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Electronics
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Smart Vehicles and Smart Transportation Research Trends
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Smart Vehicles and Smart Transportation Research Trends

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electrical and Autonomous Vehicles".

Deadline for manuscript submissions: 15 November 2024 | Viewed by 4971

Special Issue Editor

Dr. Mohamed Karray

E-Mail Website
Guest Editor
ESME, Ivry sur Seine 94200, France
Interests: embedded AI; smart car; autonomous car

Special Issue Information

Dear Colleagues,

This Special Issue will focus on addressing new trends and challenges related to smart mobility in general. It aims to address new applied research related to smart transportation and smart cities-related research topics. Additionally, it will focus on applications, new techniques, new algorithms and improvements in aspects related to smart mobilities, the IoT and ITS, among others. AI and generative AI is today becoming a necessity in many fields; therefore, this Special Issue is also giving attention to the integration of AI and generative AI in smart mobility research.

Being responsible and sensitive to SDGs, sustainability and smart transportation is also an area of focus in the addressed publications. Submission related (but not limited to) the below sub topics are welcome:

  • Autonomous vehicles;
  • Connected vehicles;
  • Electric vehicles;
  • Intelligent transportation systems;
  • AI and generative AI usage for smart transportation;
  • ADAS / ADS;
  • Mobility as a service;
  • Smart cities;
  • IoT.

Dr. Mohamed Karray
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. Electronics 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

  • AI
  • smart mobility
  • autonomous vehicles
  • connected vehicles
  • intelligent transportation system
  • ADAS/ADS
  • mobility as a service
  • smart cities
  • IoT

Published Papers (6 papers)

