Crossroad Detection Using Artificial Neural Networks

An autonomous ground vehicle has to be able to execute several tasks such as: environment perception, obstacle detection, and safe navigation. The road shape provides essential information to localization and navigation. It can also be used to identify reference points in the scenario. Crossroads are usual road shapes in urban environments. The detection of these structures is the main focus of this paper. Whereas cameras are sensible to illumination changes, we developed methods that handle LIDAR (Light Detection And Ranging) sensor data to accomplish this task. In the literature, neural networks have not been widely adopted to crossroad detection. One advantage of neural networks is its capability to deal with noisy data, so the detection can be performed even in the presence of other obstacles as cars and pedestrians. Our approach takes advantage of a road detector system that produces curb data and road surface data. Thus we propose a crossroad detector that is performed by an artificial neural network and LIDAR data. We propose two methods (curb detection and road surface detection) for this task. Classification results obtained by different network topologies have been evaluated and the performance compared with ROC graphs. Experimental tests have been carried out to validate the approaches proposed, obtaining good results when compared to other methods in the literature.