Intelligent Functions Development on Autonomous Electric Vehicle Platform
,
 
,
 
,
 
 
 
More details
Hide details
1
Tallinn University of Technology, Tallinn, Estonia
 
 
Submission date: 2019-12-11
 
 
Acceptance date: 2020-01-22
 
 
Online publication date: 2020-06-24
 
 
Publication date: 2020-06-24
 
 
Journal of Machine Engineering 2020;20(2):114-125
 
KEYWORDS
TOPICS
ABSTRACT
Autonomous driving is no longer just an idea of technology vision instead a real technical trend all over the world. The continuing development to a further level of autonomy requires more on mobile robots safety while bringing more challenges to human-vehicle interaction. A robot autonomous vehicle (AV) as a research platform operates an experimental study on human-AV-interaction (HAVI) and performs a novel method for mobile robot safety assurance. Not only autonomous driving technology itself but human cognition also performs an essential role in how to ensure better autonomous mobile robot safety. A Wizard-of-Oz experiment in the university combing a survey-based study indicates public attitudes towards driverless robot vehicles. HAVI experiment have been carried through light patterns designed for experiment. This paper presents an attempt to investigate humans’ acceptance and emotions as well as a validation to bring the mobile robot vehicle to a high-level autonomy.
 
REFERENCES (30)
1.
ROTHENBUCHER D., LI J., SIRKIN D., MOK B., JU W., 2016, Ghost driver: A field study investigating the interaction between humans and driverless vehicles, 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), 795–802.
 
2.
LAGSTRÖM T., LUNDGREN V.M., 2015, AVIP-Autonomous vehicles interaction with humans, Chalmers University, Retrieved from http://publications.lib.chalme....
 
3.
CLAMANN M., AUBERT M., CUMMINGS M.L., 2017, Evaluation of Vehicle-to-Human Communication Displays for Autonomous Vehicles, 96th Annual Transportation Research Board Meeting, Washington DC.
 
4.
DAHLBÄCK N., JÖNSSON A., AHRENBERG L., 1993, Wizard of Oz studies – why and how, Knowledge-Based Syst., 6/4, 258–266.
 
5.
Automated Driving Roadmap, 2015, Brussels, Belgium.
 
6.
KYRIAKIDIS M., HAPPEE R., DE WINTER J.C.F., 2015, Public opinion on automated driving: Results of an international questionnaire among 5000 respondents, Transp. Res. Part F Traffic Psychol. Behav., 32, 127–140.
 
7.
SELL R., LEIER M., RASSÕLKIN A., ERNITS J.-P., 2018, Self-driving car ISEAUTO for research and education, 19th International Conference on Research and Education in Mechatronics (REM 2018).
 
8.
EVERINGHAM M., 2015, The Pascal Visual Object Classes Challenge: A Retrospective, Int. J. Comput. Vis., 111/1, 98–136.
 
9.
LIN T.Y,. 2014, Microsoft COCO: Common Objects in Context, Springer, Cham, 740–755.
 
10.
RASSÕLKIN A., GEVORKOV L., VAIMANN T., KALLASTE A., SELL R., 2018, Calculation of the traction effort of ISEAUTO self-driving vehicle, 25th International Workshop on Electric Drives: Optimization in Control of Electric Drives (IWED), 1–5.
 
11.
RASSÕLKIN A., VAIMANN T., KALLASTE A., 2018, Propulsion Motor Drive Topology Selection for Further Development of ISEAUTO Self-Driving Car, 59th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON).
 
12.
SELL R., RASSÕLKIN A., RUXIN W., OTTO T., 2019, Integration of Autonomous Vehicles and Industry 4.0, Proceedings of the Estonian Academy of Sciences, 68/4, 389–394.
 
13.
DEB S., WARNER B., POUDEL S.R., BHANDARI S., 2016, Identification of external design preferences in autonomous vehicles, Proceedings of the Industrial and Systems Engineering Research Conference, Anaheim, California.
 
