Analysis of vehicle driving behavior on special road segments using context-specific information

Typical telematics and fleet-management systems today use embedded systems attached to the vehicle and get their driving data from their diagnostics port to identify the action of driver and grade it to provide feedback based on their quality of driving to efficiently handle the vehicle and also their driving behavior. Today’s insurance companies provide embedded devices or the customer’s smartphone to analyze basic driving parameters such as speed, rpm, GPS location to understand driver’s braking, acceleration and distance traveled over a period and use it to assess quotes for insurance premium. But most of the solutions above do not consider of context-specific information in the cases of fixed-route scenarios whose details can be understood better in the first place and use it to grade the driver’s performance for the trip more efficiently. In this experiment, a driver’s behavior on a pre-defined route is analyzed on different perspectives by also taking into account of the road context, such as turns, straight road segments, traffic lights, stop signs, etc. and graded accordingly and providing a score to reflect their behavior in each segment of the road as well as a complete score for their tri

Analyzing driving behavior from CAN data using context-specific information

Typical telematics and fleet-management systems today use embedded systems attached to the vehicle and get their driving data from their diagnostics port to identify the action of driver and grade it to provide feedback based on their quality of driving to efficiently handle the vehicle and also their driving behavior. Today’s insurance companies provide embedded devices or the customer’s smartphone to analyze basic driving parameters such as speed, rpm, GPS location to understand driver’s braking, acceleration and distance travelled over a period and use it to assess quotes for insurance premium. But most of the solutions above do not consider of context specific information in the cases of fixed-route scenarios whose details can be understood better in the first place and use it to grade the driver’s performance for the trip more efficiently. In this experiment, a driver’s behavior on a pre-defined route is analyzed on different perspectives by also taking into account of the road context, such as turns, straight road segments, traffic lights, stop signs etc. and graded accordingly and providing a score to reflect their behavior in each segment of the road as well as a complete score for their trip

PEMANFAATAN ON BOARD DIAGNOSTIC-II UNTUK PEMANTAUAN SENSOR ENGINE CONTROL UNIT PADA KENDARAAN RODA EMPAT

Kecelakaan lalu lintas adalah suatu peritiwa di jalan yang tidak diduga dan tidak disengaja melibatkan kendaraan dengan atau tanpa pengguna jalan lain yang mengakibatkan korban manusia dan/atau kerugian harta benda. Kecelakaan juga dapat disebabkan oleh kelalaian seorang pengemudi yang tidak dapat memeriksa kondisi mesin sehingga mengakibatkan suatu bahaya yang serius untuk para pengemudi serta sekitarnya. Dengan bantuan On Board Diagnostic-II (OBD-II) yang dapat memudahkan transfer data dari Engine Control Unit (ECU) akan dapat mengurangi tingkat kecelakaan lalu lintas. Sensor-sensor yang ditinjau antara lain Revolutions Per Minute (RPM), tingkat suhu mobil, load, kecepatan, dan throttle. Tipe transmisi OBD-II yang digunakan adalah tipe ELM327. Proses perekaman data 5 parameter yang dilakukan melalui database dan paramosa pada skenario jalan umum dan jalan khusus (toll) mendapatkan rekam data di kedua skenario pada databse sejumlah 54 data, pada paramosa masing-masing merekam 16 data pada jalan umum dan 24 data pada jalan khusus (toll). Dengan data perekaman yang sinkron antara database dan paramosa, maka dapat dihasilkan selisih rata-rata standar deviasi yang bernilai 0 (nol). Yang berarti dapat disimpulkan bahwa semua himpunan nilai yang terekam oleh database dan paramosa adalah sama . Kata Kunci: OBD-II, ECU, cloud server, database, paramosa

Driving pattern analysis to determine driver behaviors for local authority based on cloud using OBD II

