Using an automatic transmission the driver can focus his attention on the pedals and the steering, because the gears are automatically selected according to a prescribed shift pattern. In current day automatic gear shift systems an electronic device controls the shifting process. Some programs use adaptations to consider current road slope, temperature, and special vehicle states. But neither such a system nor the driver takes into account the upcoming road. One can expect that future passenger cars will be equipped with new information systems such as 3D navigation and car-to-car communication systems. These systems, that provide environmental data, open the way to control vehicles with new methods, and assist the driver. In this paper, a new control system based on Predictive Gear Scheduling (PGS) for automatic transmissions using environmental data has been presented. The core of the PGS algorithm is the prediction of the speed trajectory, which gives the run of the vehicle speed the driver will desire in the upcoming section of the road. The speed prediction incorporates an adaptation to the driver's behavior, thus the individual driving style can be taken into account. Modeling the stochastic events in traffic using Markov Chains has been shown to be satisfactory. The functionality has been tested in both simulation and real test runs. The simulation results of the complete system with PGS are presented and compared to the conventional driving and shifting behavior. A reduction of fuel consumption of up to 3.5 percent was presented for a sample route section of 5000 m, depending on the route and the driving style five percent can be achieved. Thereby the vehicle speed is not influenced. The function of vehicle speed prediction based on environmental data is not limited to optimal gear shifting, it can also be used in further future predictive systems.
Vehicle speed prediction for driver assistance systems
Prädiktion der Fahrzeuggeschwindigkeit für Fahrerassistenzsysteme
2004
9 Seiten, 11 Bilder, 3 Quellen
Conference paper
English
Vehicle Speed Prediction for Driver Assistance Systems
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