14.
TURNER C., MCCLURE R., 2003, Age and gender differences in risk-taking behaviour as an explanation for high incidence of motor vehicle crashes as a driver in young males, Inj. Control Saf. Promot., 10/3, 123–130.
 
15.
LIU P., ZHANG Y., HE Z., 2019, The effect of population age on the acceptable safety of self-driving vehicles, Reliab. Eng. Syst. Saf., 185, 341–347.
 
16.
ALESSANDRINI A., ALFONSI R., SITE P.D., STAM D., 2014, Users’ Preferences towards Automated Road Public Transport: Results from European Surveys, Transp. Res. Procedia, 3, 139–144.
 
17.
KUTS V., OTTO T., TÄHEMAA T., BONDARENKO Y., 2019. Digital twin based synchronised control and simulation of the industrial robotic cell using virtual reality, Journal of Machine Engineering, 19/1, 128−145.
 
18.
DAVIS F.D., 1989, Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology, MIS Q., 13,/3, 319–340.
 
19.
XU Z., 2018, What drives people to accept automated vehicles? Findings from a field experiment, Transp. Res. Part C Emerg. Technol., 95, 320–334.
 
20.
ZHANG T., 2019, The roles of initial trust and perceived risk in public’s acceptance of automated vehicles, Transp. Res. Part C Emerg. Technol., 98, 207–220.
 
21.
PARASURAMAN R., SHERIDAN T.B., WICKENS C.D., 2000, A model for types and levels of human interaction with automation, IEEE Trans. Syst. Man, Cybern. – Part A Syst. Humans, 30/3, 286–297.
 
22.
LEE J.D., SEE K.A., 2004, Trust in Automation: Designing for Appropriate Reliance, Hum. Factors J. Hum. Factors Ergon. Soc., 46/1, 50–80.
 
23.
KÖRBER M., BASELER E., BENGLER K., 2018, Introduction matters: Manipulating trust in automation and reliance in automated driving, Appl. Ergon., 66, 18–31.
 
24.
WILKINSON R. G., PICKETT K. E., 2006, Income inequality and population health: A review and explanation of the evidence, Soc. Sci. Med., vol. 62/7, 1768–1784.
 
25.
DEB S., RAHMAN M.M., STRAWDERMAN L.J., GARRISON T.M., 2018, Pedestrians’ Receptivity Toward Fully Automated Vehicles: Research Review and Roadmap for Future Research, IEEE Trans. Human-Machine Syst., 48/3, 279–290.
 
26.
MARANGUNIĆ N., GRANIĆ A., 2015, Technology acceptance model: a literature review from 1986 to 2013, Univers. Access Inf. Soc., 14/1, 81–95.
 
27.
DAVIS F.D., BAGOZZI R.P., WARSHAW P.R., 1989, User Acceptance of Computer Technology: A Comparison of Two Theoretical Models, Management Science, 35/8, 982–1003.
 
28.
BROOKS R., 2017, The Big Problem With Self-Driving Cars Is People, IEEE Spectrum, Retrieved from https://spectrum.ieee.org/tran....
 
29.
MOKHTARZADEH A.A., YANGQING Z.J., 2018, Human-Robot Interaction and Self-Driving Cars Safety Integration of Dispositif Networks, IEEE International Conference on Intelligence and Safety for Robotics (ISR), 494–499.
 
30.
SELL R., OTTO T., 2008, Remotely controlled multi robot environment, Proceedings of 19th EAEEIE Annual Conference, Tallinn, Estonia, 20−25.
 
