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Intelligent Systems and Control Application in Autonomous Vehicle

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

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 29201

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Special Issue Editors

Prof. Dr. Yu-Chen Lin

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Guest Editor

Department Automatic Control Engineering, Feng Chia University, Wenhwa Rd, Seatwen, Taichung 40724, Taiwan
Interests: image processing; optimal control; robust control; advanced vehicle safety assistant systems
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Valentina E. Balas

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Guest Editor

Department of Automatics and Applied Software, Faculty of Engineering, Aurel Vlaicu University of Arad, Bd Revolutiei 77, 310130 Arad, Romania
Interests: intelligent systems; soft computing; fuzzy control; modeling and simulation; biometrics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The aim of this Special Issue is to present the state-of-the-art results in the area of intelligent systems and advanced control technologies and applications in autonomous vehicle research, particularly covering autonomous and semi-autonomous driving, advanced driver assistant systems (ADAS), artificial intelligence, sensing technology, soft computing, hardware-oriented neural network optimization, control design of dynamical systems, hardware and software implementation, system integration and control applications, and relevant topics. This Special Issue intends to bring together researchers from academia and industries working on emerging topics of intelligent transportation systems.

Prof. Dr. Yu-Chen Lin
Prof. Dr. Valentina E. Balas
Guest Editors

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Keywords

autonomous and semi-autonomous driving
advanced driver assistant systems (ADAS)
artificial intelligence
sensing technology
soft computing
hardware-oriented neural network optimization
control design of dynamical systems
hardware and software implementation
system integration and control applications

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Published Papers (8 papers)

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Research

15 pages, 11340 KiB

Open AccessArticle

Torque Ripple Suppression Method of Switched Reluctance Motor Based on an Improved Torque Distribution Function

by Xiao Ling, Chenhao Zhou, Lianqiao Yang and Jianhua Zhang

Electronics 2022, 11(10), 1552; https://doi.org/10.3390/electronics11101552 - 12 May 2022

Cited by 9 | Viewed by 2011

Abstract

Currently, torque ripple is a crucial factor hindering the application of the switched reluctance motor (SRM). Hence, it is of crucial importance to suppress this undesirable torque ripple. This paper proposes a new torque ripple suppression method of SRM based on the improved torque distribution function. Firstly, the electromagnetic characteristic model of a 8/6-pole four-phase SRM is established, and the cerebellar model articulation controller (CMAC) is used to complete the learning of each model. Then, the improved torque distribution function is planned based on the torque model to give the reference torque of each phase, and the inverse torque model is used to realize the mapping of the reference torque to the reference flux linkage. Finally, the duty of each phase voltage PWM wave modulation is output based on the PID control theory. The proposed accurate model-based planning scheme is implemented on the simulation platform, and the results shows that the maximum torque fluctuation of the output results is reduced to within 3%, and the average error is reduced to within 1%, which is much lower than the error of 15% under the traditional direct torque control method. Full article

(This article belongs to the Special Issue Intelligent Systems and Control Application in Autonomous Vehicle)

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15 pages, 5487 KiB

Open AccessFeature PaperArticle

Intelligent Contact Force Regulation of Pantograph–Catenary Based on Novel Type-Reduction Technology

by Tsung-Chih Lin, Chien-Wen Sun, Yu-Chen Lin and Majid Moradi Zirkohi

Electronics 2022, 11(1), 132; https://doi.org/10.3390/electronics11010132 - 1 Jan 2022

Cited by 8 | Viewed by 4273

Abstract

In this paper, an intelligent control scheme is proposed to suppress vibrations between the pantograph and the catenary by regulating the contact force to a reference value, thereby achieving stable current collection. In order to reduce the computational cost, an interval Type-2 adaptive fuzzy logic control with the Moradi–Zirhohi–Lin type reduction method is applied to deal with model uncertainties and exterior interference. Based on a simplified pantograph–catenary system model, the comparative simulation results show that variation of the contact force can be attenuated and variation disturbances can be repressed simultaneously. Furthermore, in terms of computational burden, the proposed type reduction method outperforms other type reduction methods. Full article

