Conventional road tracking approaches rely on direct measurements of the road. Using camera, LiDAR, or radar sensors, they detect lane markings, textures, road boundaries, curbs, or obstacles. However, all require a direct line of sight to the road. In shared spaces, e.g., a busy campus road, a lot of pedestrian groups are occluding the road for all sensors. Hence, we present a novel measurement concept to support road tracking in presence of other road users. In fact, we are taking advantage of them: Where other road users are moving, there might be the road. Therefore, we probabilistically identify dynamic objects based on radar Doppler measurements. Considering the measurement uncertainties, we then build a probability map of road user paths. This is used to derive probable road areas. Integrating this measurement into an existing camera-based road tracking, we evaluate the accuracy of the resulting road estimate using an HD map.

For autonomous driving applications, knowledge of the ego position, orientation, and velocity is a necessary prerequisite for recognizing landmarks and moving targets. We use radar sensors for the determination of these quantities in a radar odometry system. Radar odometry uses the advantage of a direct measurement of the radial speed using radar sensors. Radar sensors are less susceptible to bad weather and lighting conditions than camera and lidar sensors. In addition, radar data is not susceptible to wheel slippage or blocked wheels compared with wheel speed measurements. However, radar data is still susceptible to clutter. In order to achieve a combination of good precision under optimal conditions and good precision under adverse weather conditions, we fuse measurements from radar sensors, wheel speed sensors and the gyrometer. We do not simply combine these measurements according to assumed covariances. Instead, we check the plausibility of the measurements based on their likelihood. Subsequently, we weight the results of the sensor combinations accordingly. The decision about sensor weighting is carried out in a principled, probabilistic manner and adaptively with regard to environmental influences. We validate our approach using real data. Our approach is more precise under adverse conditions than using wheel speed sensors and gyrometers alone. On the other hand, it is more precise under good conditions than using only radar measurements.

The random hypersurface model is well-suited to describe extended target contours. Its applicability is limited only by the mild assumption that the target contour has to be star convex. Gaussian processes provide a sound way to estimate the contour functions, and the ability to model the contour uncertainty in a detailed way at different contour points. However, the association of measurements to target contour points is not optimal in current implementations using Gaussian Processes and the random hypersurface model. In this work, we provide an improved approach compared to the standard approach. The standard approach projects measurements radially onto the predicted contour. Our approach provides expected measurements matching the physical reality of the measurement process more closely. In addition, we perform the association of the whole batch of measurements to the expected contour measurements at once. Compared to a sequential association of individual measurements, this leads to a better association decision.

Central low-level sensor data fusion approaches are getting more popular in advanced driver assistant systems. They allow for the resolution of ambiguities in the retrieval of environmental information on the basis of a large, raw data pool. Hereby, one emerging challenge is the unification of sensor data of different formats and sensor types. A popular intermediate layer of data is given by spatial occupancy grids. The conversion of a discrete list of radar detections, which is a commonly utilized measurement format, is problematic due to the sparse spatial resolution. This work addresses this conversion by interpolating the data spatially using generic sensor model knowledge. Traditional approaches derive occupancy evidence in the vicinity of a detection. In addition, we analyze spatial and kinematic properties derived from Doppler measurements, compute likelihoods that multiple detections are caused by the same object and deduce the space between them accordingly. The incorporation of sensor parameters allows full-and short-range radars to be used generically. In addition, we outline the deduction of free space evidence. The elaborated models and algorithms are evaluated on realworld datasets and discussed w.r.t. their applicability in a subsequent Dempster-Shafer-based sensor data fusion approach.

The radar sensor is an important component in autonomous driving applications. Compared with other sensor types like LiDAR or camera, the radar sensor comes with best weather robustness and ease of integration. Its exclusive capability to measure electromagnetic reflectivity and Doppler-derived radial speeds plays a major role in environment perception applications. However, the processing chain of the radar is more complex and often results in unintuitive measurement effects. In this paper, we explain the technical background of high-resolution radar detections. We give a probabilistic architecture modeling Doppler and Micro-Doppler measurements and their influence on the Monopulse-based azimuth angle, the peak detection and the resulting measurement data. Examples accompany the description of the modeling steps.

For the purpose of extended object tracking in multiple hypothesis tracking algorithms such as the Gaussian mixture probability hypothesis density filter (GMPHD), we develop an approach for the combination of different contour estimates. The developed approach works for tracking algorithms that represent target shapes using contour functions to describe the target shape as the distance of the contour to a reference point over the angle. In a heterogeneous multiple sensor setup, the individual sensors' measurements lead to different extent estimates due to their individual measurement principles. Thus a straight forward use of the extended object state in the traditional merging algorithm either results in unexpected shapes, or tracks cannot be merged due to the differing shape of the objects. Our merging procedure explicitly takes the extent estimates into account by using a merging function. The choice of the merging function provides the means to reach objectives such as a conservative or a generous extent estimate. We evaluate the approach using simulated multisensor data in a GMPHD filter. Compared to the traditional merging method, our approach results in better shape estimates.