5 accepted papers
In this paper, we describe how collision checking for car-like robots can be sped up utilizing system knowledge. Their non-holonomic motion, while being a challenge for motion planning, is utilized here to place discs which are used as an approximation of the robot's shape in a predictive manner. Fo…
We present a novel approach to trajectory generation that enables an autonomous vehicle to accurately follow a lead vehicle tracked by on-board sensors. In contrast to other approaches, we ignore the structure of the environment (e.g., lane markings), focusing purely on following the path driven by…
In this paper, we propose a system for autonomous vehicle following without a line of sight. From monocular camera images, the leading vehicle extracts scene descriptors which it transmits to the following vehicle by means of vehicle-to-vehicle (V2V) communication. The follower is able to recognize…
In this paper, we present a motion planning algorithm for autonomous navigation in highly constrained urban environments. Since common approaches to on-road trajectory planning turned out to be unsuitable for this task, we instead extended an A*-based planner originally designed for navigation in un…
We present a new mapping and navigation system based on human-recognizable landmarks with highly compact representations. Road segments, intersections and salient structures such as houses and trees are detected using vision and LiDAR data. The landmarks are entered in a sparse metrictopological map…