Environment analysis of automatic vehicles needs the detection from 3-D point cloud information. This paper addresses this task when only partial scanning data are available. Our method develops the detection capabilities of autonomous vehicles equipped with 3-D range sensors for navigation purposes. In industrial practice, the safety scanners of automated guided vehicles (AGVs) and a localization technology provide an additional possibility to gain 3-D point clouds from planar contour points or low vertical resolution. Based on this data and a suitable evaluation algorithm, intelligence of vehicles can be significantly increased without the need for installation of additional sensors. In this paper, we propose a solution for an obstacle categorization problem for partial point clouds without shape modeling. The approach is tested for a known database, as well as for real-life scenarios. In case of AGVs, real-time run is provided by on-board computers of usual complexity.

LIDAR sensors enable object and free-space detection for intelligent transportation systems and vehicles. This paper proposes a recognition method for LIDARs based on only a few detection planes. This method is useful especially in the case when the angular resolution of the scan is sufficient, but in the vertical direction, the planes are far from each other. We use Fourier descriptor to characterize a scan plane and Convolutional Neural Network for classification. Our method exploits both time varying shape information and contours from multiple scan planes if available. The method performs at least as well as the state of the art algorithms in case of near field, and it also expands the detection range. It was evaluated on tens of thousands of samples from large public datasets and we did separate evaluation for far field objects as well.

For the optimization of daily activity chains a novel method has been elaborated, where flexible demand points were introduced. Some activities are not necessarily fixed temporally and spatially, therefore they can be realized in different times or locations. By using flexible demand points, the method is capable of finding new combinations of activity chains and choosing the optimal set of activities. The optimization algorithm solves the TSP-TW (Traveling Salesman Problem – Time Window) problem with many flexible demand points, which resulted in high complexity and long processing times. Therefore, two extensions were developed to speed up the processes. A POI (Point Of Interest) search algorithm enabled to search demand points in advance and store them in an offline database. Furthermore GA (genetic algorithm) was applied and customized to realize lower optimization times. During the implementation, three different transportation modes were defined: car, public transport, and combined (public transport with car-sharing opportunity). The simulations were performed on arbitrarily chosen test networks using Matlab. Promising test results were obtained for all transportation modes with total travel time reduction of 10–15 percent. The application of the extended optimization method produced shorter activity chains and decreased total travel time for the users.

In order to provide optimal choice of activity locations and travel time reduction, a daily activity chain optimization method has been elaborated, which includes temporal and spatial flexibility of the activities. In the course of the optimization process, possible alternatives are searched using a modified version of the Traveling Salesman Problem with Time Window. The method is extended with a genetic algorithm to provide an optimal order of activities as the minimum of the predefined cost function (i.e. travel time). The multiobjective optimization can efficiently mitigate the travel time of the users. In order to provide some insight regarding the performance of the optimization algorithm, application-oriented simulations were performed on a real-world transportation network using real travel time information. Three transportation modes were considered: car, public transport, and combined (public transport with car-sharing). The simulation results demonstrate that the optimization is able to reduce travel time by 20–30%.

Motion estimation of surrounding objects is indispensable to any mobile machinery. The paper proposes a method to solve the estimation and reconstruction problem of dynamic objects with a mono camera. Using the relative camera motion and detected rigidly moving objects on the image, we estimate their movement up to a scale factor. Utilization priors about their moving direction are used to estimate the transformation, which maps the 3D object from the previous frame to the actual one. Our two-frame method works twice the speed or more as other methods using three frames or more for the estimation, and we do this without any constraints. We evaluate our method on various traffic scenarios of different autonomous driving datasets.