Identifying dynamic objects in urban environments has become a major concern with the advent of autonomous cars in industry and competitions such as the Darpa Urban Challenge. However, detecting and classifying moving obstacles is extremely challenging because of the richness of real-world environments.
To this end, Katz et al. have developed a technique where cars learn to classify dynamic objects. The strategy consists in collecting large amounts of data using a laser scanner while driving around an urban environment. From this data, labels are automatically extracted to describe the dynamic objects (unsupervised learning). Automatic labeling is important because manual labeling is time consuming or might even be impossible if the data set is too large. These labels are then fed to a second supervised classifier that can be used to identify objects instantaneously, even with different sensors such as a camera.
Experiments were conducted with a car equipped with a laser scanner and a camera driving around the University of Sydney campus between 0–40 km/h. Results showed the robust and accurate classification of bikes, pedestrians and cars.