International audienceDrivers typically depict different behavior with respect to various driving events. The modeling of their behavior enables an accurate estimation of fuel consumption during the truck design process and is also helpful for ADAS in order to give relevant advices. In this paper, we propose a learningbased approach to the automatic recognition of driving events,e.g., roundabouts or stops, which impact the driver behavior. We first synthesize and categorize meaningful driving events and then study a set of features potentially sensitive to the driver behavior. These features were experimented on real truck driver data using two machine-learning techniques, i.e., decision tree and linear logic regression, to evaluate their relevance and ability to recognize driving events
International audienceTruck drivers typically display different behaviors when facing various driving events, e.g., approaching a roundabout, and thereby have a major impact both on the fuel consumption and the vehicle speed. Within the context where fuel is increasingly a major cost center for merchandise transport companies, it is important to recognize different driver behaviors in order to be able to simulate them as closely to the real data as possible during the truck development process. In this paper, we introduce, instead of economic driving, the notion of rational driving which seeks to decrease the average fuel consumption while respecting the transport companies’ constraint, i.e., the delivery delay. Moreover, we also propose an indicator, namely rational driving index (RDI), which enables to quantify how good a driver behavior is with respect to the rational driving. We then investigate various driving features contributing to characterize a rational driver behavior, using real driving data collected from 34 different truck drivers on an extra-urban road section particularly representative of travel paths of trucks ensuring regional merchandise distribution. Given the fact that real driving data collected on an open road can differ in terms of environment, e.g.,weather, traffic, we further study, through simulations on a digital representation of a roundabout, the impact of two major driving features, i.e., the use of coasting and crossing speed at roundabouts, with respect to rational driving. The experimental results from both real driving data and simulations show high correlations of these two driving features with respect to RDI and demonstrate that a good rational driver tends to decelerate slowly during braking periods (use of coasting) and have high crossing speed in roundabouts
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