(This article belongs to the Special Issue Intelligent Systems and Control Application in Autonomous Vehicle)

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19 pages, 7202 KiB

Open AccessArticle

Integrated Chassis Control and Control Allocation for All Wheel Drive Electric Cars with Rear Wheel Steering

by Pai-Chen Chien and Chih-Keng Chen

Electronics 2021, 10(22), 2885; https://doi.org/10.3390/electronics10222885 - 22 Nov 2021

Cited by 7 | Viewed by 4089

Abstract

This study investigates a control strategy for torque vectoring (TV) and active rear wheel steering (RWS) using feedforward and feedback control schemes for different circumstances. A comprehensive vehicle and combined slip tire model are used to determine the secondary effect and to generate desired yaw acceleration and side slip angle rate. A model-based feedforward controller is designed to improve handling but not to track an ideal response. A feedback controller based on close loop observation is used to ensure its cornering stability. The fusion of two controllers is used to stabilize a vehicle’s lateral motion. To increase lateral performance, an optimization-based control allocation distributes the wheel torques according to the remaining tire force potential. The simulation results show that a vehicle with the proposed controller exhibits more responsive lateral dynamic behavior and greater maximum lateral acceleration. The cornering safety is also demonstrated using a standard stability test. The driving performance and stability are improved simultaneously by the proposed control strategy and the optimal control allocation scheme. Full article

(This article belongs to the Special Issue Intelligent Systems and Control Application in Autonomous Vehicle)

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9 pages, 3692 KiB

Open AccessCommunication

Rollover Index for Rollover Mitigation Function of Intelligent Commercial Vehicle’s Electronic Stability Control

by Donghoon Shin, Seunghoon Woo and Manbok Park

Electronics 2021, 10(21), 2605; https://doi.org/10.3390/electronics10212605 - 25 Oct 2021

Cited by 17 | Viewed by 4469

Abstract

This paper describes a rollover index for detection or prediction of impending rollover in different driving situations using minimum sensor signals which can be easily obtained from an electronic stability control (ESC) system. The estimated lateral load transfer ratio (LTR) was used as a rollover index with only limited information such as the roll state of the vehicle and some constant parameters. A commercial vehicle has parameter uncertainties because of its load variation. This is likely to affect the driving performance and the estimation of the dynamic state of the vehicle. The main purpose of this paper is to determine the rollover index based on reliable measurements and the parameters of the vehicle. For this purpose, a simplified lateral and vertical vehicle dynamic model was used with some assumptions. The index is appropriate for various situations although the vehicle parameters may change. As part of the index, the road bank angle was investigated in this study, using limited information. Since the vehicle roll dynamics are affected by the road bank angle, the road bank angle should be incorporated, although previous studies ignore this factor in order to simplify the problem. Because it increases or reduces the chances of rollover, consideration of the road bank angle is indispensable in the rollover detection and mitigation function of the ESC system. The performance of the proposed algorithm was investigated via computer simulation studies. The simulation studies showed that the proposed estimation method of the LTR and road bank angle with limited sensor information followed the actual LTR value, reducing the parameter uncertainties. The simulation model was constructed based on a heavy bus (12 tons). Full article

(This article belongs to the Special Issue Intelligent Systems and Control Application in Autonomous Vehicle)

