • Sensors and detectors

Published Papers (8 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Article
Customizable FPGA-Based Hardware Accelerator for Standard Convolution Processes Empowered with Quantization Applied to LiDAR Data
Sensors 2022, 22(6), 2184; https://doi.org/10.3390/s22062184 - 11 Mar 2022
Cited by 1 | Viewed by 713
Abstract
In recent years there has been an increase in the number of research and developments in deep learning solutions for object detection applied to driverless vehicles. This application benefited from the growing trend felt in innovative perception solutions, such as LiDAR sensors. Currently, [...] Read more.
In recent years there has been an increase in the number of research and developments in deep learning solutions for object detection applied to driverless vehicles. This application benefited from the growing trend felt in innovative perception solutions, such as LiDAR sensors. Currently, this is the preferred device to accomplish those tasks in autonomous vehicles. There is a broad variety of research works on models based on point clouds, standing out for being efficient and robust in their intended tasks, but they are also characterized by requiring point cloud processing times greater than the minimum required, given the risky nature of the application. This research work aims to provide a design and implementation of a hardware IP optimized for computing convolutions, rectified linear unit (ReLU), padding, and max pooling. This engine was designed to enable the configuration of features such as varying the size of the feature map, filter size, stride, number of inputs, number of filters, and the number of hardware resources required for a specific convolution. Performance results show that by resorting to parallelism and quantization approach, the proposed solution could reduce the amount of logical FPGA resources by 40 to 50%, enhancing the processing time by 50% while maintaining the deep learning operation accuracy. Full article
(This article belongs to the Special Issue Artificial Intelligence and Internet of Things in Autonomous Vehicles)
Show Figures

Figure 1

Article
Evaluation of 3D Vulnerable Objects’ Detection Using a Multi-Sensors System for Autonomous Vehicles
Sensors 2022, 22(4), 1663; https://doi.org/10.3390/s22041663 - 21 Feb 2022
Viewed by 512
Abstract
One of the primary tasks undertaken by autonomous vehicles (AVs) is object detection, which comes ahead of object tracking, trajectory estimation, and collision avoidance. Vulnerable road objects (e.g., pedestrians, cyclists, etc.) pose a greater challenge to the reliability of object detection operations due [...] Read more.
One of the primary tasks undertaken by autonomous vehicles (AVs) is object detection, which comes ahead of object tracking, trajectory estimation, and collision avoidance. Vulnerable road objects (e.g., pedestrians, cyclists, etc.) pose a greater challenge to the reliability of object detection operations due to their continuously changing behavior. The majority of commercially available AVs, and research into them, depends on employing expensive sensors. However, this hinders the development of further research on the operations of AVs. In this paper, therefore, we focus on the use of a lower-cost single-beam LiDAR in addition to a monocular camera to achieve multiple 3D vulnerable object detection in real driving scenarios, all the while maintaining real-time performance. This research also addresses the problems faced during object detection, such as the complex interaction between objects where occlusion and truncation occur, and the dynamic changes in the perspective and scale of bounding boxes. The video-processing module works upon a deep-learning detector (YOLOv3), while the LiDAR measurements are pre-processed and grouped into clusters. The output of the proposed system is objects classification and localization by having bounding boxes accompanied by a third depth dimension acquired by the LiDAR. Real-time tests show that the system can efficiently detect the 3D location of vulnerable objects in real-time scenarios. Full article
(This article belongs to the Special Issue Artificial Intelligence and Internet of Things in Autonomous Vehicles)
Show Figures

Figure 1

Article
Visualizing Street Pavement Anomalies through Fog Computing V2I Networks and Machine Learning
Sensors 2022, 22(2), 456; https://doi.org/10.3390/s22020456 - 08 Jan 2022
Viewed by 459
Abstract
Analyzing data related to the conditions of city streets and avenues could help to make better decisions about public spending on mobility. Generally, streets and avenues are fixed as soon as they have a citizen report or when a major incident occurs. However, [...] Read more.
Analyzing data related to the conditions of city streets and avenues could help to make better decisions about public spending on mobility. Generally, streets and avenues are fixed as soon as they have a citizen report or when a major incident occurs. However, it is uncommon for cities to have real-time reactive systems that detect the different problems they have to fix on the pavement. This work proposes a solution to detect anomalies in streets through state analysis using sensors within the vehicles that travel daily and connecting them to a fog-computing architecture on a V2I network. The system detects and classifies the main road problems or abnormal conditions in streets and avenues using Machine Learning Algorithms (MLA), comparing roughness against a flat reference. An instrumented vehicle obtained the reference through accelerometry sensors and then sent the data through a mid-range communication system. With these data, the system compared an Artificial Neural Network (supervised MLA) and a K-Nearest Neighbor (Supervised MLA) to select the best option to handle the acquired data. This system makes it desirable to visualize the streets’ quality and map the areas with the most significant anomalies. Full article
(This article belongs to the Special Issue Artificial Intelligence and Internet of Things in Autonomous Vehicles)
Show Figures

