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WEVJ
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Autonomous Driving of EVs
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Autonomous Driving of EVs

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

Deadline for manuscript submissions: closed (31 July 2019) | Viewed by 13179

Special Issue Editors

Prof. Dr. Myoungho Sunwoo

E-Mail Website
Guest Editor
Department of Automotive Engineering, Hanyang University, Seoul 04763, Republic of Korea
Interests: autonomous & connected car; environmental friendly vehicle (ICE, HEV, EV); automotive electronics and control
Special Issues, Collections and Topics in MDPI journals
Prof. Kichun Jo

E-Mail Website
Guest Editor
Konkuk University, Korea
Interests: sensor fusion for autonomous driving, perception with variety of sensors, localization and dynamic map generation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Most automobile companies and many AI companies, including semiconductor companies, are working on the development of autonomous vehicles based on EVs. Especially sharing service companies such as Uber, Lyft, Google and many others are interested in the autonomous operation of EVs.

In the near future, autonomous vehicles with EVs will be popular for not only sharing service companies but also personal mobility, because EVs need less maintenance and are more convenient than internal combustion engine vehicles.

We propose that this Special Issue will cover all areas of autonomous driving subsystems and their components or sensors such as cameras, laser scanners, radars, IMU, GPS, and not only hardware but also software and related algorithms. Artificial intelligence and big data will also be very important technologies for autonomous driving.

Any topic regarding autonomous driving is welcome in this Special Issue.

Prof. Myoungho Sunwoo
Prof. Kichun Jo
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. 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

  • Sensors
    o LiDAR
    o Radaro
    o Camerao
    o GPS and IMU
  • Perception
    o Vision
    o Point cloud processing
    o Tracking
    o Detection
    o Segmentation
    o Classification
  • Localization
    o Localization
    o Mapping
    o SLAM
    o High definition (HD) map
  • Decision
    o Behavior decision
    o Trajectory planning
    o SLAM
    o Driving strategy of EVs
  • Control
    o Autonomous energy management
    o Driving control of EVs
  • AD algorithms
    o Artificial intelligence
    o Deep learning

Published Papers (4 papers)

