Fundamentals of Motion Planning for Mitigating Motion Sickness in Automated Vehicles

被引:13
|
作者
Htike, Zaw [1 ]
Papaioannou, Georgios [2 ]
Siampis, Efstathios [3 ,4 ]
Velenis, Efstathios [3 ,4 ]
Longo, Stefano [5 ]
机构
[1] Cranfield Univ, Transport Syst, Bedford MK43 0AL, England
[2] KTH Royal Inst Technol, Sch Engn Sci, S-10044 Stockholm, Sweden
[3] Cranfield Univ, Adv Vehicle Engn Ctr, Cranfield MK43 0AL, Beds, England
[4] Cranfield Univ, Sch Engn, Cranfield MK43 0AL, Beds, England
[5] Cranfield Univ, Automot Engn, Cranfield MK43 0AL, Beds, England
基金
英国工程与自然科学研究理事会;
关键词
Roads; Planning; Mathematical models; Vehicle dynamics; Dynamics; Vehicles; ISO Standards; Motion sickness; automated vehicles; optimal control; mutil-objective optimization; OPTIMIZATION;
D O I
10.1109/TVT.2021.3138722
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This paper investigates the fundamentals of motion planning for minimizing motion sickness in transportation systems of higher automation levels. The optimum velocity profile is sought for a predefined road path from a specific starting point to a final one within specific and given boundaries and constraints in order to minimize the motion sickness and the journey time. An empirical approach based on British standard is used to evaluate motion sickness. The trade-off between minimizing motion sickness and journey time is investigated through multi-objective optimization by altering the weighting factors. The correlation between sickness and journey time is represented as a Pareto front because of their conflicting relation. The compromise between the two components is quantified along the curve, while the severity of the sickness is determined using frequency analysis. In addition, three case studies are developed to investigate the effect of driving style, vehicle speed, and road width, which can be considered among the main factors affecting motion sickness. According to the results, the driving style has higher impact on both motion sickness and journey time compared to the vehicle speed and the road width. The benefit of higher vehicle speed gives shorter journey time while maintaining relatively lower illness rating compared with lower vehicle speed. The effect of the road width is negligible on both sickness and journey time when travelling on a longer road. The results pave the path for the development of vehicular technologies to implement for real-world driving from the outcomes of this paper.
引用
收藏
页码:2375 / 2384
页数:10
相关论文
共 50 条
  • [21] On-Road Motion Planning for Automated Vehicles at Ulm University
    Graf, Maximilian
    Speidel, Oliver M.
    Ruof, Jona O.
    Dietmayer, Klaus
    IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE, 2022, 14 (03) : 121 - 131
  • [22] Optimal motion planning for automated vehicles with scheduled arrivals at intersections
    Mueller, Eduardo Rauh
    Wahlberg, Bo
    Carlson, Rodrigo Castelan
    2018 EUROPEAN CONTROL CONFERENCE (ECC), 2018, : 1672 - 1678
  • [23] Motion Sickness in Automated Vehicles: Principal Research Questions and the Need for Common Protocols
    Diels, Cyriel
    Ye, Ying
    Bos, Jelte E.
    Maeda, Setsuo
    SAE INTERNATIONAL JOURNAL OF CONNECTED AND AUTOMATED VEHICLES, 2022, 5 (02): : 121 - 134
  • [24] The (in)effectiveness of anticipatory vibrotactile cues in mitigating motion sickness
    Reuten, A. J. C.
    Smeets, J. B. J.
    Rausch, J.
    Martens, M. H.
    Schmidt, E. A.
    Bos, J. E.
    EXPERIMENTAL BRAIN RESEARCH, 2023, 241 (05) : 1251 - 1261
  • [25] The (in)effectiveness of anticipatory vibrotactile cues in mitigating motion sickness
    A. J. C. Reuten
    J. B. J. Smeets
    J. Rausch
    M. H. Martens
    E. A. Schmidt
    J. E. Bos
    Experimental Brain Research, 2023, 241 : 1251 - 1261
  • [26] Specification-Compliant Driving Corridors for Motion Planning of Automated Vehicles
    Liu, Edmond Irani
    Althoff, Matthias
    IEEE TRANSACTIONS ON INTELLIGENT VEHICLES, 2023, 8 (09): : 4180 - 4197
  • [27] A Model Based Motion Planning Framework For Automated Vehicles in Structured Environments
    Graf, Maximilian
    Speidel, Oliver
    Dietmayer, Klaus
    2019 30TH IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV19), 2019, : 201 - 206
  • [28] Real-time optimal motion planning for automated road vehicles
    Hegedus, Ferenc
    Becsi, Tamas
    Aradi, Szilard
    Szalay, Zsolt
    Gaspar, Peter
    IFAC PAPERSONLINE, 2020, 53 (02): : 15647 - 15652
  • [29] Promises and Challenges of Reinforcement Learning Applications in Motion Planning of Automated Vehicles
    Pankiewicz, Nikodem
    Wrona, Tomasz
    Turlej, Wojciech
    Orlowski, Mateusz
    ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING (ICAISC 2021), PT II, 2021, 12855 : 318 - 329
  • [30] Motion Planning for Connected Automated Vehicles at Occluded Intersections With Infrastructure Sensors
    Mueller, Johannes
    Strohbeck, Jan
    Herrmann, Martin
    Buchholz, Michael
    IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2022, 23 (10) : 17479 - 17490