7 citations · 15 across the 8 of their papers we have counts for
6 papers · 1 filter
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…
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…
Motion Planning for Connected Automated Vehicles at Occluded Intersections With Infrastructure Sensors
Johannes Müller, Jan Strohbeck, Martin Herrmann +1
Motion planning at urban intersections that accounts for the situation context, handles occlusions, and deals with measurement and prediction uncertainty is a major challenge on th…
LMB Filter Based Tracking Allowing for Multiple Hypotheses in Object Reference Point Association*
Martin Herrmann, Aldi Piroli, Jan Strohbeck +2
Autonomous vehicles need precise knowledge on dynamic objects in their surroundings. Especially in urban areas with many objects and possible occlusions, an infrastructure system b…