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Research

21 pages, 9301 KiB  
Article
Obstacle Avoidance of Semi-Trailers Based on Nonlinear Model Predictive Control
by Guoxing Bai, Chen Liang, Yu Meng, Li Liu, Weidong Luo and Qing Gu
World Electr. Veh. J. 2019, 10(4), 72; https://doi.org/10.3390/wevj10040072 - 1 Nov 2019
Cited by 9 | Viewed by 3134
Abstract
Obstacle avoidance is a core part of the autonomous driving of off-road vehicles, such as semi-trailers. Due to the long length of semi-trailers, the traditional obstacle avoidance controller based on the circumcircle model can ensure that there is no collision between the semi-trailer [...] Read more.
Obstacle avoidance is a core part of the autonomous driving of off-road vehicles, such as semi-trailers. Due to the long length of semi-trailers, the traditional obstacle avoidance controller based on the circumcircle model can ensure that there is no collision between the semi-trailer and the obstacle, but it also greatly reduces the passable area. To solve this problem, we propose a new obstacle avoidance model. In this model, the distance between the obstacle and the middle line of semi-trailers is used as the indicator of obstacle avoidance. Based on this model, we design a new obstacle avoidance controller for semi-trailers. The simulation results show that the proposed controller can ensure that no collision occurs between the semi-trailer and the obstacle. The minimum distance between the obstacle center and the semi-trailer body trajectory is greater than the sum of the obstacle radius and the safety margin. Compared with the traditional obstacle avoidance controller based on the circumcircle model, the proposed controller greatly reduces the error between the semi-trailer and the reference path during obstacle avoidance. Full article
(This article belongs to the Special Issue Autonomous Driving of EVs)
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23 pages, 8048 KiB  
Article
Automatic Longitudinal Regenerative Control of EVs Based on a Driver Characteristics-Oriented Deceleration Model
by Gyubin Sim, Seongju Ahn, Inseok Park, Jeamyoung Youn, Seungjae Yoo and Kyunghan Min
World Electr. Veh. J. 2019, 10(4), 58; https://doi.org/10.3390/wevj10040058 - 20 Sep 2019
Cited by 6 | Viewed by 2911
Abstract
To preserve the fun of driving and enhance driving convenience, a smart regenerative braking system (SRS) is developed. The SRS provides automatic regeneration that is appropriate for the driving conditions, but the existing technology has a low level of acceptability and comfort. To [...] Read more.
To preserve the fun of driving and enhance driving convenience, a smart regenerative braking system (SRS) is developed. The SRS provides automatic regeneration that is appropriate for the driving conditions, but the existing technology has a low level of acceptability and comfort. To solve this problem, this paper presents an automatic regenerative control system based on a deceleration model that reflects the driver’s characteristics. The deceleration model is designed as a parametric model that mimics the driver’s behavior. In addition, it consists of parameters that represent the driver’s characteristics. These parameters are updated online by a learning algorithm. The validation results of the vehicle testing show that the vehicle maintained a safe distance from the leading car while simulating a driver’s behavior. Of all the deceleration that occurred during the testing, 92% was conducted by the automatic regeneration system. In addition, the results of the online learning algorithm are different based on the driver’s deceleration pattern. The presented automatic regenerative control system can be safely used in diverse car-following situations. Moreover, the system’s acceptability is improved by updating the driver characteristics. In the future, the algorithm will be extended for use in more diverse deceleration situations by using intelligent transportation system information. Full article
(This article belongs to the Special Issue Autonomous Driving of EVs)
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18 pages, 8644 KiB  
Article
Multi-Level Deceleration Planning Based on Reinforcement Learning Algorithm for Autonomous Regenerative Braking of EV
by Kyunghan Min, Gyubin Sim, Seongju Ahn, Inseok Park, Seungjae Yoo and Jeamyoung Youn
World Electr. Veh. J. 2019, 10(3), 57; https://doi.org/10.3390/wevj10030057 - 16 Sep 2019
Cited by 2 | Viewed by 3568
Abstract
A smart regenerative braking system, which is an advanced driver assistance system of electric vehicles, automatically controls the regeneration torque of the electric motor to brake the vehicle by recognizing the deceleration conditions. Thus, this autonomous braking system can provide driver convenience and [...] Read more.
A smart regenerative braking system, which is an advanced driver assistance system of electric vehicles, automatically controls the regeneration torque of the electric motor to brake the vehicle by recognizing the deceleration conditions. Thus, this autonomous braking system can provide driver convenience and energy efficiency by suppressing the frequent braking of the driver brake pedaling. In order to apply this assistance system, a deceleration planning algorithm should guarantee the safety deceleration under diverse driving situations. Furthermore, the planning algorithm suppresses a sense of heterogeneity by autonomous braking. To ensuring these requirements for deceleration planning, this study proposes a multi-level deceleration planning algorithm which consists of the two representative planning algorithms and one planning management. Two planning algorithms, which are the driver model-based planning and optimization-based planning, generate the deceleration profiles. Then, the planning management determines the optimal planning result among the deceleration profiles. To obtain an optimal result, planning management is updated based on the reinforcement learning algorithm. The proposed algorithm was learned and validated under a simulation environment using the real vehicle experimental data. As a result, the algorithm determines the optimal deceleration vehicle trajectory to autonomous regenerative braking. Full article
(This article belongs to the Special Issue Autonomous Driving of EVs)
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20 pages, 2447 KiB  
Article
Automated Longitudinal Control Based on Nonlinear Recursive B-Spline Approximation for Battery Electric Vehicles
by Jens Jauch, Felix Bleimund, Michael Frey and Frank Gauterin
World Electr. Veh. J. 2019, 10(3), 52; https://doi.org/10.3390/wevj10030052 - 5 Sep 2019
Viewed by 2697
Abstract
This works presents a driver assistance system for energy-efficient ALC of a BEV. The ALC calculates a temporal velocity trajectory from map data. The trajectory is represented by a cubic B-spline function and results from an optimization problem with respect to travel time, [...] Read more.
This works presents a driver assistance system for energy-efficient ALC of a BEV. The ALC calculates a temporal velocity trajectory from map data. The trajectory is represented by a cubic B-spline function and results from an optimization problem with respect to travel time, driving comfort and energy consumption. For the energetic optimization we propose an adaptive model of the required electrical traction power. The simple power train of a BEV allows the formulation of constraints as soft constraints. This leads to an unconstrained optimization problem that can be solved with iterative filter-based data approximation algorithms. The result is a direct trajectory optimization method of which the effort grows linearly with the trajectory length, as opposed to exponentially as with most other direct methods. We evaluate ALC in real test drives with a BEV. We also investigate the energy-saving potential in driving simulations with ALC compared to MLC. On the chosen reference route the ALC saves up to 3.4% energy compared to MLC at same average velocity, and achieves a 2.6% higher average velocity than MLC at the same energy consumption. Full article
(This article belongs to the Special Issue Autonomous Driving of EVs)
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