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Research

20 pages, 37773 KiB  
Article
Deep Reinforcement Learning Car-Following Control Based on Multivehicle Motion Prediction
by Tao Wang, Dayi Qu, Kedong Wang and Shouchen Dai
Electronics 2024, 13(6), 1133; https://doi.org/10.3390/electronics13061133 - 20 Mar 2024
Viewed by 497
Abstract
Reinforcement learning (RL)–based car-following (CF) control strategies have attracted significant attention in academia, emerging as a prominent research topic in recent years. Most of these control strategies focus solely on the motion status of the immediately preceding vehicle. However, with the development of [...] Read more.
Reinforcement learning (RL)–based car-following (CF) control strategies have attracted significant attention in academia, emerging as a prominent research topic in recent years. Most of these control strategies focus solely on the motion status of the immediately preceding vehicle. However, with the development of vehicle-to-vehicle (V2V) communication technologies, intelligent vehicles such as connected autonomous vehicles (CAVs) can gather information about surrounding vehicles. Therefore, this study proposes an RL-based CF control strategy that takes multivehicle scenarios into account. First, the trajectories of two preceding vehicles and one following vehicle relative to the subject vehicle (SV) are extracted from a highD dataset to construct the environment. Then the twin-delayed deep deterministic policy gradient (TD3) algorithm is implemented as the control strategy for the agent. Furthermore, a sequence-to-sequence (seq2seq) module is developed to predict the uncertain motion statuses of surrounding vehicles. Once integrated into the RL framework, this module enables the agent to account for dynamic changes in the traffic environment, enhancing its robustness. Finally, the performance of the CF control strategy is validated both in the highD dataset and in two traffic perturbation scenarios. In the highD dataset, the TD3-based prediction CF control strategy outperforms standard RL algorithms in terms of convergence speed and rewards. Its performance also surpasses that of human drivers in safety, efficiency, comfort, and fuel consumption. In traffic perturbation scenarios, the performance of the proposed CF control strategy is compared with the model predictive controller (MPC). The results show that the TD3-based prediction CF control strategy effectively mitigates undesired traffic waves caused by the perturbations from the head vehicle. Simultaneously, it maintains the desired traffic state and consistently ensures a stable and efficient traffic flow. Full article
(This article belongs to the Special Issue Smart Vehicles and Smart Transportation Research Trends)
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20 pages, 15778 KiB  
Article
Double-Layer Coils Design for 11 kW Wireless Power Transfer
by Dejana Herceg, Vladimir Rajs, Živadin Despotović, Bane Popadić, Mirjana Šiljegović, Zoltan Kiraly, Zoltan Vizvari, Krisztian Wizner, Imre Felde, Peter Odry and Vladimir Tadic
Electronics 2024, 13(3), 547; https://doi.org/10.3390/electronics13030547 - 29 Jan 2024
Viewed by 498
Abstract
The design of a wireless power transfer system with double rectangular coils for 11 kW power transfer is considered. System modeling and numerical calculation of the system parameters are described. Coils are made from available Litz wire, which has a smaller than necessary [...] Read more.
The design of a wireless power transfer system with double rectangular coils for 11 kW power transfer is considered. System modeling and numerical calculation of the system parameters are described. Coils are made from available Litz wire, which has a smaller than necessary diameter for the required power. Thus, a setup with double layer coils was developed, which resulted in a modified design. Starting from a system consisting of coupled coils, as suggested by the standard for wireless power transfer Level 3 in class Z1, different coil and ferrite shield layouts were tested in numerical simulations, and their parameters were calculated. The prototype was constructed based on the simulated model with the best results and properties. Numerical results were verified by laboratory measurements, and a successful power transfer at 11 kW was achieved. Full article
(This article belongs to the Special Issue Smart Vehicles and Smart Transportation Research Trends)
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20 pages, 6003 KiB  
Article
Autonomous Driving Control for Passing Unsignalized Intersections Using the Semantic Segmentation Technique
by Jichiang Tsai, Yuan-Tsun Chang, Zhi-Yuan Chen and Zhehao You
Electronics 2024, 13(3), 484; https://doi.org/10.3390/electronics13030484 - 24 Jan 2024
Cited by 1 | Viewed by 748
Abstract
Autonomous driving in urban areas is challenging because it requires understanding vehicle movements, traffic rules, map topologies and unknown environments in the highly complex driving environment, and thus typical urban traffic scenarios include various potentially hazardous situations. Therefore, training self-driving cars by using [...] Read more.
Autonomous driving in urban areas is challenging because it requires understanding vehicle movements, traffic rules, map topologies and unknown environments in the highly complex driving environment, and thus typical urban traffic scenarios include various potentially hazardous situations. Therefore, training self-driving cars by using traditional deep learning models not only requires the labelling of numerous datasets but also takes a large amount of time. Because of this, it is important to find better alternatives for effectively training self-driving cars to handle vehicle behavior and complex road shapes in dynamic environments and to follow line guidance information. In this paper, we propose a method for training a self-driving car in simulated urban traffic scenarios to be able to judge the road conditions on its own for crossing an unsignalized intersection. In order to identify the behavior of traffic flow at the intersection, we use the CARLA (CAR Learning to Act) self-driving car simulator to build the intersection environment and simulate the process of traffic operation. Moreover, we attempt to use the DDPG (Deep Deterministic Policy Gradient) and RDPG (Recurrent Deterministic Policy Gradient) learning algorithms of the DRL (Deep Reinforcement Learning) technology to train models based on the CNN (Convolutional Neural Network) architecture. Specifically, the observation image of the semantic segmentation camera installed on the self-driving car and the vehicle speed are used as the model input. Moreover, we design an appropriate reward mechanism for performing training according to the current situation of the self-driving car judged from sensing data of the obstacle sensor, collision sensor and lane invasion detector. Doing so can improve the convergence speed of the model to achieve the purpose of the self-driving car autonomously judging the driving paths so as to accomplish accurate and stable autonomous driving control. Full article
(This article belongs to the Special Issue Smart Vehicles and Smart Transportation Research Trends)
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13 pages, 5535 KiB  
Article
Reducing Distracted Driving and Improving Consistency with Brine Truck Automation
