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Intelligent Vehicle Control Systems
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Intelligent Vehicle Control Systems

A special issue of World Electric Vehicle Journal (ISSN 2032-6653).

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 20608

Special Issue Editor

Prof. Dr. Chung-Neng Huang

E-Mail Website
Guest Editor
Department of Electrical Engineering, National University of Tainan, Tainan 700, Taiwan
Interests: intelligent vehicular technologies; hybrid controls; customized design and manufacture; mechatronic system optimization; green-energy exploration
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Millions of people suffer injuries in car crashes every year, about 94% of which are caused by careless driving. To solve this problem, adaptive cruise control (ACC) is available to advance driver-assistance systems (DASs) to enhance driving safety and riding comfort through adjusting driving velocity to maintain a safe distance from vehicles ahead. Considering the safety and real-time application of ACC, bionic optimizations are proposed to resolve challenges to enhancing safety and driving comfort. Firstly, according to the dynamics model, the fitness function is defined concerning driving safety, including the distance between intelligent vehicles and obstacles, and distance between intelligent vehicles and targets, and riding comfort. Secondly, the optimal driving parameters that minimize the fitness function can be found using bionic optimization algorithms. Finally, simulation results show that the optimization method and its fitness function can further enhance ACC performance and reliability in real time.

There are numerous recent and novel bionic optimizations being developed with algorithm swarm intelligence, including ant colony, swarm of bees, flock of birds, school of fish, swarm of salmonella bacterium, behavior of cuckoos during forced nest parasitism, glowworm swarm, weed farmland colonization, behavior of frog groups during the food searching process, swarm of flies, behavior of frog groups during the food searching process, flock of bats, etc.

This Special Issue is devoted to the latest developments in bionic optimizations and controls for intelligent vehicles. Prospective authors are invited to submit original contributions that include but are not limited to the following topics of interest:

  • Advanced ACC/DAS systems;
  • Advanced manufacturing techniques, including bionic characteristics;
  • Optimal solutions to driving issues;
  • Integration of hybrid strategies for driving safety;
  • Integration of bionic optimization and machine learning technologies;
  • Fitness functions designed for system optimization;
  • Bionic control/optimization for intelligent vehicles.

Prof. Dr. Chung-Neng Huang
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. World Electric Vehicle Journal is an international peer-reviewed open access monthly 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 1400 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

  • bionic optimization
  • adaptive cruise control (ACC)
  • advanced driver-assistance system (DAS)
  • driving safety
  • riding comfort

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Published Papers (5 papers)

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Research

20 pages, 15568 KiB  
Article
Design of Unsignalized Roundabouts Driving Policy of Autonomous Vehicles Using Deep Reinforcement Learning
by Zengrong Wang, Xujin Liu and Zhifei Wu
World Electr. Veh. J. 2023, 14(2), 52; https://doi.org/10.3390/wevj14020052 - 13 Feb 2023
Cited by 8 | Viewed by 2393
Abstract
Driving at an unsignalized roundabout is a complex traffic scenario that requires both traffic safety and efficiency. At the unsignalized roundabout, the driving policy does not simply maintain a safe distance for all vehicles. Instead, it pays more attention to vehicles that potentially [...] Read more.
Driving at an unsignalized roundabout is a complex traffic scenario that requires both traffic safety and efficiency. At the unsignalized roundabout, the driving policy does not simply maintain a safe distance for all vehicles. Instead, it pays more attention to vehicles that potentially have conflicts with the ego-vehicle, while guessing the intentions of other obstacle vehicles. In this paper, a driving policy based on the Soft actor-critic (SAC) algorithm combined with interval prediction and self-attention mechanism is proposed to achieve safe driving of ego-vehicle at unsignalized roundabouts. The objective of this work is to simulate a roundabout scenario and train the proposed algorithm in a low-dimensional environment, and then test and validate the policy in the CARLA simulator to ensure safety while reducing costs. By using a self-attention network and interval prediction algorithms to enable ego-vehicle to focus on more temporal and spatial features, the risk of driving into and out of the roundabout is predicted, and safe and effective driving decisions are made. Simulation results show that our proposed driving policy can provide collision risk avoidance and improve vehicle driving safety, resulting in a 15% reduction in collisions. Finally, the trained model is transferred to the complete vehicle system of CARLA to validate the possibility of real-world deployment of the policy model. Full article
(This article belongs to the Special Issue Intelligent Vehicle Control Systems)
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Graphical abstract

