4,340 research outputs found
Detecting Vehicles' Relative Position on Two-Lane Highways Through a Smartphone-Based Video Overtaking Aid Application
[EN] 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.This work was partially funding by the "Ministerio de Ciencia, Innovacion y Universidades, Programa Estatal de Investigacion, Desarrollo e Innovacion Orientada a los Retos de la Sociedad, Proyectos I+D+I 2018", Spain, under Grant RTI2018-096384-B-I00.Patra, S.; Van Hamme, D.; Veelaert, P.; Tavares De Araujo Cesariny Calafate, CM.; Cano, J.; Manzoni, P.; Zamora, W. (2020). Detecting Vehicles' Relative Position on Two-Lane Highways Through a Smartphone-Based Video Overtaking Aid Application. Mobile Networks and Applications. 25(3):1084-1094. https://doi.org/10.1007/s11036-020-01526-210841094253AbdulQawy 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 341â346National 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 811:059Akritas MG, Murphy SA, Lavalley MP (1995) The Theil-Sen estimator with doubly censored data and applications to astronomy. J Am Stat Assoc 90(429):170â177Bastani 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):2086â2098Bhandari R, Raman B, Padmanabhan V (2019) Fullstop: A camera-assisted system for characterizing unsafe bus stopping. IEEE Trans. Mob. Comput: 1â1Clarke DD, Ward P, Jones J (1998) Overtaking accidents. Transport Research LaboratoryEl-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):4672â4685Galarza EE, Egas FD, Silva FM, Velasco PM, Galarza ED (2018) Real time driver drowsiness detection based on driverâs face image behavior using a system of human computer interaction implemented in a smartphone. In: Rocha Ă, Guarda T (eds) Proceedings of the International Conference on Information Technology & Systems (ICITS 2018). Springer International Publishing, Cham, pp 563â572Groeger J, Clegg B (1994) Why isnât 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): 249â262Kataoka 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 612â615Ma 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 7:8028â8038Mantouka EG, Barmpounakis EN, Vlahogianni EI (2019) Identifying driving safety profiles from smartphone data using unsupervised learning. Saf Sci 119:84â90Patra 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):1550147717748114Patra 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 â19. ACM, New York, pp 84â89Phillips RF (2002) Least absolute deviations estimation via the EM algorithm. Stat Comput 12(3):281â285Rousseeuw PJ, Van Driessen K (2006) Computing LTS regression for large data sets. Data Mining and Knowledge Discovery 12(1):29â45Shikishima A, Nakamura K, Wada T (2018) Detection of texting while walking by using smartphoneâs posture and acceleration information for safety of pedestrians. In: 2018 16th International Conference on Intelligent Transportation Systems Telecommunications (ITST), pp 1â6Siegel AF (1982) Robust regression using repeated medians. Biometrika 69(1):242â244Tanaka S, Takami K (2018) Detection of cyclistsâ violation of stop sign rules using smartphone sensors. In: TENCON 2018 - 2018 IEEE Region 10 Conference, pp 1387â1392Tornell 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):478,064Wallace GK (1991) The JPEG still picture compression standard. Commun ACM 34(4):30â44Warren I, Meads A, Wang C, Whittaker R Awan I, Younas M, Ănal P, Aleksy M (eds) (2019) Monitoring driver behaviour with backpocketdriver. Springer International Publishing, ChamXie 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 801â806Xu 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 â19. ACM, New York, pp 54â66Xu 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):1553â156
Characterizing driving behavior using automatic visual analysis
In this work, we present the problem of rash driving detection algorithm
using a single wide angle camera sensor, particularly useful in the Indian
context. To our knowledge this rash driving problem has not been addressed
using Image processing techniques (existing works use other sensors such as
accelerometer). Car Image processing literature, though rich and mature, does
not address the rash driving problem. In this work-in-progress paper, we
present the need to address this problem, our approach and our future plans to
build a rash driving detector.Comment: 4 pages,7 figures, IBM-ICARE201
Safe Intelligent Driver Assistance System in V2X Communication Environments based on IoT
In the modern world, power and speed of cars have increased steadily, as traffic continued to increase. At the same time highway-related fatalities and injuries due to road incidents are constantly growing and safety problems come first. Therefore, the development of Driver Assistance Systems (DAS) has become a major issue. Numerous innovations, systems and technologies have been developed in order to improve road transportation and safety. Modern computer vision algorithms enable cars to understand the road environment with low miss rates. A number of Intelligent Transportation Systems (ITSs), Vehicle Ad-Hoc Networks (VANETs) have been applied in the different cities over the world. Recently, a new global paradigm, known as the Internet of Things (IoT) brings new idea to update the existing solutions. Vehicle-to-Infrastructure communication based on IoT technologies would be a next step in intelligent transportation for the future Internet-of-Vehicles (IoV).
The overall purpose of this research was to come up with a scalable IoT solution for driver assistance, which allows to combine safety relevant information for a driver from different types of in-vehicle sensors, in-vehicle DAS, vehicle networks and driver`s gadgets.
