Special Issue "New Advances of Intelligent Vehicles"

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

Deadline for manuscript submissions: closed (30 September 2020) | Viewed by 12680

Special Issue Editors

Prof. Shiho Kim
E-Mail Website
Guest Editor
School of Integrated Technology, College of Engineering, Yonsei University, 85 Songdo-kwahak-ro, Yeonsu-gu, Incheon 406-840, Korea
Interests: development of software and hardware technologies for autonomous vehicles; UAV; AI and reinforcement learning for intelligent transportation systems
Special Issues, Collections and Topics in MDPI journals
Prof. Seongjin Yim
E-Mail Website
Guest Editor
Seoul National University of Science and Technology, 232 Gongneung-ro, Nowon-gu, Seoul 139-743, Korea
Interests: vehicle dynamics and control; state and parameter estimation; steer-by-wire; integrated chassis control with V2X communication
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are now facing the most exciting and important innovations in history regarding both automotive technology and ICT, with the development of intelligent vehicles commonly referred to as autonomous or self-driving vehicles. Thanks to technological advances in hardware/software technologies, the promise of unmanned self-driving vehicles for safer fully automated driving systems is closer to becoming a reality. ICT has the capability to provide systems for Connected, Cooperative, and Autonomous Driving. In this context, we need to handle the massive amount of data generated by connected intelligent vehicles. Emerging systems present several issues, such as those related to the E/E architecture of a vehicle, ensuring and validating safety, testing, passenger comfort, human factors, and cyber security aspects of intelligent vehicles. Therefore, great efforts are required from the research community to solve these problems.

In this Special Issue, we are particularly interested in describing, defining, and quantifying the potential problems of intelligent vehicles and in looking at solutions, prototypes, and demonstrators which address the different aspects of the intelligent vehicles and their applications;

The topics of interest include, but are not limited to:

  • E/E architectures of Intelligent Vehicles
  • Vehicle dynamics and control of Intelligent Vehicles
  • Vehicle hardware/software systems for Connected, Cooperative, and Autonomous Driving
  • Machine Learning approaches for Intelligent Vehicles
  • Artificial intelligence for Intelligent Vehicles
  • Ensuring and Validating Safety for Intelligent Vehicles
  • Cyber security in intelligent vehicles
  • Vehicle Cloud
  • Data-Driven Intelligent Vehicle Applications
  • Neural Network models of Vehicles
  • On-board diagnostics of Intelligent Vehicles
  • Testing of Autonomous Cars
  • Passenger Comfort of Autonomous driving
  • Human Factors
  • OEDR (object and event detection and response) and/or all-back system for highly (fully) automated vehicles

Prof. Shiho Kim
Prof. Seongjin Yim
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. 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 2000 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

  • E/E architectures of Intelligent Vehicles
  • Vehicle dynamics and control of Intelligent Vehicles
  • Vehicle hardware/software systems for Connected, Cooperative and Autonomous Driving
  • Machine Learning approaches for Intelligent Vehicles
  • Artificial intelligence for Intelligent Vehicles
  • Ensuring and Validating Safety for Intelligent Vehicles
  • Cyber security in intelligent vehicles
  • Vehicle Cloud
  • Data-Driven Intelligent Vehicle Applications
  • Neural Network models of Vehicle
  • On-board diagnostics of Intelligent Vehicles
  • Testing of Autonomous Cars
  • Passenger Comfort of Autonomous driving
  • Human Factors
  • OEDR (object and event detection and response) and/or fall-back system for highly (fully) automated vehicles

Published Papers (5 papers)

