Simulation models show that real-time on-board driver assistance systems that recommend proper following distances can improve fuel economy by approximately 10 percent.
Performance of the proposed system was simulated using the AIMSUN NG microscopic simulation model applied to a simplified vehicle traffic configuration. The system was designed to minimize fuel consumption per unit distance by providing drivers with input that could be used to reduce unnecessary braking and acceleration in various driving situations. For computational simplicity, only the longitudinal motion of a host vehicle, lead vehicle, and a signalized intersection were assessed. The potential impacts of other traffic around the host vehicle or vehicles in front of the lead vehicle were not included in the analysis.
Vehicle performance was measured with and without driver assistance. Baseline data representing performance without assistance were generated using the AIMSUN NG base model (Gipps Method). EDAS features were applied using an application program interface (API) and recommendations for driver assistance were generated using the generalized minimum residual (GMRES) method.
The model assumed that all actions recommended by the system would be executed properly by the driver. Fuel consumption parameters of a subcompact car were used to quantify the average fuel savings and travel economy benefits.
In general, the modeling effort indicated that predictive control systems can improve fuel economy. Average results from different vehicles under different conditions indicated the system could reduce fuel consumption by approximately 10 percent. The authors noted that the system would be more suitable for urban roadways where traffic signals and congestion are more frequent.
Development of Ecological Driving Assist System: Model Predictive Approach in Vehicle Control
Author: M.A.S. Kamal, et.al.
Published By: Paper presented at the 16th ITS World Congress, Stockholm, Sweden.
Source Date: 21-25 September 2009
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Intelligent Transportation Systems > Driver Assistance > Intelligent Speed Control
Intelligent Transportation Systems > Arterial Management > Information Dissemination > In-Vehicle Systems
Intelligent Transportation Systems > Traveler Information > En Route Information > In-Vehicle Systems
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