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17 pages, 1577 KiB

Open AccessArticle

Intelligent Traffic Control Decision-Making Based on Type-2 Fuzzy and Reinforcement Learning

by Yunrui Bi, Qinglin Ding, Yijun Du, Di Liu and Shuaihang Ren

Electronics 2024, 13(19), 3894; https://doi.org/10.3390/electronics13193894 - 1 Oct 2024

Viewed by 586

Intelligent traffic control decision-making has long been a crucial issue for improving the efficiency and safety of the intelligent transportation system. The deficiencies of the Type-1 fuzzy traffic control system in dealing with uncertainty have led to a reduced ability to address traffic [...] Read more.

Intelligent traffic control decision-making has long been a crucial issue for improving the efficiency and safety of the intelligent transportation system. The deficiencies of the Type-1 fuzzy traffic control system in dealing with uncertainty have led to a reduced ability to address traffic congestion. Therefore, this paper proposes a Type-2 fuzzy controller for a single intersection. Based on real-time traffic flow information, the green timing of each phase is dynamically determined to achieve the minimum average vehicle delay. Additionally, in traffic light control, various factors (such as vehicle delay and queue length) need to be balanced to define the appropriate reward. Improper reward design may fail to guide the Deep Q-Network algorithm to learn the optimal strategy. To address these issues, this paper proposes a deep reinforcement learning traffic control strategy combined with Type-2 fuzzy control. The output action of the Type-2 fuzzy control system replaces the action of selecting the maximum output Q-value of the target network in the DQN algorithm, reducing the error caused by the use of the max operation of the target network. This approach improves the online learning rate of the agent and increases the reward value of the signal control action. The simulation results using the Simulation of Urban MObility platform show that the traffic signal optimization control proposed in this paper has achieved significant improvement in traffic flow optimization and congestion alleviation, which can effectively improve the traffic efficiency in front of the signal light and improve the overall operation level of traffic flow. Full article

(This article belongs to the Special Issue Smart Vehicles and Smart Transportation Research Trends)

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20 pages, 1279 KiB

Open AccessArticle

AUTOSAR-Compatible Level-4 Virtual ECU for the Verification of the Target Binary for Cloud-Native Development

by Hyeongrae Kim, Junho Kwak and Jeonghun Cho

Electronics 2024, 13(18), 3704; https://doi.org/10.3390/electronics13183704 - 18 Sep 2024

Viewed by 516

Abstract

The rapid evolution of automotive software necessitates efficient and accurate development and verification processes. This study proposes a virtual electronic control unit (vECU) that allows for precise software testing without the need for hardware, thereby reducing developmental costs and enabling cloud-native development. The [...] Read more.

The rapid evolution of automotive software necessitates efficient and accurate development and verification processes. This study proposes a virtual electronic control unit (vECU) that allows for precise software testing without the need for hardware, thereby reducing developmental costs and enabling cloud-native development. The software was configured and built on a Hyundai Autoever AUTomotive Open System Architecture (AUTOSAR) classic platform, Mobilgene, and Renode was used for high-fidelity emulations. Custom peripherals in C# were implemented for the FlexTimer, system clock generator, and analog-to-digital converter to ensure the proper functionality of the vECU. Renode’s GNU debugger server function facilitates detailed software debugging in a cloud environment, further accelerating the developmental cycle. Additionally, automated testing was implemented using a vECU tester to enable the verification of the vECU. Performance evaluations demonstrated that the vECU’s execution order and timing of tasks and runnable entities closely matched those of the actual ECU. The vECU tester also enabled fast and accurate verification. These findings confirm the potential of the AUTOSAR-compatible Level-4 vECU to replace hardware in development processes. Future efforts will focus on extending capabilities to emulate a broader range of hardware components and complex system integration scenarios, supporting more diverse research and development efforts. Full article

(This article belongs to the Special Issue Smart Vehicles and Smart Transportation Research Trends)

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20 pages, 37773 KiB

Open AccessArticle

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 1024

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

Open AccessArticle

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

Cited by 1 | Viewed by 1094

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

Open AccessArticle

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 1264

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

Open AccessArticle

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 809

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

Open AccessArticle

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 - 2 Jan 2024

Cited by 1 | Viewed by 2680

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

Open AccessArticle

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

Cited by 2 | Viewed by 1512

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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