25 citations · 39 across the 5 of their papers we have counts for
5 papers
V3LMA: Visual 3D-enhanced Language Model for Autonomous Driving
Jannik Lübberstedt, Esteban Rivera, Nico Uhlemann +1
Large Vision Language Models (LVLMs) have shown strong capabilities in understanding and analyzing visual scenes across various domains. However, in the context of autonomous drivi…
Scenario Understanding of Traffic Scenes Through Large Visual Language Models
Esteban Rivera, Jannik Lübberstedt, Nico Uhlemann +1
Deep learning models for autonomous driving, encompassing perception, planning, and control, depend on vast datasets to achieve their high performance. However, their generalizatio…
Snapshot: Towards Application-centered Models for Pedestrian Trajectory Prediction in Urban Traffic Environments
Nico Uhlemann, Yipeng Zhou, Tobias Simeon Mohr +1
This paper explores pedestrian trajectory prediction in urban traffic while focusing on both model accuracy and real-world applicability. While promising approaches exist, they oft…
EDGAR: An Autonomous Driving Research Platform -- From Feature Development to Real-World Application
Phillip Karle, Tobias Betz, Marcin Bosk +31
While current research and development of autonomous driving primarily focuses on developing new features and algorithms, the transfer from isolated software components into an ent…
Evaluating Pedestrian Trajectory Prediction Methods with Respect to Autonomous Driving
Nico Uhlemann, Felix Fent, Markus Lienkamp
In this paper, we assess the state of the art in pedestrian trajectory prediction within the context of generating single trajectories, a critical aspect aligning with the requirem…