Dual approach for maneuver classification in vehicle environment data

An evaluation of sensor and function performance of an automated driving system as well as behavior analysis in certain situations are important steps on the road to automated driving. The prerequisite is the filtering of relevant situations with an automated and reliable offline classification of maneuvers in large amounts of driving environment data. In this paper, a dual approach for detecting lane changes in laser scanner environment data is presented and evaluated. It uses two classifiers combined, a probabilistic and one based on fuzzy logic, leading to a higher confidence of the results with lower false positive detections. In addition, different probabilistic methods for the former classifier are compared and evaluated.