Cloud-based Driving Data Analysis for Driving Experience Routing
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Cloud-based Driving Data Analysis for Driving Experience Routing Vehicle development is affected by the cross-domain entry of digital services. These informing, assisting and automating systems influence the driving experience more and more. Thus, future mobility concepts as well as manu facturers and models will differ by digital services. With the Porsche C onnected Experience, a research cooperation between Porsche and the University of Technology D resden was carried out to develop a vehicle-related online service supporting the driver by driving experience routing.
AUTHOR
Dipl.-Ing. Falk Salzmann is Doctoral Candidate at the Chair of Vehicle Mechatronics at the Dresden Institute for Automotive Technology (IAD) of the University of Technology Dresden (Germany).
© [M] AA+W | stock.adobe.com | Falk Salzmann
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1 MOTIVATION 2 OB JECTIFICATION OF DRIVING E XPERIENCE 3 NATUR ALISTIC DRIVING STUDY 4 C ONSTRUCTION OF DRIVING E XPERIENCE ROUTES 5 ANALYSIS OF C ONSTRUCTED ROUTES 6 SUMMARY AND OUTLO OK
In the research project carried out at the Chair of Vehicle Mechatronics of the Institute of Automotive Technology Dresden (IAD), vehicle sensor data were used to calculate a local quality measure for traveled routes. For this purpose, a previously derived heuristic model for real-time evaluation of sporty driving was used. Its output can be aggregated by observing the signals of many vehicles. In a result, routes of particular quality can be selected statistically and assigned to a corresponding use, for example in a navigation service. 2 OBJECTIFICATION OF DRIVING EXPERIENCE
1 MOTIVATION
In addition to safety, comfort, efficiency and driving time, the driving experience is becoming increasingly important for the development of mobility services. In general, the recognition and prediction of driving sentience for the adaptation of vehicle systems is an ongoing subject of research. Driving experience can be understood as the measure of sensationalism expressed by activity and dynamics in a sporty driving event. Besides temporary parameters, such as the visibility or the volume of traffic, there are many aspects of unknown priority for qualifying driving experience. One of specific interest is the location of pertinent routes. To distinguish them, the use and driving style seen on certain routes can be mapped.
A model for calculating the quality of the driving experience was derived based on traffic-psychological considerations using vehicle signals [1]. This model, FIGURE 1, uses both parameters of vehicle dynamics and driver activity, which are checked with regard to statistical and empirical boundary conditions. The sum of resulting check signals is then processed by means of a low-pass filter and a two-point controller. The result is an inert signal, similar to the human experience. The parameters used in the signal model are vehicle-specific and taken from the empirical studies documented in [2, 3, 4]. In addition, there are analog approaches for examining comfort limits in [5, 6] as well as in [7, 8]. T
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