Aggressive driving is the main cause of road accidents and it is affected by driving behavior which endanger not only the driver himself but also the people around. It is very significant step to identify such behaviors of the drivers by the local authorities which would help in correcting the behaviors or to understand the root cause of the accidents by analyzing the data recorded by the On Board Diagnostic( OBD ) II device. An aggressive driving behavior is characterized by sudden change inmaneuverings of vehicle which eventually yields non uniform parameters values returned by the ECU (Engine Control Unit) system without any specific reason. In this research work, the real time data is recorded from ECU using OBD II and the accelerometer. The Artificial Intelligenceis used in grouping the different types of data toidentifythe behaviors data on the basis of similarity of datapoints.The purpose of this research work is to identify such drivers and reduce the risk of further accidents.The work identifies the behaviors as bad, normal and aggressive behavior. As the clustering is made on basis crowded data which signifies the similar driving patterns for most of the time in the course of recording, therefore, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) unsupervised learning algorithm was used. The data will be sent to the cloud so that it can be accessed by the authority from any place for further action.ANOVA test is conducted usingIBMSPSS(Statistical Package for the Social Sciences) package to compare and determine the best method to collect data by comparing the means between groups

Online environment for data acquisition from car sensors

Modern cars are equipped with a lot of  electronic devices called Electronic Control Units (ECU). They collect diagnostic data from different car components to control their work, assess the quality of their work and and detect defects. Regardless of the development of on-board computers in cars, only a small amount of information is passed on to the driver. The paper describes how to build a data collecting environment for acquisition real data from a car. The system whose components include an Android based smartphone and Torque PRO application is able to send data via the Internet in real-time to the chosen server. The collected data can be further analysed both on-line and off-line

EXPLORING THREAT-SPECIFIC PRIVACY ASSURANCES IN THE CONTEXT OF CONNECTED VEHICLE APPLICATIONS

Connected vehicles enable a wide range of use cases, often facilitated by smartphone apps and involving extensive processing of driving-related data. Since information about actual driving behavior or even daily routines can be derived from this data, the question of privacy arises. We explore the impact of privacy assurances on driving data sharing concerns. Specifically, we consider two data-intensive cases: usage-based insurance and traffic hazard warning apps. We conducted two experimental comparisons to investigate whether and how privacy-related perceptions about vehicle data sharing can be altered by different types of text-based privacy assurances on fictional app store pages. Our results are largely inconclusive, and we did not find clear evidence that text-based privacy guarantees can significantly alter privacy concerns and download intentions. Our results suggest that general and threat-specific privacy assurance statements likely yield no or only negligible benefits for providers of connected vehicle apps regarding user perceptions

Pasado y presente en el diagnóstico de los motores en los talleres de servicio automotor. Del vacuómetro a los sistemas basados en la nube

In this paper, a comparison between the past and the present in the diagnosis of the engines is made, starting with a brief historical review of the classic diagnosis of engines, common failures in some of its systems, and details of the diagnoses of the alternative engines, as background and contrast with the current modern techniques that are based on the analysis of the information provided by the on-board diagnostic tools and the signals of the sensor of the power train. This work seeks to update and extend the view on the practice of automotive diagnosis. To illustrate the use of those modern procedures, especially for service technicians, some operating parameters of a vehicle's engine are recorded for a test city driving tour, for later graphs and analysis of some operation maps and vehicle dynamics behaviors that can be obtained through the information obtained by the On-Board Diagnostics II system (OBD II).Resumen En el artículo se realizó una comparación entre el pasado y el presente en el diagnóstico de los motores, iniciando con una breve reseña histórica del diagnóstico clásico de motores, fallos comunes en algunos de sus sistemas y detalle de los procedimientos diagnósticos del conjunto móvil del motor. Todo ello, como antecedente y contraposición con las técnicas actuales modernas que se basan en el análisis de la información provista por las herramientas de diagnóstico de bordo y por las señales de los sensores del tren de potencia.  El trabajo realizado busca actualizar y extender la mirada sobre la práctica del diagnóstico automotor, con el propósito de ilustrar la utilización de los procedimientos modernos, en especial para los técnicos de servicio, algunos parámetros de operación del motor de un vehículo son registrados para un recorrido de conducción en ciudad, para posteriormente graficar y analizar algunos mapas de operación y comportamientos de la dinámica de tracción del vehículo que pueden obtenerse a través de la información obtenida por el sistema On Board Diagnostics II system (OBD II)