7 citations · 15 across the 8 of their papers we have counts for
11 papers
A Graph Neural Network Approach for Solving the Ranked Assignment Problem in Multi-Object Tracking
Robin Dehler, Martin Herrmann, Jan Strohbeck +1
Associating measurements with tracks is a crucial step in Multi-Object Tracking (MOT) to guarantee the safety of autonomous vehicles. To manage the exponentially growing number of…
Fast Long-Term Multi-Scenario Prediction for Maneuver Planning at Unsignalized Intersections
Max Bastian Mertens, Jona Ruof, Jan Strohbeck +1
Motion prediction for intelligent vehicles typically focuses on estimating the most probable future evolutions of a traffic scenario. Estimating the gap acceptance, i.e., whether a…
Graph-based Trajectory Prediction with Cooperative Information
Jan Strohbeck, Sebastian Maschke, Max Mertens +1
For automated driving, predicting the future trajectories of other road users in complex traffic situations is a hard problem. Modern neural networks use the past trajectories of t…
Automated Static Camera Calibration with Intelligent Vehicles
Alexander Tsaregorodtsev, Adrian Holzbock, Jan Strohbeck +2
Connected and cooperative driving requires precise calibration of the roadside infrastructure for having a reliable perception system. To solve this requirement in an automated man…
Extrinsic Camera Calibration with Semantic Segmentation
Alexander Tsaregorodtsev, Johannes Müller, Jan Strohbeck +3
Monocular camera sensors are vital to intelligent vehicle operation and automated driving assistance and are also heavily employed in traffic control infrastructure. Calibrating th…
Identification of Threat Regions From a Dynamic Occupancy Grid Map for Situation-Aware Environment Perception
Matti Henning, Jan Strohbeck, Michael Buchholz +1
The advance towards higher levels of automation within the field of automated driving is accompanied by increasing requirements for the operational safety of vehicles. Induced by t…