PRACA ORYGINALNA
Improvement of the Autodriver Algorithm for Autonomous Vehicles Using Roll Dynamics
1
Engineering, VTC
2
Engineering, RMIT
Data nadesłania: 30-12-2020
Data ostatniej rewizji: 12-03-2021
Data akceptacji: 30-03-2021
Data publikacji: 31-03-2021
The Archives of Automotive Engineering – Archiwum Motoryzacji 2021;91(1):5-23
SŁOWA KLUCZOWE
DZIEDZINY
STRESZCZENIE
The autodriver algorithm was introduced as a path-following algorithm for autonomous vehicles, which uses road geometry data and planar vehicle dynamics. In this paper, the autodriver algorithm is improved according to practical implications, and a more realistic vehicle model (roll mode) is used, which considers roll degree of freedom in addition to a planar motion. A Ghost-Car path-following approach is introduced to define the desired location of the car at every instance. Key steady-state characteristics of turning vehicles, namely the curvature, yaw rate, and side-slip responses, are discussed and used to construct a feed-forward component of a path-following controller based on the autodriver algorithm. Feedback control loops are designed and applied to minimise transient errors between the road and vehicle positions. Finally, simulations are performed to analyse the path-following performance of the proposed scheme. The results show promising performance of the controller both in terms of error minimisation and passenger comfort.
REFERENCJE (14)
1.
Bishop R.: Intelligent vehicle technology and trend, Artech House, Norwood, MA. 2005.
2.
Gajek A.: Directions for the development of periodic technical inspection for motor vehicles safety systems. The Archives of Automotive Engineering – Archiwum Motoryzacji. 2018, 80(2), 37–51, DOI: 10.14669/AM.VOL80.ART3.
3.
Hansson L.: Regulatory governance in emerging technologies: The case of autonomous vehicles in Sweden and Norway. Research in Transportation Economics. 2020, 83, 100967, DOI: 10.1016/j.retrec.2020.100967.
4.
Hasan M.H., Hentenryck P.: The benefits of autonomous vehicles for community-based trip sharing. Transportation Research Part C: Emerging Technologies. 2021, 124, 102929, DOI: 10.1016/j.trc.2020.102929.
5.
Hong Z.L., Zimmerman N.: Air quality and greenhouse gas implications of autonomous vehicles in Vancouver, Canada. Transportation Research Part D: Transport and Environment, 2021, 90, 102676, DOI:10.1016/j.trd.2020.102676.
6.
Masmoudi M., Ghazzai H., Frikha M., Massoud Y.: Object Detection Learning Techniques for Autonomous Vehicle Applications. IEEE International Conference on Vehicular Electronics and Safety (ICVES), Cairo, Egypt. 2019, 1–5, DOI: 10.1109/ICVES.2019.8906437.
7.
Molina C.B.S.T., Almeida J.R., Vismari L.F., Gonzalez R.I.R., Naufal J.K., Camargo J.B.: Assuring Fully Autonomous Vehicles Safety by Design The Autonomous Vehicle Control (AVC) Module Strategy, 2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W), Denver, CO. 2017, 16–21, DOI: 10.1109/DSN-W.2017.14.
8.
Mrowicki A., Kubiak P., Zakrzewicz W.: Nonlinear method of precrash velocity determination for Mini car class-B-spline tensors products with probabilistic weights. The Archives of Automotive Engineering – Archiwum Motoryzacji. 2020, 87(1), 97–108, DOI: 10.14669/AM.VOL87.ART8.
9.
Santana E.F.Z., Covas G., Duarte F., Santi P., Ratti C., Kon F.: Transitioning to a driverless city: Evaluating a hybrid system for autonomous and non-autonomous vehicles. Simulation Modelling Practice and Theory. 2021, 107, 102210, DOI: 10.1016/j.simpat.2020.102210.
10.
Sung K., Min K., Choi J.: Driving information logger with in-vehicle communication for autonomous vehicle research. 20th International Conference on Advanced Communication Technology (ICACT), Chuncheon-si Gangwon-do, Korea (South). 2018, 300–302, DOI: 10.23919/ICACT.2018.8323732.
11.
Todorovic M., Simic M., Kumar A.: Managing Transition to Electrical and Autonomous Vehicles. Procedia Computer Science. 2017, 112, 2335–2344, DOI: 10.1016/j.procs.2017.08.201.
12.
Xue Q.W., Wang K., Lu J.J., Liu Y.J.: Rapid Driving Style Recognition in Car-Following Using Machine Learning and Vehicle Trajectory Data. Journal of Advanced Transportation. 2019, 9085238, DOI: 10.1155/2019/9085238.
13.
Vadi S., Padmanaban S., Bayindir R., Blaabjerg F.: A Review on Optimization and Control Methods Used to Provide Transient Stability in Microgrids. Energies. 2019, 12(18), 3582, DOI: 10.3390/en12183582.
14.
Wang J. Zhou L., Pan Y., Lee S., Song Z., Han B.S., et al: Appearance-based Brake-Lights recognition using deep learning and vehicle detection. IEEE Intelligent Vehicles Symposium (IV), Gothenburg, Sweden. 2016, 815–820, DOI: 10.1109/IVS.2016.7535481.
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