This study brushed up on the evolution and state-of-the-art of Vehicle Systems. Existing ITSs, VANETs and DASs were evaluated in the research. The study proposed a design approach for the future development of transport systems applying IoT paradigm to the transport safety applications in order to enable driver assistance become part of Internet of Vehicles (IoV). The research proposed the architecture of the Safe Intelligent DAS (SiDAS) based on IoT V2X communications in order to combine different types of data from different available devices and vehicle systems. The research proposed IoT ARM structure for SiDAS, data flow diagrams, protocols.
The study proposes several IoT system structures for the vehicle-pedestrian and vehicle-vehicle collision prediction as case studies for the flexible SiDAS framework architecture. The research has demonstrated the significant increase in driver situation awareness by using IoT SiDAS, especially in NLOS conditions. Moreover, the time analysis, taking into account IoT, Cloud, LTE and DSRS latency, has been provided for different collision scenarios, in order to evaluate the overall system latency and ensure applicability for real-time driver emergency notification. Experimental results demonstrate that the proposed SiDAS improves traffic safety
Smartphone-based vehicle telematics: a ten-year anniversary
This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordJust as it has irrevocably reshaped social life, the fast growth of smartphone ownership is now beginning to revolutionize the driving experience and change how we think about automotive insurance, vehicle safety systems, and traffic research. This paper summarizes the first ten years of research in smartphone-based vehicle telematics, with a focus on user-friendly implementations and the challenges that arise due to the mobility of the smartphone. Notable academic and industrial projects are reviewed, and system aspects related to sensors, energy consumption, and human-machine interfaces are examined. Moreover, we highlight the differences between traditional and smartphone-based automotive navigation, and survey the state of the art in smartphone-based transportation mode classification, vehicular ad hoc networks, cloud computing, driver classification, and road condition monitoring. Future advances are expected to be driven by improvements in sensor technology, evidence of the societal benefits of current implementations, and the establishment of industry standards for sensor fusion and driver assessment
Towards hybrid driver state monitoring : review, future perspectives and the role of consumer electronics
The purpose of this paper is to bring together multiple literature sources which present innovative methodologies for the assessment of driver state, driving context and performance by means of technology within a vehicle and consumer electronic devices. It also provides an overview of ongoing research and trends in the area of driver state monitoring. As part of this review a model of a hybrid driver state monitoring system is proposed. The model incorporates technology within a vehicle and multiple broughtin devices for enhanced validity and reliability of recorded data. Additionally, the model draws upon requirement of data fusion in order to generate unified driver state indicator(-s) that could be used to modify in-vehicle information and safety systems hence, make them driver state adaptable. Such modification could help to reach optimal driving performance in a particular driving situation. To conclude, we discuss the advantages of integrating hybrid driver state monitoring system into a vehicle and suggest future areas of research
VANET Applications: Hot Use Cases
Current challenges of car manufacturers are to make roads safe, to achieve
free flowing traffic with few congestions, and to reduce pollution by an
effective fuel use. To reach these goals, many improvements are performed
in-car, but more and more approaches rely on connected cars with communication
capabilities between cars, with an infrastructure, or with IoT devices.
Monitoring and coordinating vehicles allow then to compute intelligent ways of
transportation. Connected cars have introduced a new way of thinking cars - not
only as a mean for a driver to go from A to B, but as smart cars - a user
extension like the smartphone today. In this report, we introduce concepts and
specific vocabulary in order to classify current innovations or ideas on the
emerging topic of smart car. We present a graphical categorization showing this
evolution in function of the societal evolution. Different perspectives are
adopted: a vehicle-centric view, a vehicle-network view, and a user-centric
view; described by simple and complex use-cases and illustrated by a list of
emerging and current projects from the academic and industrial worlds. We
identified an empty space in innovation between the user and his car:
paradoxically even if they are both in interaction, they are separated through
different application uses. Future challenge is to interlace social concerns of
the user within an intelligent and efficient driving
On driver behavior recognition for increased safety:A roadmap
Advanced Driver-Assistance Systems (ADASs) are used for increasing safety in the automotive domain, yet current ADASs notably operate without taking into account driversâ states, e.g., whether she/he is emotionally apt to drive. In this paper, we first review the state-of-the-art of emotional and cognitive analysis for ADAS: we consider psychological models, the sensors needed for capturing physiological signals, and the typical algorithms used for human emotion classification. Our investigation highlights a lack of advanced Driver Monitoring Systems (DMSs) for ADASs, which could increase driving quality and security for both drivers and passengers. We then provide our view on a novel perception architecture for driver monitoring, built around the concept of Driver Complex State (DCS). DCS relies on multiple non-obtrusive sensors and Artificial Intelligence (AI) for uncovering the driver state and uses it to implement innovative HumanâMachine Interface (HMI) functionalities. This concept will be implemented and validated in the recently EU-funded NextPerception project, which is briefly introduced
- âŠ