by Justin Anthony Mahlberg, Jijo K. Mathew, Jairaj Desai and Darcy M. Bullock
Electronics 2024, 13(2), 327; https://doi.org/10.3390/electronics13020327 - 12 Jan 2024
Viewed by 520
Abstract
Salt brine is routinely used by transportation agencies to pre-treat critical infrastructure such as bridges, ramps, and underpasses in advance of winter storms. This requires an operator turning on and off brine controls while driving at highway speeds, introducing driver distraction and consistency [...] Read more.
Salt brine is routinely used by transportation agencies to pre-treat critical infrastructure such as bridges, ramps, and underpasses in advance of winter storms. This requires an operator turning on and off brine controls while driving at highway speeds, introducing driver distraction and consistency challenges. In urban areas, such as Indianapolis, a 5500-gallon tractor trailer with a gross vehicle weight of 80,000 pounds is typically used and the driver may have 1200 on/off activations while covering 318 miles during a pre-treatment shift. This study conducted in collaboration with Indiana Department of Transportation has worked with their truck upfitters to adapt geo-fenced agriculture spraying controls to seven trucks that use the Global Positioning System (GPS) position of the truck to activate the sprayer valves when the trucks enter and exit geo-fenced areas that require pre-treatment. This automated brine system enhances safety, reduces driver workload, and ensures the consistent application of brine in designated areas. Furthermore, as additional environmental constraints and reporting requirements evolve, this system has the capability of reducing application rates in sensitive areas and provides a comprehensive geo-coded application history. The Indiana Department of Transportation has scaled deployment for treating interstates and major arterials with brine. This deployment on 5500-gallon tankers, used on I-64/65/69/70/74, and 465, eliminates over 10,000 driver distraction events during every statewide pre-treatment event. Full article
(This article belongs to the Special Issue Smart Vehicles and Smart Transportation Research Trends)
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20 pages, 3411 KiB  
Article
Optimization Control of Adaptive Traffic Signal with Deep Reinforcement Learning
by Kerang Cao, Liwei Wang, Shuo Zhang, Lini Duan, Guimin Jiang, Stefano Sfarra, Hai Zhang and Hoekyung Jung
Electronics 2024, 13(1), 198; https://doi.org/10.3390/electronics13010198 - 02 Jan 2024
Viewed by 1206
Abstract
The optimization and control of traffic signals is very important for logistics transportation. It not only improves the operational efficiency and safety of road traffic, but also conforms to the direction of the intelligent, green, and sustainable development of modern cities. In order [...] Read more.
The optimization and control of traffic signals is very important for logistics transportation. It not only improves the operational efficiency and safety of road traffic, but also conforms to the direction of the intelligent, green, and sustainable development of modern cities. In order to improve the optimization effect of traffic signal control, this paper proposes a traffic signal optimization method based on deep reinforcement learning and Simulation of Urban Mobility (SUMO) software for urban traffic scenarios. The intersection training scenario was established using SUMO micro traffic simulation software, and the maximum vehicle queue length and vehicle queue time were selected as performance evaluation indicators. In order to be more relevant to the real environment, the experiment uses Weibull distribution to simulate vehicle generation. Since deep reinforcement learning takes into account both perceptual and decision-making capabilities, this study proposes a traffic signal optimization control model based on the deep reinforcement learning Deep Q Network (DQN) algorithm by considering the realism and complexity of traffic intersections, and first uses the DQN algorithm to train the model in a training scenario. After that, the G-DQN (Grouping-DQN) algorithm is proposed to address the problems that the definition of states in existing studies cannot accurately represent the traffic states and the slow convergence of neural networks. Finally, the performance of the G-DQN algorithm model was compared with the original DQN algorithm model and Advantage Actor-Critic (A2C) algorithm model. The experimental results show that the improved algorithm increased the main indicators in all aspects. Full article
(This article belongs to the Special Issue Smart Vehicles and Smart Transportation Research Trends)
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15 pages, 2222 KiB  
Article
Lightweight Cryptography for Connected Vehicles Communication Security on Edge Devices
by Sahbi Boubaker, Faisal S. Alsubaei, Yahia Said and Hossam E. Ahmed
Electronics 2023, 12(19), 4090; https://doi.org/10.3390/electronics12194090 - 29 Sep 2023
Viewed by 1006
Abstract
Recent advances in mobile connection technology have been involved in every aspect of modern life. Even vehicles are becoming more connected, with the ability to communicate without human intervention. The main idea of connected vehicles is to exchange information to avoid a potential [...] Read more.
Recent advances in mobile connection technology have been involved in every aspect of modern life. Even vehicles are becoming more connected, with the ability to communicate without human intervention. The main idea of connected vehicles is to exchange information to avoid a potential collision or to warn the driver about stop signs/lights. To achieve a wide range of connections between vehicles, they must be equipped with connected devices such as Bluetooth, wi-fi, and cellular connectivity. However, communication raises security issues with regard to cybersecurity attacks that attempt to collect confidential information or to take control of the vehicle by forcing unintended braking or steering. In this paper, we proposed a secure vehicle-to-vehicle (V2V) communication approach by deploying a secure communication protocol based on a key management process and a cryptography system to encrypt exchanged data. The proposed key management process was designed to resist many attacks and eliminate connections to the infrastructure for key generation. Since vehicles are equipped with embedded devices with limited computation resources, a lightweight cryptography algorithm was used. The light encryption device (LED) block cipher was used to encrypt exchanged data. The LED has a low implementation area on hardware and low power consumption. It is considered to be a perfect solution for security issues in connected vehicles. The proposed data encryption algorithm was synthesized with VHDL on the Xilinx Zynq-7020 FPGA using the Vivado HLS tool. The encryption algorithm was implemented only on the logic of the device. The achieved results proved that the proposed algorithm is suitable for implementation in vehicles due to its low implementation requirements and low power consumption in addition to its high security level against cyber-attacks. Full article
(This article belongs to the Special Issue Smart Vehicles and Smart Transportation Research Trends)
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