13 pages, 3231 KiB  
Article
Design of Obstacle Avoidance for Autonomous Vehicle Using Deep Q-Network and CARLA Simulator
by Wasinee Terapaptommakol, Danai Phaoharuhansa, Pramote Koowattanasuchat and Jartuwat Rajruangrabin
World Electr. Veh. J. 2022, 13(12), 239; https://doi.org/10.3390/wevj13120239 - 12 Dec 2022
Cited by 15 | Viewed by 4547
Abstract
In this paper, we propose a deep Q-network (DQN) method to develop an autonomous vehicle control system to achieve trajectory design and collision avoidance with regard to obstacles on the road in a virtual environment. The intention of this work is to simulate [...] Read more.
In this paper, we propose a deep Q-network (DQN) method to develop an autonomous vehicle control system to achieve trajectory design and collision avoidance with regard to obstacles on the road in a virtual environment. The intention of this work is to simulate a case scenario and train the DQN algorithm in a virtual environment before testing it in a real scenario in order to ensure safety while reducing costs. The CARLA simulator is used to emulate the motion of the autonomous vehicle in a virtual environment, including an obstacle vehicle parked on the road while the autonomous vehicle drives on the road. The target position, real-time position, velocity, and LiDAR point cloud information are taken as inputs, while action settings such as acceleration, braking, and steering are taken as outputs. The actions are sent to the torque control in the transmission system of the vehicle. A reward function is created using continuous equations designed, especially for this case, in order to imitate human driving behaviors. The results demonstrate that the proposed method can be used to navigate to the destination without collision with the obstacle, through the use of braking and dodging methods. Furthermore, according to the trend of DQN behavior, a better result can be obtained with an increased number of training episodes. This method is a non-global path planning method successfully implemented on a virtual environment platform, which is an advantage of this method over other autonomous vehicle designs, allowing for simulation testing and application with further experiments in future work. Full article
(This article belongs to the Special Issue Intelligent Vehicle Control Systems)
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22 pages, 4233 KiB  
Article
Research on the Trajectory Tracking of Adaptive Second-Order Sliding Mode Control Based on Super-Twisting
by Shaoyi Bei, Hongzhen Hu, Bo Li, Jing Tian, Haoran Tang, Zhenqiang Quan and Yunhai Zhu
World Electr. Veh. J. 2022, 13(8), 141; https://doi.org/10.3390/wevj13080141 - 1 Aug 2022
Cited by 6 | Viewed by 2054
Abstract
This article focuses on the trajectory tracking problem in the actuation control section of autonomous vehicles. Based on a two-degrees-of-freedom dynamics model, this paper combines adaptive preview control with a second-order sliding mode control method to develop a new control method. By designing [...] Read more.
This article focuses on the trajectory tracking problem in the actuation control section of autonomous vehicles. Based on a two-degrees-of-freedom dynamics model, this paper combines adaptive preview control with a second-order sliding mode control method to develop a new control method. By designing an objective function based on lateral deviations, road boundaries, and the corresponding characteristics of the overall vehicle motion, the method adaptively adjusts the preview time to obtain the ideal yaw rate and then uses a second-order sliding mode control algorithm named Super-Twisting to calculate the steering wheel angle. Combining the low-pass filter with this controller can effectively suppress the chattering caused by the switching of the sliding mode plane while proposing a concept of smoothing based on gradient derivative, the smoothness after filtering is one-seventeenth of that before filtering, whereas the phase plane is used to prove its effectiveness and stability, it can be seen from the phase diagrams that all the state points are in the stable region. A joint simulation model of Matlab/Simulink and Carsim was built to verify the control effectiveness of the controller under the double-shift road, and the simulation results show that the designed controller has good control effect and high tracking accuracy. Meanwhile, the simulation model is also used for other simulations, firstly, simulation comparison tests were carried out with the Model Predictive Control algorithm at speeds of 36 and 54 km/h, compared to the MPC controller, the tacking accuracy of the ST controller has improved to 64.42% and 51.02% at 36 and 54 km/h; secondly, taking simulation of the designed controller against a conventional sliding mode controller based on isokinetic law of convergence, compared to the CSMC controller, the tracking accuracy of the ST controller has improved 41.78% at 54 km/h, and the smoothness of the ST controller is one-nineteenth of that of the CSMC controller; thirdly, carrying out simulations on parameter uncertainties, and replacing parameter uncertainty with Gaussian white noise, the maximum tracking error at 36 and 54 km/h did not exceed 0.3 m, and tracking remains good. Small fluctuations in the steering wheel angle do not affect the normal actuation of the actuator. Full article
