Study on an Urban Ramp Driving Cycle Using Self-Organizing Map Neural Network

To tackle the issue of lacking slope information in urban driving cycles used for vehicle performance evaluation, a construction method for urban ramp driving cycle (URDC) is formulated based on self-organizing map (SOM) neural network. The fundamental data regarding vehicles driving on typical roads with urban ramp characteristics and road slopes were collected using the method of average traffic flow, which were then pre-processed and divided into short-range segments; and twenty parameters that can represent the operation characteristics of vehicle driving on urban ramp were selected as the feature parameters of short-range segments. Dimension of the selected feature parameters was then reduced by means of principal component analysis. And a SOM neural network was applied in cluster analysis to classify the short-range segments. An URDC with velocity and slope information were constructed by combination of short-range segments with highly relevant coefficients according to the principle of smooth connection of slope. The constructed URDC was applied in the urban ramp driving performance test of automatic transmission via simulation, which shows that the constructed driving cycle can reflect the driving characteristics of vehicles driving on urban ramps, which can be used as the benchmark driving cycle for performance test of vehicle driving on urban ramp.