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.

In this contribution we apply the probability hypothesis density (PHD) filter algorithm for joint tracking of an unknown varying number of targets to automotive environment sensing systems. We use data from a vision and a lidar sensor as well as the vehicle ESP system. After deriving a method to parametrise the algorithm systematically from detection performance statistics we proof the applicability of the method for automotive tracking based on real sensor data