Big Data-Based Driving Pattern Clustering and Evaluation in Combination with Driving Circumstances 2018-01-1087

Car driver’s behavior and its influence on driving characteristics play an increasing role in the development of modern vehicles, e.g. in view of efficient powertrain control and implementation of driving assistance functions. In addition, knowledge about actual driving style can provide feedback to the driver and support efficient driving or even safety-related measures. Driving patterns are caused not only by the driver, but also influenced by road characteristics, environmental boundary conditions and other traffic participants. Thus, it is necessary to take the driving circumstances into account, when driving patterns are studied. This work proposes a methodology to cluster and evaluate driving patterns under consideration of vehicle-related parameters (e.g. acceleration and jerk) in combination with additional influencing factors, e.g. road style and inclination. Firstly, segmentation of the trip in distance series is performed to generate micro cycles. Based on a set of measured data, a features matrix is developed from the micro cycles for clustering. Secondly, driving circumstances clustering is proceeded by using the K-Means algorithm [29]. Thirdly, driving patterns clustering is accomplished under consideration of each cluster of driving circumstances. The methodology is applied on the measured driving behavior of a fleet of electric cars. In this exemplary use-case, the driving circumstances are clustered into three groups (hilly roads, start/stop and turning cycles, flat roads). The results show that various driving patterns are described differently in the three groups. In this way, the study points out that the definition of driving patterns varies a lot from one type of driving circumstances to another and a driver often changes her/his behavior in the same and in different driving conditions. The discussed methodology can be extended to offer a set of exact and objective labels to be applied for recognition of different patterns in various driving circumstances.