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.