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Figure 1
Planar model of vehicle dynamics. Full article ">Figure 2
Roll model of vehicle dynamics. Full article ">Figure 3
Vehicle vertical model. Full article ">Figure 4
<math display="inline"><semantics> <mrow> <mi>sin</mi> <msub> <mi>ϕ</mi> <mi>v</mi> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>k</mi> <msub> <mi>a</mi> <mrow> <mi>y</mi> <mo>,</mo> <mi>m</mi> <mi>e</mi> <mi>a</mi> <mi>s</mi> </mrow> </msub> </mrow> </semantics></math>: (a) Slowly increasing steering maneuver; (b) Sine with Dwell test; (c) 120 deg step steer; (d) 60 deg step steer. Full article ">Figure 5
Estimated LTR using lateral acceleration and actual LTR: (a) Slowly increasing steering maneuver; (b) Sine with Dwell test; (c) 120 deg step steer; (d) 60 deg step steer. Full article ">Figure 6
Estimated LTR using lateral acceleration and actual LTR in banked road condition. (a,b) refer to the straight road with the bank angle condition, and (c) refers to the S-curve with the road bank angle condition. Full article ">Figure 7
Estimated bank angle. (a,b) refer to the straight road with the bank angle condition, and (c) refers to the S-curve with the road bank angle condition. Full article ">Figure 8
Rollover principle test using equipment of commercial vehicle ESC. Full article ">

22 pages, 3814 KiB

Open AccessArticle

Implementation of an Autonomous Overtaking System Based on Time to Lane Crossing Estimation and Model Predictive Control

by Yu-Chen Lin, Chun-Liang Lin, Shih-Ting Huang and Cheng-Hsuan Kuo

Electronics 2021, 10(18), 2293; https://doi.org/10.3390/electronics10182293 - 17 Sep 2021

Cited by 11 | Viewed by 3950

Abstract

According to statistics, the majority of accidents are attributed to driver negligence, especially when a driver intends to lane change or to overtake another vehicle, which is most likely to cause accidents. In addition, overtaking is one of the most difficult and complex functions for the development of autonomous driving technologies because of the dynamic and complicated task involved in the control strategy and electronic control systems, such as steering, throttle, and brake control. This paper proposes a safe overtaking maneuver procedure for an autonomous vehicle based on time to lane crossing (TLC) estimation and the model predictive control scheme. As overtaking is one of the most complex maneuvers that require both lane keeping and lane changing, a vision-based lane-detection system is used to estimate TLC to make a timely and accurate decision about whether to overtake or remain within the lane. Next, to maintain the minimal safe distance and to choose the best timing to overtake, the successive linearization-based model predictive control is employed to derive an optimal vehicle controller, such as throttle, brake, and steering angle control. Simultaneously, it can make certain that the longitudinal acceleration and steering velocity are maintained under constraints to maintain driving safety. Finally, the proposed system is validated by real-world experiments performed on a prototype electric golf cart and executed in real-time on the automotive embedded hardware with limited computational power. In addition, communication between the sensors and actuators as well as the vehicle control unit (VCU) are based on the controller area network (CAN) bus to realize vehicle control and data collection. The experiments demonstrate the ability of the proposed overtaking decision and control strategy to handle a variety of driving scenarios, including a lane-following function when a relative yaw angle exists and an overtaking function when the approaching vehicle has a different lateral velocity. Full article

(This article belongs to the Special Issue Intelligent Systems and Control Application in Autonomous Vehicle)

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14 pages, 4237 KiB

Open AccessArticle

A New Density-Based Clustering Method Considering Spatial Distribution of Lidar Point Cloud for Object Detection of Autonomous Driving

by Caihong Li, Feng Gao, Xiangyu Han and Bowen Zhang

Electronics 2021, 10(16), 2005; https://doi.org/10.3390/electronics10162005 - 19 Aug 2021

Cited by 9 | Viewed by 3142

Abstract

Lidar is a key sensor of autonomous driving systems, but the spatial distribution of its point cloud is uneven because of its scanning mechanism, which greatly degrades the clustering performance of the traditional density-based spatial clustering of application with noise (DSC). Considering the outline feature of detected objects for intelligent vehicles, a DSC-based adaptive clustering method (DAC) is proposed with the adoption of an elliptic neighborhood, which is designed according to the distribution properties of the point cloud. The parameters of the ellipse are adaptively adjusted with the location of the sample point to deal with the uniformity of points in different ranges. Furthermore, the dependence among different parameters of DAC is analyzed, and the parameters are numerically optimized with the KITTI dataset by considering comprehensive performance. To verify the effectiveness, a comparative experiment was conducted with a vehicle equipped with three IBEO LUX8 lidars on campus, and the results show that compared with DSC using a circular neighborhood, DAC has a better clustering performance and can notably reduce the rate of over-segmentation and under-segmentation. Full article

