Abstract
In this paper we present a smartphone-based real-time video overtaking architecture for vehicular networks. The developed application aims to prevent head-on collisions that might occur due to attempts to overtake when the view of the driver is obstructed by the presence of a larger vehicle ahead. Under such conditions, the driver does not have a clear view of the road ahead and of any vehicles that might be approaching from the opposite direction, resulting in a high probability of accident occurrence. Our application relies on the use of a dashboard-mounted smartphone with the back camera facing the windshield, and having the screen towards the driver. A video is streamed from the vehicle ahead to the vehicle behind automatically, where it is displayed so that the driver can decide if it is safe to overtake. One of the major challenges is the way to pick the right video source and destination among vehicles in close proximity, depending on their relative position on the road. For this purpose, we have focused on two different methods: one relying solely on GPS data, and the other involving the use of the camera and vehicle heading information. Our experiments show that the faster method, using just the location information, is prone to errors due to GPS inaccuracies. A second method that depends on data fusion from the optical sensor and GPS, although accurate over short distances, becomes more computationally intensive, and its performance significantly depends on the quality of the camera.
Similar content being viewed by others
References
AbdulQawy A, Elkhouly R, Sallam E (2018) Approaching rutted road-segment alert using smartphone. In: 2018 13th International Conference on Computer Engineering and Systems (ICCES), pp 341–346
National Highway Traffic Safety Administration, et al. (2008) National motor vehicle crash causation survey: Report to congress. National Highway Traffic Safety Administration Technical Report DOT HS 811:059
Akritas MG, Murphy SA, Lavalley MP (1995) The Theil-Sen estimator with doubly censored data and applications to astronomy. J Am Stat Assoc 90(429):170–177
Bastani Zadeh R, Ghatee M, Eftekhari HR (2018) Three-phases smartphone-based warning system to protect vulnerable road users under fuzzy conditions. IEEE Trans Intell Transp Syst 19(7):2086–2098
Bhandari R, Raman B, Padmanabhan V (2019) Fullstop: A camera-assisted system for characterizing unsafe bus stopping. IEEE Trans. Mob. Comput: 1–1
Clarke DD, Ward P, Jones J (1998) Overtaking accidents. Transport Research Laboratory
El-Wakeel AS, Li J, Noureldin A, Hassanein HS, Zorba N (2018) Towards a practical crowdsensing system for road surface conditions monitoring. IEEE Internet of Things Journal 5(6):4672–4685
Galarza EE, Egas FD, Silva FM, Velasco PM, Galarza ED (2018) Real time driver drowsiness detection based on driver’s face image behavior using a system of human computer interaction implemented in a smartphone. In: Rocha Á, Guarda T (eds) Proceedings of the International Conference on Information Technology & Systems (ICITS 2018). Springer International Publishing, Cham, pp 563–572
Groeger J, Clegg B (1994) Why isn’t driver training contributing more to road safety?. In: Behavioural Research in Road Safety IV. Proceedings of a seminar held 6-7 September 1993, Brunel University.(TRL published article PA 3035/94)
Hadiwardoyo SA, Patra S, Calafate CT, Cano JC, Manzoni P (2018) An intelligent transportation system application for smartphones based on vehicle position advertising and route sharing in vehicular ad-hoc networks. J Comput Sci Technol 33(2): 249–262
Kataoka K, Gangwar S, Mudda KY, Mandal S (2018) A smartphone-based probe data platform for road management and safety in developing countries. In: 2018 IEEE international conference on data mining workshops (ICDMW), pp 612–615
Ma Y, Zhang Z, Chen S, Yu Y, Tang K (2019) A comparative study of aggressive driving behavior recognition algorithms based on vehicle motion data. IEEE Access 7:8028–8038
Mantouka EG, Barmpounakis EN, Vlahogianni EI (2019) Identifying driving safety profiles from smartphone data using unsupervised learning. Saf Sci 119:84–90
Patra S, Calafate CT, Cano JC, Veelaert P, Philips W (2017) Integration of vehicular network and smartphones to provide real-time visual assistance during overtaking. International Journal of Distributed Sensor Networks 13(12):1550147717748114
Patra S, Zamora W, Calafate CT, Cano JC, Manzoni P, Veelaert P (2019) Using the smartphone camera as a sensor for safety applications. In: Proceedings of the 5th EAI International Conference on Smart Objects and Technologies for Social Good, GoodTechs ’19. ACM, New York, pp 84–89
Phillips RF (2002) Least absolute deviations estimation via the EM algorithm. Stat Comput 12(3):281–285
Rousseeuw PJ, Van Driessen K (2006) Computing LTS regression for large data sets. Data Mining and Knowledge Discovery 12(1):29–45
Shikishima A, Nakamura K, Wada T (2018) Detection of texting while walking by using smartphone’s posture and acceleration information for safety of pedestrians. In: 2018 16th International Conference on Intelligent Transportation Systems Telecommunications (ITST), pp 1–6
Siegel AF (1982) Robust regression using repeated medians. Biometrika 69(1):242–244
Tanaka S, Takami K (2018) Detection of cyclists’ violation of stop sign rules using smartphone sensors. In: TENCON 2018 - 2018 IEEE Region 10 Conference, pp 1387–1392
Tornell SM, Patra S, Calafate CT, Cano JC, Manzoni P (2015) GRCBox: extending smartphone connectivity in vehicular networks. International Journal of Distributed Sensor Networks 11(3):478,064
Wallace GK (1991) The JPEG still picture compression standard. Commun ACM 34(4):30–44
Warren I, Meads A, Wang C, Whittaker R Awan I, Younas M, Ünal P, Aleksy M (eds) (2019) Monitoring driver behaviour with backpocketdriver. Springer International Publishing, Cham
Xie J, Hilal AR, Kulic D (2018) Driver distraction recognition based on smartphone sensor data. In: 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp 801–806
Xu X, Yu J, Chen Y, Zhu Y, Kong L, Li M (2019) Breathlistener: Fine-grained breathing monitoring in driving environments utilizing acoustic signals. In: Proceedings of the 17th annual international conference on mobile systems, applications, and services, MobiSys ’19. ACM, New York, pp 54–66
Xu X, Yu J, Chen Y, Zhu Y, Qian S, Li M (2018) Leveraging audio signals for early recognition of inattentive driving with smartphones. IEEE Trans Mob Comput 17(7):1553–1567
Acknowledgements
This work was partially funding by the “Ministerio de Ciencia, Innovación y Universidades, Programa Estatal de Investigación, Desarrollo e Innovación Orientada a los Retos de la Sociedad, Proyectos I+D+I 2018”, Spain, under Grant RTI2018-096384-B-I00.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Patra, S., Van Hamme, D., Veelaert, P. et al. Detecting Vehicles’ Relative Position on Two-Lane Highways Through a Smartphone-Based Video Overtaking Aid Application. Mobile Netw Appl 25, 1084–1094 (2020). https://doi.org/10.1007/s11036-020-01526-2
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11036-020-01526-2