Making Transport Safer: V2V-Based Automated Emergency Braking System

An important goal in the field of intelligent transportation systems (ITS) is to provide driving aids aimed at preventing accidents and reducing the number of traffic victims. The commonest traffic accidents in urban areas are due to sudden braking that demands a very fast response on the part of drivers. Attempts to solve this problem have motivated many ITS advances including the detection of the intention of surrounding cars using lasers, radars or cameras. However, this might not be enough to increase safety when there is a danger of collision. Vehicle to vehicle communications are needed to ensure that the other intentions of cars are also available. The article describes the development of a controller to perform an emergency stop via an electro-hydraulic braking system employed on dry asphalt. An original V2V communication scheme based on WiFi cards has been used for broadcasting positioning information to other vehicles. The reliability of the scheme has been theoretically analyzed to estimate its performance when the number of vehicles involved is much higher. This controller has been incorporated into the AUTOPIA program control for automatic cars. The system has been implemented in Citroën C3 Pluriel, and various tests were performed to evaluate its operation

Holistic Vehicle Instrumentation for Assessing Driver Driving Styles

Nowadays, autonomous vehicles are increasing, and the driving scenario that includes both autonomous and human-driven vehicles is a fact. Knowing the driving styles of drivers in the process of automating vehicles is interest in order to make driving as natural as possible. To this end, this article presents a first approach to the design of a controller for the braking system capable of imitating the different manoeuvres that any driver performs while driving. With this aim, different experimental tests have been carried out with a vehicle instrumented with sensors capable of providing real-time information related to the braking system. The experimental tests consist of reproducing a series of braking manoeuvres at different speeds on a flat floor track following a straight path. The tests distinguish between three types of braking manoeuvre: maintained, progressive and emergency braking, which cover all the driving circumstances in which the braking system may intervene. This article presents an innovative approach to characterise braking types thanks to the methodology of analysing the data obtained by sensors during experimental tests. The characterisation of braking types makes it possible to dynamically classify three driving styles: cautious, normal and aggressive. The proposed classifications allow it possible to identify the driving styles on the basis of the pressure in the hydraulic brake circuit, the force exerted by the driver on the brake pedal, the longitudinal deceleration and the braking power, knowing in all cases the speed of the vehicle. The experiments are limited by the fact that there are no other vehicles, obstacles, etc. in the vehicle's environment, but in this article the focus is exclusively on characterising a driver with methods that use the vehicle's dynamic responses measured by on-board sensors. The results of this study can be used to define the driving style of an autonomous vehicle

Drive Force and Longitudinal Dynamics Estimation in Heavy-Duty Vehicles

Modelling the dynamic behaviour of heavy vehicles, such as buses or trucks, can be very useful for driving simulation and training, autonomous driving, crash analysis, etc. However, dynamic modelling of a vehicle is a difficult task because there are many subsystems and signals that affect its behaviour. In addition, it might be hard to combine data because available signals come at different rates, or even some samples might be missed due to disturbances or communication issues. In this paper, we propose a non-invasive data acquisition hardware/software setup to carry out several experiments with an urban bus, in order to collect data from one of the internal communication networks and other embedded systems. Subsequently, non-conventional sampling data fusion using a Kalman filter has been implemented to fuse data gathered from different sources, connected through a wireless network (the vehicle's internal CAN bus messages, IMU, GPS, and other sensors placed in pedals). Our results show that the proposed combination of experimental data gathering and multi-rate filtering algorithm allows useful signal estimation for vehicle identification and modelling, even when data samples are missing

Customer loads of two-wheeled vehicles

Customer usage profiles are the most unknown influences in vehicle design targets and they play an important role in durability analysis. This publication presents a customer load acquisition system for two-wheeled vehicles that utilises the vehicle's onboard signals. A road slope estimator was developed to reveal the unknown slope resistance force with the help of a linear Kalman filter. Furthermore, an automated mass estimator was developed to consider the correct vehicle loading. The mass estimation is performed by an extended Kalman filter. Finally, a model-based wheel force calculation was derived, which is based on the superposition of forces calculated from measured onboard signals. The calculated wheel forces were validated by measurements with wheel–load transducers through the comparison of rainflow matrices. The calculated wheel forces correspond with the measured wheel forces in terms of both quality and quantity. The proposed methods can be used to gather field data for improved vehicle design loads

Data Acquisition System for the Characterization of Biomechanical and Ergonomic Thresholds in Driving Vehicles

[EN] Directive (EU) 2015/653 on driving licenses has involved the modification of different codes that must appear on driver's licenses. The definition of specific codes (20.07 and 40.01) compels measurement of the braking and steering forces. Performing practical tests to assess the driving fitness of special drivers will help to determine the maximum force that a driver can apply on primary controls when driving. From that point, definition of car control adaptations required to supply their functional deficiencies can be stated. This article describes a data acquisition system designed and developed for obtaining data from experimental tests based on the execution of habitual driving manoeuvres (braking, lane change and roundabouts). The data gathered will allow for definition of the thresholds of biomechanical values (forces on the steering wheel and brake pedal) and ergonomic values (driver's upper extremity mobility ranges) necessary for driving motor vehicles. The results have shown that application in real driving tests of the data acquisition system designed provides valid and suitable results for the case studied. Therefore, it will contribute to substantially improving the assessment procedure for drivers in general and for disabled people in particular when obtaining or renewing their driving licenses.This research was funded by Generalitat Valenciana (Spain) under grant APOSTD/2017/055 and by the Universitat Politecnica de Valencia (UPV) (Spain) under the project Characterization of biomechanical and ergonomic thresholds in driving motor vehicles applicable to driver evaluation (Ref. 20190480). This research has been approved by the UPV Ethical Committee at a session celebrated on 18 June 2019 (ref. P5_18_06_19).Dols Ruiz, JF.; Girbés, V.; Luna, Á.; Catalán, J. (2020). Data Acquisition System for the Characterization of Biomechanical and Ergonomic Thresholds in Driving Vehicles. Sustainability. 12(17):1-16. https://doi.org/10.3390/su12177013S1161217Disability Statisticshttps://ec.europa.eu/eurostat/statistics-explained/index.php?title=Disability_statistics_introducedFlash Eurobarometer 345; Accessibility; Report; Directorate-General Justice and Coordinated by Directorate-General for Communication; Brusselshttps://ec.europa.eu/commfrontoffice/publicopinion/flash/fl_345_en.pdfGirbés, V., Hernández, D., Armesto, L., Dols, J., & Sala, A. (2019). Drive Force and Longitudinal Dynamics Estimation in Heavy-Duty Vehicles. Sensors, 19(16), 3515. doi:10.3390/s19163515Dols, J., & Mirabet, E. (2008). Análisis experimental de los rangos de movilidad articular y fuerza muscular requerida para la conducción de vehículos automóviles. Securitas Vialis, 1(1), 17-26. doi:10.1007/s12615-008-9003-zHorberry, T., & Inwood, C. (2010). Defining criteria for the functional assessment of driving. Applied Ergonomics, 41(6), 796-805. doi:10.1016/j.apergo.2010.01.006Dols, J. F., Molina, J., Camacho, F. J., Marín-Morales, J., Pérez-Zuriaga, A. M., & Garcia, A. (2016). Design and Development of Driving Simulator Scenarios for Road Validation Studies. Transportation Research Procedia, 18, 289-296. doi:10.1016/j.trpro.2016.12.03