1 code implementation • 21 Apr 2023 • Alexander Tsaregorodtsev, Adrian Holzbock, Jan Strohbeck, Michael Buchholz, Vasileios Belagiannis
Our method does not require any human interaction with the information recorded by both the infrastructure and the vehicle.
1 code implementation • 8 Aug 2022 • Alexander Tsaregorodtsev, Johannes Müller, Jan Strohbeck, Martin Herrmann, Michael Buchholz, Vasileios Belagiannis
Our approach relies on a coarse initial measurement of the camera pose and builds on lidar sensors mounted on a vehicle with high-precision localization to capture a point cloud of the camera environment.
no code implementations • 29 Oct 2020 • Jan Strohbeck, Vasileios Belagiannis, Johannes Muller, Marcel Schreiber, Martin Herrmann, Daniel Wolf and Michael Buchholz
Automated vehicles need to not only perceive their environment, but also predict the possible future behavior of all detected traffic participants in order to safely navigate in complex scenarios and avoid critical situations, ranging from merging on highways to crossing urban intersections.
no code implementations • 5 Nov 2019 • Johannes Müller, Martin Herrmann, Jan Strohbeck, Vasileios Belagiannis, Michael Buchholz
While classical approaches are sensor-specific and often need calibration targets as well as a widely overlapping field of view (FOV), within this work, a cooperative intelligent vehicle is used as callibration target.