Skip to main content
SpringerLink
Log in

An intelligent curve warning system for road cycling races

  • Technical Note
  • Published:
Sports Engineering Aims and scope Submit manuscript

Abstract

To mitigate the incidence of the crashes in road cycling races, new technologies that can help the riders in evaluating risks in advance are called for. An advanced rider assistance system has been developed to warn the riders before they negotiate a corner during a fast-descending section. The advanced rider assistance system was based on the optimal manoeuvre method applied to a state-of-the-art cycling locomotion model. Global positioning system data collected at 1 Hz were used to compute initial conditions for the optimal manoeuvre calculation. The advanced rider assistance system was deployed on a mobile device, and it was tested off-line for real-time performances. Computational cost was examined versus the horizon length to arrive at an optimum for the problem. The proposed advanced rider assistance system was designed to provide audible warning to the riders so they can focus their actions and improve trajectory security. This work prompts more research to reduce the incidence of crashes in cycling training and racing by means of new methods based on optimal manoeuvre calculation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Notes

  1. https://openweathermap.org/.

  2. www.gpsvisualizer.com.

  3. https://termux.com/.

  4. https://bitbucket.org/andrea_zignoli/optimal-itt/src/master/

References

  1. UCI - Press releases (2020) Road cycling: the UCI announces the introduction of numerous measures to improve rider safety as of 2021. https://www.uci.org/inside-uci/press-releases/road-cycling-the-uci-announces-the-introduction-of-numerous-measures-to-improve-rider-safety-as-of-2021

  2. Biral F, Da Lio M, Lot R, Sartori R (2010) An intelligent curve warning system for powered two wheel vehicles. Eur Transp Res Rev 2:147–156. https://doi.org/10.1007/s12544-010-0033-2

    Article  Google Scholar 

  3. Cossalter V, Da Lio M, Lot R, Fabbri L (1999) A general method for the evaluation of vehicle manoeuvrability with special emphasis on motorcycles. Veh Syst Dyn 31:113–135. https://doi.org/10.1076/vesd.31.2.113.2094

    Article  Google Scholar 

  4. Cossalter V, Da Lio M, Biral F, Fabbri L (1998) Evaluation of motorcycle maneuverability with the optimal Maneuver method. SAE Tech Pap. https://doi.org/10.4271/983022

    Article  Google Scholar 

  5. Biral F, Bertolazzi E, Bosetti P (2015) Notes on numerical methods for solving optimal control problems. IEEJ J Ind Appl 5:154–166

    Google Scholar 

  6. Zignoli A (2020) Influence of corners and road conditions on cycling individual time trial performance and ‘optimal’ pacing strategy: a simulation study. J Sports Eng Technol. https://doi.org/10.1177/1754337120974872

    Article  Google Scholar 

  7. Zignoli A, Biral F (2020) Prediction of pacing and cornering strategies during cycling individual time trials with optimal control. Sports Eng 23:13. https://doi.org/10.1007/s12283-020-00326-x

    Article  Google Scholar 

  8. Lot R, Biral F (2014) A curvilinear abscissa approach for the lap time optimization of racing vehicles. IFAC Proc Vol 47:7559–7565

    Article  Google Scholar 

  9. Burke E (2003) High-tech cycling. Human Kinetics, Champaign

    Google Scholar 

  10. Martin JC, Milliken DL, Cobb JE et al (1998) Validation of a mathematical model for road cycling power. J Appl Biomech 14:276–291. https://doi.org/10.1123/jab.14.3.276

    Article  Google Scholar 

  11. Faria EW, Parker DL, Faria IE (2005) The science of cycling: factors affecting performance—part 2. Sports Med 35:313–337. https://doi.org/10.2165/00007256-200535040-00003

    Article  Google Scholar 

  12. Heil DP (2001) Body mass scaling of projected frontal area in competitive cyclists. Eur J Appl Physiol 85:358–366. https://doi.org/10.1007/s004210100424

    Article  Google Scholar 

  13. Beal L, Hill D, Martin R, Hedengren J (2018) GEKKO optimization suite. Processes 6:106. https://doi.org/10.3390/pr6080106

    Article  Google Scholar 

  14. Wächter A, Biegler LT (2006) On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Math Program 106:25–57. https://doi.org/10.1007/s10107-004-0559-y

    Article  MathSciNet  MATH  Google Scholar 

  15. Rice RS (1973) Measuring car-driver interaction with the gg diagram. In: SAE technical paper. Society of automotive engineers, New York

  16. Muller S, Uchanski M, Hedrick K (2003) Estimation of the maximum tire-road friction coefficient. J Dyn Syst Meas Contr 125:607–617

    Article  Google Scholar 

  17. Vieyra R, Vieyra C, Jeanjacquot P et al (2015) Turn your smartphone into a science laboratory. Sci Teach 82:32

    Google Scholar 

  18. Biral F, Lot R, Rota S et al (2012) intersection support system for powered two-wheeled vehicles: threat assessment based on a receding horizon approach. IEEE Trans Intell Transp Syst 13:805–816. https://doi.org/10.1109/TITS.2011.2181835

    Article  Google Scholar 

  19. Huth V, Biral F, Martín Ó, Lot R (2012) Comparison of two warning concepts of an intelligent Curve Warning system for motorcyclists in a simulator study. Accid Anal Prev 44:118–125. https://doi.org/10.1016/j.aap.2011.04.023

    Article  Google Scholar 

  20. Kegelman JC, Harbott LK, Gerdes JC (2017) Insights into vehicle trajectories at the handling limits: analysing open data from race car drivers. Veh Syst Dyn 55:191–207

    Article  Google Scholar 

  21. Hedengren JD, Shishavan RA, Powell KM, Edgar TF (2014) Nonlinear modeling, estimation and predictive control in APMonitor. Comput Chem Eng 70:133–148. https://doi.org/10.1016/j.compchemeng.2014.04.013

    Article  Google Scholar 

  22. Dal Bianco N, Bertolazzi E, Biral F, Massaro M (2019) Comparison of direct and indirect methods for minimum lap time optimal control problems. Veh Syst Dyn 57:665–696. https://doi.org/10.1080/00423114.2018.1480048

    Article  Google Scholar 

  23. Bertolazzi E, Bevilacqua P, Biral F, et al (2018) Efficient Re-planning for Robotic Cars. In: 2018 European Control Conference (ECC). IEEE, Limassol, pp 1068–1073

  24. Bertolazzi E, Frego M, Biral F (2020) Point data reconstruction and smoothing using cubic splines and clusterization. Math Comput Simul 176:36–56. https://doi.org/10.1016/j.matcom.2020.04.002

    Article  MathSciNet  MATH  Google Scholar 

  25. Frego M, Bertolazzi E, Biral F et al (2017) Semi-analytical minimum time solutions with velocity constraints for trajectory following of vehicles. Automatica 86:18–28. https://doi.org/10.1016/j.automatica.2017.08.020

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Industrial Engineering, University of Trento, Via Sommarive 9, 38123, Povo, Trento, Italy

    Andrea Zignoli

  2. Cerism Research Centre, University of Verona, Trento, Italy

    Andrea Zignoli

Authors
  1. Andrea Zignoli
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Andrea Zignoli.

Ethics declarations

Conflict of interest

The authors declare no conflicts of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zignoli, A. An intelligent curve warning system for road cycling races. Sports Eng 24, 19 (2021). https://doi.org/10.1007/s12283-021-00356-z

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12283-021-00356-z

Keywords

Search

Navigation