Figure 1

Article
SEE-TREND: SEcurE Traffic-Related EveNt Detection in Smart Communities
Sensors 2021, 21(22), 7652; https://doi.org/10.3390/s21227652 - 18 Nov 2021
Cited by 1 | Viewed by 416
Abstract
It has been widely recognized that one of the critical services provided by Smart Cities and Smart Communities is Smart Mobility. This paper lays the theoretical foundations of SEE-TREND, a system for Secure Early Traffic-Related EveNt Detection in Smart Cities and Smart Communities. [...] Read more.
It has been widely recognized that one of the critical services provided by Smart Cities and Smart Communities is Smart Mobility. This paper lays the theoretical foundations of SEE-TREND, a system for Secure Early Traffic-Related EveNt Detection in Smart Cities and Smart Communities. SEE-TREND promotes Smart Mobility by implementing an anonymous, probabilistic collection of traffic-related data from passing vehicles. The collected data are then aggregated and used by its inference engine to build beliefs about the state of the traffic, to detect traffic trends, and to disseminate relevant traffic-related information along the roadway to help the driving public make informed decisions about their travel plans, thereby preventing congestion altogether or mitigating its nefarious effects. Full article
(This article belongs to the Special Issue Artificial Intelligence and Internet of Things in Autonomous Vehicles)
Show Figures

Figure 1

Article
Integration of Context Awareness in Smart Service Provision System Based on Wireless Sensor Networks for Sustainable Cargo Transportation
Sensors 2021, 21(15), 5140; https://doi.org/10.3390/s21155140 - 29 Jul 2021
Viewed by 702
Abstract
Smart service provision systems can assist in the management of cargo transportation. The development of these systems faces a number of issues that relate to the analysis of numerous factors, which are influenced by the properties of such complex and dynamic systems. The [...] Read more.
Smart service provision systems can assist in the management of cargo transportation. The development of these systems faces a number of issues that relate to the analysis of numerous factors, which are influenced by the properties of such complex and dynamic systems. The aim of this research was the development of an adaptable smart service provision system that is able to recognize a wide spectrum of contextual information, which is obtained from different services and heterogeneous devices of wireless sensor networks (WSNs). To ensure that the smart service provision system can assist with the analysis of specific cases of unforeseen and unwanted situations during the cargo transportation process, the system must have additional adaptability. To address the adequate provision of contextual data, we examined the problems of multi-dimensional definitions of contextual data and the choice of appropriate artificial intelligence (AI) methods for recognition of contextual information. The objectives relate to prioritizing potential service provision by ensuring the optimal quality of data supply channels and avoiding the flooding of wireless communication channels. The proposed methodology is based on methods of smart system architecture development that integrate the identification of context-aware data, conceptual structures of data warehouses, and algorithms for the recognition of transportation situations based on AI methods. Experimental research is outlined to illustrate the algorithmic analysis of the prototype system using an appropriate simulation environment. Full article
(This article belongs to the Special Issue Artificial Intelligence and Internet of Things in Autonomous Vehicles)
Show Figures

