In this paper, we describe a strategy for training neural networks for object detection in range images obtained from one type of LiDAR sensor using labeled data from a different type of LiDAR sensor. Additionally, an efficient model for object detection in range images for use in self-driving cars is presented. Currently, the highest performing algorithms for object detection from LiDAR measurements are based on neural networks. Training these networks using supervised learning requires large annotated datasets. Therefore, most research using neural networks for object detection from LiDAR point clouds is conducted on a very small number of publicly available datasets. Consequently, only a small number of sensor types are used. We use an existing annotated dataset to train a neural network that can be used with a LiDAR sensor that has a lower resolution than the one used for recording the annotated dataset. This is done by simulating data from the lower resolution LiDAR sensor based on the higher resolution dataset. Furthermore, improvements to models that use LiDAR range images for object detection are presented. The results are validated using both simulated sensor data and data from an actual lower resolution sensor mounted to a research vehicle. It is shown that the model can detect objects from 360 range images in real time.
 In this paper, the authors describe a road boundaries detection system that is based on rage data. A laser scanner mounted on a vehicle is used to measure distances and detect road boundaries. The range data is used to estimate the parameters of a model of the road boundaries. Kalman filtering is used to process successive scans. Test results indicate that the road boundaries can be reliably detected as long as guardrails or posts are available. Abstract This submission is concerned with obstacle detection and tracking for an autonomous, unsupervised vehicle. A multisensor concept is proposed yielding a high level of reliability and security. It includes a variety of different sensor technologies with widely overlapping fields of view between the individual sensors. The major sensors for obstacle detection comprise a self-assessing vision sensor directed forwards and a laser scanner system surveying 360° around the vehicle. Preliminary results indicate the high reliability of the sensor system.