Christian Raubitschek

Predictive driving strategies under urban conditions for reducing fuel consumption based on vehicle environment information

This brief deals with the improvement of a vehicles pass-through of predictively known urban driving situations concerning its fuel consumption. Todays technology enables the prediction of information about traffic events. This information can be used to identify efficient driving strategies. The main aim is to reduce the dynamics in the velocity profiles of driving situations and with it the corresponding fuel consumption in urban traffic. An algorithm has been built to calculate fuel consumption optimized driving trajectories. Input parameters are temporal and spatial depending constraints of the driving situation as well as other restrictions like a speed-limit. The main objective of the function was to enable a situation adaptive reaction to every predictively known forthcoming traffic event. Thus an optimized driving trajectory can be steadily calculated for the next route section of the vehicle provided that predictive information about the traffic events are available. The higher the availability of information the better an optimization of the driving strategies will be possible. Fuel characteristics and other energetically relevant data for a real-world vehicle have been created by detailed simulation to evaluate the fuel consumption of the driving strategies. For demonstration purposes the common driving situation ”traffic light” was chosen. The fuel consumption calculated for the predictive driving strategies is compared to the consumption of a simulated average driver without predictive information. The calculated potentials have been verified by measuring the fuel consumption of an experimental vehicle for the simulated driving strategies.

ElisaTM - Car to infrastructure communication in the field

Car to infrastructure (C2I) communication as an aspect of Intelligent Transportation Systems (ITS) is a topic that is currently under wide research. Most works however deal with theoretical analysis, simulative evaluation or closed testbeds. There are only a few publications describing real world application of C2I on real world streets with real world traffic. In this paper we present the results of initial tests and measurements performed in the testbed ElisaTM (Efficient Light Signal Adaptation Testbed Munich). The testbed consists of four intersections in a mostly residential area in Munich, Germany. The results include an analysis of data availability and data quality under different traffic conditions.

Traffic Signal Information in a Real Residential Area

In this study we share an evaluation of measurements performed in a traffic light communication test bed in real life traffic. We describe our hardware and software architecture and present our measurement methods. As a basis for the evaluation, we selected two use cases: Micropause Infotainment and Fuel Efficient Traffic Light Approach. We develop, train and evaluate a model for estimating micropauses at traffic lights and determine the value of predictive traffic light information in terms of fuel efficiency.