Figure 1

Article
Autonomous Collision Avoidance Using MPC with LQR-Based Weight Transformation
Sensors 2021, 21(13), 4296; https://doi.org/10.3390/s21134296 - 23 Jun 2021
Cited by 3 | Viewed by 888
Abstract
Model predictive control (MPC) is a multi-objective control technique that can handle system constraints. However, the performance of an MPC controller highly relies on a proper prioritization weight for each objective, which highlights the need for a precise weight tuning technique. In this [...] Read more.
Model predictive control (MPC) is a multi-objective control technique that can handle system constraints. However, the performance of an MPC controller highly relies on a proper prioritization weight for each objective, which highlights the need for a precise weight tuning technique. In this paper, we propose an analytical tuning technique by matching the MPC controller performance with the performance of a linear quadratic regulator (LQR) controller. The proposed methodology derives the transformation of a LQR weighting matrix with a fixed weighting factor using a discrete algebraic Riccati equation (DARE) and designs an MPC controller using the idea of a discrete time linear quadratic tracking problem (LQT) in the presence of constraints. The proposed methodology ensures optimal performance between unconstrained MPC and LQR controllers and provides a sub-optimal solution while the constraints are active during transient operations. The resulting MPC behaves as the discrete time LQR by selecting an appropriate weighting matrix in the MPC control problem and ensures the asymptotic stability of the system. In this paper, the effectiveness of the proposed technique is investigated in the application of a novel vehicle collision avoidance system that is designed in the form of linear inequality constraints within MPC. The simulation results confirm the potency of the proposed MPC control technique in performing a safe, feasible and collision-free path while respecting the inputs, states and collision avoidance constraints. Full article
(This article belongs to the Special Issue Artificial Intelligence and Internet of Things in Autonomous Vehicles)
Show Figures

Figure 1

Article
Multi-Sensor Fault Detection, Identification, Isolation and Health Forecasting for Autonomous Vehicles
Sensors 2021, 21(7), 2547; https://doi.org/10.3390/s21072547 - 05 Apr 2021
Cited by 5 | Viewed by 2235
Abstract
The primary focus of autonomous driving research is to improve driving accuracy and reliability. While great progress has been made, state-of-the-art algorithms still fail at times and some of these failures are due to the faults in sensors. Such failures may have fatal [...] Read more.
The primary focus of autonomous driving research is to improve driving accuracy and reliability. While great progress has been made, state-of-the-art algorithms still fail at times and some of these failures are due to the faults in sensors. Such failures may have fatal consequences. It therefore is important that automated cars foresee problems ahead as early as possible. By using real-world data and artificial injection of different types of sensor faults to the healthy signals, data models can be trained using machine learning techniques. This paper proposes a novel fault detection, isolation, identification and prediction (based on detection) architecture for multi-fault in multi-sensor systems, such as autonomous vehicles.Our detection, identification and isolation platform uses two distinct and efficient deep neural network architectures and obtained very impressive performance. Utilizing the sensor fault detection system’s output, we then introduce our health index measure and use it to train the health index forecasting network. Full article
(This article belongs to the Special Issue Artificial Intelligence and Internet of Things in Autonomous Vehicles)
Show Figures

Figure 1

Article
Weakly Supervised Reinforcement Learning for Autonomous Highway Driving via Virtual Safety Cages
Sensors 2021, 21(6), 2032; https://doi.org/10.3390/s21062032 - 13 Mar 2021
Cited by 2 | Viewed by 1020
Abstract
The use of neural networks and reinforcement learning has become increasingly popular in autonomous vehicle control. However, the opaqueness of the resulting control policies presents a significant barrier to deploying neural network-based control in autonomous vehicles. In this paper, we present a reinforcement [...] Read more.
The use of neural networks and reinforcement learning has become increasingly popular in autonomous vehicle control. However, the opaqueness of the resulting control policies presents a significant barrier to deploying neural network-based control in autonomous vehicles. In this paper, we present a reinforcement learning based approach to autonomous vehicle longitudinal control, where the rule-based safety cages provide enhanced safety for the vehicle as well as weak supervision to the reinforcement learning agent. By guiding the agent to meaningful states and actions, this weak supervision improves the convergence during training and enhances the safety of the final trained policy. This rule-based supervisory controller has the further advantage of being fully interpretable, thereby enabling traditional validation and verification approaches to ensure the safety of the vehicle. We compare models with and without safety cages, as well as models with optimal and constrained model parameters, and show that the weak supervision consistently improves the safety of exploration, speed of convergence, and model performance. Additionally, we show that when the model parameters are constrained or sub-optimal, the safety cages can enable a model to learn a safe driving policy even when the model could not be trained to drive through reinforcement learning alone. Full article
(This article belongs to the Special Issue Artificial Intelligence and Internet of Things in Autonomous Vehicles)
Show Figures

Figure 1

Back to TopTop