Classification of laserscanner measurements at intersection scenarios with automatic parameter optimization

Abstract

Object classification at intersection scenarios is necessary in order to provide a general environment description. Objects are observed using a multilayer laserscanner. Significant features for object classification are identified and their extraction is described. Classification is performed using well-known techniques of statistical learning. Classification results of several neural networks are described and compared with classification performance of support vector machines.