(This article belongs to the Special Issue Intelligent Vehicle Control Systems)
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14 pages, 4754 KiB  
Article
Research on 3D Point Cloud Data Preprocessing and Clustering Algorithm of Obstacles for Intelligent Vehicle
by Pengwei Wang, Tianqi Gu, Binbin Sun, Di Huang and Ke Sun
World Electr. Veh. J. 2022, 13(7), 130; https://doi.org/10.3390/wevj13070130 - 21 Jul 2022
Cited by 6 | Viewed by 3588
Abstract
Environment perception is the foundation of the intelligent driving system and is a prerequisite for achieving path planning and vehicle control. Among them, obstacle detection is the key to environment perception. In order to solve the problems of difficult-to-distinguish adjacent obstacles and easy-to-split [...] Read more.
Environment perception is the foundation of the intelligent driving system and is a prerequisite for achieving path planning and vehicle control. Among them, obstacle detection is the key to environment perception. In order to solve the problems of difficult-to-distinguish adjacent obstacles and easy-to-split distant obstacles in the traditional obstacle detection algorithm, this study firstly designed a 3D point cloud data filtering algorithm, completed the point cloud data removal of vehicle body points and noise points, and designed the point cloud down-sampling method. Then a ground segmentation method based on the Ray Ground Filter algorithm was designed to solve the under-segmentation problem in ground segmentation, while ensuring real time. Furthermore, an improved DBSCAN (Density-Based Spatial Clustering of Application with Noise) clustering algorithm was proposed, and the L-shaped fitting method was used to complete the 3D bounding box fitting of the point cloud, thus solving the problems that it is difficult to distinguish adjacent obstacles at close distances caused by the fixed parameter thresholds and it is easy for obstacles at long distances to split into multiple obstacles; thus, the real-time performance of the algorithm was improved. Finally, a real vehicle test was conducted, and the test results show that the proposed obstacle detection algorithm in this paper has improved the accuracy by 6.1% and the real-time performance by 13.2% compared with the traditional algorithm. Full article
(This article belongs to the Special Issue Intelligent Vehicle Control Systems)
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21 pages, 6526 KiB  
Article
Steering Control in Electric Power Steering Autonomous Vehicle Using Type-2 Fuzzy Logic Control and PI Control
by Bustanul Arifin, Bhakti Yudho Suprapto, Sri Arttini Dwi Prasetyowati and Zainuddin Nawawi
World Electr. Veh. J. 2022, 13(3), 53; https://doi.org/10.3390/wevj13030053 - 17 Mar 2022
Cited by 16 | Viewed by 6606
Abstract
The steering system in autonomous vehicles is an essential issue that must be addressed. Appropriate control will result in a smooth and risk-free steering system. Compared to other types of controls, type-2 fuzzy logic control has the advantage of dealing with uncertain inputs, [...] Read more.
The steering system in autonomous vehicles is an essential issue that must be addressed. Appropriate control will result in a smooth and risk-free steering system. Compared to other types of controls, type-2 fuzzy logic control has the advantage of dealing with uncertain inputs, which are common in autonomous vehicles. This paper proposes a novel method for the steering control of autonomous vehicles based on type-2 fuzzy logic control combined with PI control. The primary control, type-2 fuzzy logic control, has three inputs—distance, navigation, and speed. The fuzzy system’s output is the steering angle value. This was used as input for the secondary control, PI control. This control is in charge of adjusting the motor’s position as a manifestation of the steering angle. The study results applied to the EPS system of autonomous vehicles revealed that type-2 fuzzy logic control and PI control produced better and smoother control than type-1 fuzzy logic control and PI. The slightest disturbance in the type-1 fuzzy logic control showed a significant change in steering, while this did not occur in the type-2 fuzzy logic control. The results indicate that type-2 fuzzy logic control and PI control could be used for autonomous vehicles by maintaining the comfort and safety of the users. Full article
(This article belongs to the Special Issue Intelligent Vehicle Control Systems)
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