 
CITATIONS (17):
1.
Design, User Experience, and Usability
Shuyi Cui, Donghan Hou, Jiayue Li, Yuwei Liu, Zi Wang, Jiayu Zheng, Xueshi Dou, Zhanyao Feng, Yuxuan Gu, Minglan Li, Songbo Ni, Ziwei Ran, Bojuan Ren, Jingyi Sun, Shenmin Wang, Xinyan Xiong, Guanzhuo Zhang, Wangjun Li, Jingpeng Jia, Xin Xin
 
2.
A Two-Layered Approach for the Validation of an Operational Autonomous Shuttle
Mohsen Malayjerdi, Quentin A. Goss, Mustafa İlhan Akbaş, Raivo Sell, Mauro Bellone
IEEE Access
 
3.
Self-driving Shuttle Bus Use Case in City of Tallinn
K Kalda, R Sell, R-M Soe
IOP Conference Series: Materials Science and Engineering
 
4.
Experimentally Adjusted Modelling and Simulation Technique for a Catamaran Autonomous Surface Vessel
Igor Astrov, Andres Udal, Heigo Molder, Tanel Jalakas, Taavi Moller
2022 International Conference on Electrical, Computer and Energy Technologies (ICECET)
 
5.
Applications of New Technology in Operations and Supply Chain Management
Wade Forsyth, Abubaker Haddud
 
6.
Safety Assessment and Simulation of Autonomous Vehicle in Urban Environments
Mohsen Malayjerdi, Bariş Cem Baykara, Raivo Sell, Ehsan Malayjerdi
IOP Conference Series: Materials Science and Engineering
 
7.
An Optimal Control Method for an Autonomous Surface Vessel for Environment Monitoring and Cargo Transportation Applications
Igor Astrov, Andres Udal, Heigo Molder
2021 25th International Conference Electronics
 
8.
Range Sensor Overview and Blind-Zone Reduction of Autonomous Vehicle Shuttles
Junyi Gu, Tek Raj Chhetri
IOP Conference Series: Materials Science and Engineering
 
9.
Cyber-physical universal safety and crash detection system for autonomous robot
Heiko Pikner, Mohsen Malayjerdi
Robotic Systems and Applications
 
10.
Safety Toolkit for Automated Vehicle Shuttle -Practical Implementation of Digital Twin
Raivo Sell, Ehsan Malayjerdi, Mohsen Malayjerdi, Baris Cem Baykara
2022 International Conference on Connected Vehicle and Expo (ICCVE)
 
11.
Research on NEV Platform Development Strategies for Automotive Companies
Zongwei Liu, Xinglong Liu, Fuquan Zhao
World Electric Vehicle Journal
 
12.
Comprehensive recycling of lithium-ion batteries: Fundamentals, pretreatment, and perspectives
Wenhao Yu, Yi Guo, Shengming Xu, Yue Yang, Yufeng Zhao, Jiujun Zhang
Energy Storage Materials
 
13.
A Trajectory Control Method for a Strongly Underactuated Spherical Underwater Surveillance Robot
Igor Astrov, Andres Udal, Heigo Molder
2021 IEEE 25th International Conference on Intelligent Engineering Systems (INES)
 
14.
Intelligent System Solutions for Auto Mobility and Beyond
Raivo Sell, Ralf-Martin Soe, Ruxin Wang, Anton Rassõlkin
 
15.
A Model-Based PID Control of Turning Maneuver for Catamaran Autonomous Surface Vessel
Igor Astrov
2023 International Conference on Engineering and Emerging Technologies (ICEET)
 
16.
A Model-Based LQR Control of an Obstacle Avoidance Maneuver of a Self-Driving Car
Igor Astrov, Andres Udal, Heiko Pikner, Ehsan Malayjerdi
2022 IEEE 20th Jubilee World Symposium on Applied Machine Intelligence and Informatics (SAMI)
 
17.
A Model-Based Adaptive Control of an Autonomous Driving Car for Lane Change Maneuver
Igor Astrov, Andres Udal, Martin Jaanus
2021 44th International Convention on Information, Communication and Electronic Technology (MIPRO)
 
eISSN:2391-8071
ISSN:1895-7595
Journals System - logo
Scroll to top