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Research

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Article
Vehicle Stability Control with Four-Wheel Independent Braking, Drive and Steering on In-Wheel Motor-Driven Electric Vehicles
Electronics 2020, 9(11), 1934; https://doi.org/10.3390/electronics9111934 - 17 Nov 2020
Cited by 9 | Viewed by 1505
Abstract
This paper presents a method to design a vehicle stability controller with four-wheel independent braking (4WIB), drive (4WID) and steering (4WIS) for electric vehicles (EVs) adopting in-wheel motor (IWM) system. To improve lateral stability and maneuverability of vehicles, a direct yaw moment control [...] Read more.
This paper presents a method to design a vehicle stability controller with four-wheel independent braking (4WIB), drive (4WID) and steering (4WIS) for electric vehicles (EVs) adopting in-wheel motor (IWM) system. To improve lateral stability and maneuverability of vehicles, a direct yaw moment control strategy is adopted. A control allocation method is adopted to distribute control yaw moment into tire forces, generated by 4WIB, 4WID and 4WIS. A set of variable weights in the control allocation method is introduced for the application of several actuator combinations. Simulation on a driving simulation tool, CarSim®, shows that the proposed vehicle stability controller is capable of enhancing lateral stability and maneuverability. From the simulation, the effects of actuator combinations on control performance are analyzed. Full article
(This article belongs to the Special Issue New Advances of Intelligent Vehicles)
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Article
Comparison among Active Front, Front Independent, 4-Wheel and 4-Wheel Independent Steering Systems for Vehicle Stability Control
Electronics 2020, 9(5), 798; https://doi.org/10.3390/electronics9050798 - 12 May 2020
Cited by 9 | Viewed by 1433
Abstract
For the last four decades, several steering systems for vehicles such as active front steering (AFS), front wheel independent steering (FWIS), 4-wheel steering (4WS) and 4-wheel independent steering (4WIS) have been proposed and developed. However, there have been few approaches for comparison among [...] Read more.
For the last four decades, several steering systems for vehicles such as active front steering (AFS), front wheel independent steering (FWIS), 4-wheel steering (4WS) and 4-wheel independent steering (4WIS) have been proposed and developed. However, there have been few approaches for comparison among these steering systems with respect to yaw rate tracking or path tracking performance. This paper presents comparison among AFS, FWIS, 4WS and 4WIS in terms of vehicle stability control. In view of vehicle stability control, these systems are used as an actuator for generation of yaw moment. Direct yaw moment control is adopted to calculate a control yaw moment. Distribution from the control yaw moment into tire forces is achieved by a control allocation method. From the calculated tire forces, the steering angles of FWIS, 4WS and 4WIS are determined with a lateral tire force model. To check the performance of these actuators, simulation is conducted on vehicle simulation packages, CarSim. From the simulation, the advantages of FWIS and 4WIS are revealed over AFS and 4WS. Full article
(This article belongs to the Special Issue New Advances of Intelligent Vehicles)
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Article
An Inverse Vehicle Model for a Neural-Network-Based Integrated Lateral and Longitudinal Automatic Parking Controller
Electronics 2019, 8(12), 1452; https://doi.org/10.3390/electronics8121452 - 01 Dec 2019
Cited by 4 | Viewed by 1406
Abstract
The majority of currently used automatic parking systems exploit the planning-and-tracking approach that involves planning the reference trajectory first and then tracking the desired reference trajectory. However, the response delay of longitudinal velocity prevents the parking controller from tracing the desired trajectory because [...] Read more.
The majority of currently used automatic parking systems exploit the planning-and-tracking approach that involves planning the reference trajectory first and then tracking the desired reference trajectory. However, the response delay of longitudinal velocity prevents the parking controller from tracing the desired trajectory because the vehicle’s velocity and other state parameters are not synchronized, while the controller maneuvers the vehicle according to the planned desired velocity and steering profiles. We propose an inverse vehicle model to provide a neural-network-based integrated lateral and longitudinal automatic parking controller. We approximated the relationship of the planned velocity to the vehicle’s velocity using a second-order difference equation that involves the response characteristic of the vehicle’s longitudinal delay. The adjusted desired velocity to track the origin-planned velocity is calculated using the inverse vehicle model. Furthermore, we proposed an integrated longitudinal and lateral parking controller using an artificial neural network (ANN) model trained on a dataset applying the inverse vehicle model. By learning the control laws between the vehicle’s states and the corresponding actions, the proposed ANN-based controller could yield a steering angle and the adjusted desired velocity to complete automatic parking in a confined space. Full article
(This article belongs to the Special Issue New Advances of Intelligent Vehicles)
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Article
Toward a Comfortable Driving Experience for a Self-Driving Shuttle Bus
Electronics 2019, 8(9), 943; https://doi.org/10.3390/electronics8090943 - 27 Aug 2019
Cited by 45 | Viewed by 6823
Abstract
The convergence of mechanical, electrical, and advanced ICT technologies, driven by artificial intelligence and 5G vehicle-to-everything (5G-V2X) connectivity, will help to develop high-performance autonomous driving vehicles and services that are usable and convenient for self-driving passengers. Despite widespread research on self-driving, user acceptance [...] Read more.
The convergence of mechanical, electrical, and advanced ICT technologies, driven by artificial intelligence and 5G vehicle-to-everything (5G-V2X) connectivity, will help to develop high-performance autonomous driving vehicles and services that are usable and convenient for self-driving passengers. Despite widespread research on self-driving, user acceptance remains an essential part of successful market penetration; this forms the motivation behind studies on human factors associated with autonomous shuttle services. We address this by providing a comfortable driving experience while not compromising safety. We focus on the accelerations and jerks of vehicles to reduce the risk of motion sickness and to improve the driving experience for passengers. Furthermore, this study proposes a time-optimal velocity planning method for guaranteeing comfort criteria when an explicit reference path is given. The overall controller and planning method were verified using real-time, software-in-the-loop (SIL) environments for a real-time vehicle dynamics simulation; the performance was then compared with a typical planning approach. The proposed optimized planning shows a relatively better performance and enables a comfortable passenger experience in a self-driving shuttle bus according to the recommended criteria. Full article
(This article belongs to the Special Issue New Advances of Intelligent Vehicles)
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Letter
Development of Fail-Safe Algorithm for Exteroceptive Sensors of Autonomous Vehicles
Electronics 2020, 9(11), 1774; https://doi.org/10.3390/electronics9111774 - 26 Oct 2020
Cited by 1 | Viewed by 937
Abstract
This paper presents a fail-safe algorithm for the exteroceptive sensors of autonomous vehicles. The proposed fault diagnosis mechanism consists of three parts: (1) fault detecting by a duplication-comparison method, (2) fault isolating by possible area prediction and (3) in-vehicle sensor fail-safes. The main [...] Read more.
This paper presents a fail-safe algorithm for the exteroceptive sensors of autonomous vehicles. The proposed fault diagnosis mechanism consists of three parts: (1) fault detecting by a duplication-comparison method, (2) fault isolating by possible area prediction and (3) in-vehicle sensor fail-safes. The main ideas are the usage of redundant external sensor pairs, which estimate the same target, whose results are compared to detect the fault by a modified duplication-comparison method and the novel fault isolation method using target predictions. By comparing the estimations of surrounding vehicles and the raw measurement data, the location of faults can be determined whether they are from sensors themselves or a software error. In addition, faults were isolated by defining possible areas where existing sensor coordinates could be measured, which can be predicted by using previous estimation results. The performance of the algorithm has been tested by using offline vehicle data analysis via MATLAB. Various fault injection experiments were conducted and the performance of the suggested algorithm was evaluated based on the time interval between injection and the detection of faults. Full article
(This article belongs to the Special Issue New Advances of Intelligent Vehicles)
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