In this work, a deep learning approach has been developed to carry out road detection by fusing LIDAR point clouds and camera images. An unstructured and sparse point cloud is first projected onto the camera image plane and then upsampled to obtain a set of dense 2D images encoding spatial information. Several fully convolutional neural networks (FCNs) are then trained to carry out road detection, either by using data from a single sensor, or by using three fusion strategies: early, late, and the newly proposed cross fusion. Whereas in the former two fusion approaches, the integration of multimodal information is carried out at a predefined depth level, the cross fusion FCN is designed to directly learn from data where to integrate information; this is accomplished by using trainable cross connections between the LIDAR and the camera processing branches.To further highlight the benefits of using a multimodal system for road detection, a data set consisting of visually challenging scenes was extracted from driving sequences of the KITTI raw data set. It was then demonstrated that, as expected, a purely camera-based FCN severely underperforms on this data set. A multimodal system, on the other hand, is still able to provide high accuracy. Finally, the proposed cross fusion FCN was evaluated on the KITTI road benchmark where it achieved excellent performance, with a MaxF score of 96.03%, ranking it among the top-performing approaches.

Urban transportation in the next few decades will shift worldwide toward electrification and automation, with the final aim of increasing energy efficiency and safety for passengers. Such a big change requires strong collaboration and efforts among public administration, research and stakeholders in developing, testing and promoting these technologies in public transportation. Working in this direction, this work provides a review of the impact of the introduction of driverless electric minibuses, for the first and last mile transportation, in public service. More specifically, this paper covers the state of the art in terms of technological background for automation, energy efficiency via electrification and the current state of the legal framework in Europe with a focus on the Baltic Sea Region.

Urban transportation in the next few decades will shift worldwide towards electrification and automation, with the final aim of increasing energy efficiency and safety for passengers. Such a big change requires strong collaboration and efforts among public administration, research and stakeholders in developing, testing and promoting these technologies in the public transportation. Working in this direction, in the present work the impact of the introduction of driverless electric minibuses, for the first and last mile transportation, in the public service is studied. More specifically, this paper covers a state of the art in terms of technological background for automation, energy efficiency via electrification, and the current state of the legal framework in Europe with focus on the Baltic Sea Region.

In this work, a novel learning-based approach has been developed to generate driving paths by integrating LIDAR point clouds, GPS-IMU information, and Google driving directions. The system is based on a fully convolutional neural network that jointly learns to carry out perception and path generation from real-world driving sequences and that is trained using automatically generated training examples. Several combinations of input data were tested in order to assess the performance gain provided by specific information modalities. The fully convolutional neural network trained using all the available sensors together with driving directions achieved the best MaxF score of 88.13% when considering a region of interest of 60×60 meters. By considering a smaller region of interest, the agreement between predicted paths and ground-truth increased to 92.60%. The positive results obtained in this work indicate that the proposed system may help fill the gap between low-level scene parsing and behavior-reflex approaches by generating outputs that are close to vehicle control and at the same time human-interpretable. * Corresponding author: luca.caltagirone@chalmers.se. L. Caltagirone, M. Bellone, and M. Wahde are with the

No abstract