(This article belongs to the Special Issue Intelligent Systems and Control Application in Autonomous Vehicle)

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17 pages, 1265 KiB

Open AccessArticle

A Pairing Algorithm for Conflict-Free Crossings of Automated Vehicles at Lightless Intersections

by Kimia Chavoshi, Alexander Genser and Anastasios Kouvelas

Electronics 2021, 10(14), 1702; https://doi.org/10.3390/electronics10141702 - 16 Jul 2021

Cited by 4 | Viewed by 2533

Abstract

This paper studies the planning of conflict-free and efficient crossings of antagonistic vehicles’ movements at lightless intersections. A fully automated infrastructure environment is considered, where all vehicles that enter the intersection area are connected and automated (CAVs), i.e., they are equipped with advanced communication and automation technologies. In such a futuristic environment, traffic lights that regulate the right-of-way of different traffic streams are obsolete because of vehicle communication capabilities. The connectivity is utilized to derive vehicle trajectories such that a safe and efficient crossing of lightless intersections is possible. So far, published studies lack the application to complex intersection layouts. To fill this gap, we introduce a control method for CAV pairing allowing for the safe, collision-free crossing of the intersecting area and optimize traffic conditions, i.e., total delays of the system. Simulation results demonstrate the feasibility and applicability of the presented approach, given that all the technical specifications (e.g., communications, velocity actuators) are present. Finally, we conduct a sensitivity analysis for the algorithm’s main parameters, which provides practical insights for the studied experimental scenarios and other existing algorithms in the literature that tackle this problem. Full article

(This article belongs to the Special Issue Intelligent Systems and Control Application in Autonomous Vehicle)

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14 pages, 5531 KiB

Open AccessArticle

Development of a Simple Robotic Driver System (SimRoDS) to Test Fuel Economy of Hybrid Electric and Plug-In Hybrid Electric Vehicles Using Fuzzy-PI Control

by Kyunghun Hwang, Joonghoo Park, Heejung Kim, Tea-Yong Kuc and Sejoon Lim

Electronics 2021, 10(12), 1444; https://doi.org/10.3390/electronics10121444 - 16 Jun 2021

Cited by 7 | Viewed by 2613

Abstract

Over the past decade, new models of hybrid electric vehicles have been released worldwide, and the fuel efficiency of said vehicles has increased by more than 5%. To further improve fuel efficiency, vehicle manufacturers have made efforts to design modules (e.g., engines, motors, transmissions, and batteries) with the highest efficiency possible. To do so, the fuel economy test process, which is conducted primarily using a chassis dynamometer, must produce reliable and accurate results. To accurately analyze the fuel efficiency improvement rate of each module, it is necessary to reduce the test deviation. When the test conducted by human drivers, the test deviation is somewhat large. When the test is conducted by a physical robot driver, the test deviation is improved; however, these robots are expensive and time-consuming to install and take up considerable amount of space in the driver’s seat. To compensate for these shortcomings, we propose a simple, structured robot system that manipulates electrical signals without using mechanical link structures. The controller of this robot driver uses the widely used PI controller. Although PI controllers are simple and perform well, since the dynamics of each test vehicle is different (e.g., acceleration response), the PI controller has a disadvantage in that it cannot determine the optimal PI gain value for each vehicles. In this work, the fuzzy control theorem is applied to overcome this disadvantage. By using fuzzy control to deduce the optimal value of the PI gain, we confirmed that our proposed system is available to conduct tests on vehicles with different dynamics. Full article

(This article belongs to the Special Issue Intelligent Systems and Control Application in Autonomous Vehicle)

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