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

      1 , 1 , 1
      Journal of Advanced Transportation
      Hindawi Limited

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          Abstract

          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.

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          Most cited references13

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          Driving speed and the risk of road crashes: a review.

          Driving speed is an important factor in road safety. Speed not only affects the severity of a crash, but is also related to the risk of being involved in a crash. This paper discusses the most important empirical studies into speed and crash rate with an emphasis on the more recent studies. The majority of these studies looked at absolute speed, either at individual vehicle level or at road section level. Respectively, they found evidence for an exponential function and a power function between speed and crash rate. Both types of studies found evidence that crash rate increases faster with an increase in speed on minor roads than on major roads. At a more detailed level, lane width, junction density, and traffic flow were found to interact with the speed-crash rate relation. Other studies looked at speed dispersion and found evidence that this is also an important factor in determining crash rate. Larger differences in speed between vehicles are related to a higher crash rate. Without exception, a vehicle that moved (much) faster than other traffic around it, had a higher crash rate. With regard to the rate of a (much) slower moving vehicle, the evidence is inconclusive.
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            Driver Modeling Based on Driving Behavior and Its Evaluation in Driver Identification

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              Driver Behavior Classification at Intersections and Validation on Large Naturalistic Data Set

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                Author and article information

                Journal
                Journal of Advanced Transportation
                Journal of Advanced Transportation
                Hindawi Limited
                0197-6729
                2042-3195
                November 21 2018
                November 21 2018
                : 2018
                : 1-10
                Affiliations
                [1 ]The University of Western Ontario, London, Ontario, Canada N6A 5B7
                Article
                10.1155/2018/5020648
                abfc8bf1-498d-4068-af15-6a9e0b35b9ea
                © 2018

                http://creativecommons.org/licenses/by/4.0/

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