7 research outputs found

    Identifying Driver Behaviour Through Onboard Diagnostic Using CAN Bus Signals

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    Nowadays, traffic accidents occur due to the increasing number of vehicles. In the researches, it was determined that most of the accidents were caused by the driver. Audible and visual warnings of drivers against possible situations in traffic will reduce the risk of errors and accidents. it was observed that the traffic signs were not enough stimuli for the drivers. For this reason, stimulating electronic applications are developed for drivers in Intelligent Transport Systems. The selection of the correct stimulators by measuring the response of the drivers to different situations in different road conditions will provide a more efficient driving. For this purpose, in order to evaluate the driving behavior of the driver in this study, the speed and RPM information received by means of OBD (Onboard Diagnostic) access to the ECU (Electronic Control Unite) data of the vehicle was evaluated instantaneously. Thus driving information provides aggressive driver detection and warns of traffic hazard situations. For this purpose, an experimental system was created by using machine learning algorithms. The vehicle’s speed and RPM data have been used to determine the acceleration of the vehicle and drive. Four different types of drivers have been identified in this designed system. In this way, the driver will be able to detect their own driving. Research will be carried out on how to influence traffic flow by identifying aggressive driver behaviors. It is foreseen that some of the accidents caused by the driver can be prevented. © 2020, Springer Nature Switzerland AG

    Identifying Driver Behavior in Preturning Maneuvers Using In-Vehicle CANbus Signals

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    Our objective in this contribution is to categorize driver behavior in terms of preturning maneuvers. We analyze driving behavior in an urban environment prior to turns using data obtained from the CANbus of an instrumented vehicle during a one-hour driving period for 12 different individuals. CANbus data streams such as vehicle speed, gas pedal pressure, brake pedal pressure, steering wheel angle, and acceleration are collected and analyzed for 5, 10, and 15 seconds of driving prior to each turn. We consider all turns for each driver and extract statistical features from the signals and use cluster analysis to categorize drivers into groups reflecting different driving styles. The results show that using this approach we can effectively cluster drivers into two groups. The results show consistency in the membership within a cluster throughout the different timeframes. We conclude that driver behavior classification from such data streams is possible and we hope in the near future to devise driver descriptors that include additional maneuvers

    Genetic variations of OprD porin protein in imipenem resistant clinical isolates of Pseudomonas aeruginosa in burn patients

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    Background: Drug resistance is one of the important threats in uncontrolled infections by Pseudomonas aeruginosa, an opportunistic nosocomial pathogen, in burn patients. The presence of OprD porin protein in the bacterial cell wall is one of the mechanisms for resistance against hydrophilic drugs in this bacterium. Objective: The aim of this study was to evaluate genetic sequence rearrangements of OprD gene in imipenem resistant clinical isolates of pseudomonas aeruginosa in burn patients. Methods: This cross sectional study was performed in Ghotbeddin Shirazi Hospital from October 2013 to February 2015. A total of 253 wound samples were evaluated for Pseudomonas aeruginosa. All isolates were evaluated using specific sequencing of the target region. Genetic sequence rearrangements were compared with the sensitivity pattern of the isolates to the imipenem. Findings: Pseudomonas aeruginosa was found in 22% of the samples in Shiraz burn center. More than 90% of the isolates were multi drug resistant while only 25% were sensitive to imipenem. More than 80% of the imipenem resistant isolates had rearrangement in the gene associated with OprD protein. Conclusion: With regards to the results, it seems that Pseudomonas aeruginosa, as a prevalent microorganism in burn wounds, has rearrangement in the gene associated with OprD porin protein. This rearrangement may play a role in drug resistance of the Pseudomonas aeruginosa isolates in hospitalized patients