25 citations · 39 across the 13 of their papers we have counts for
13 papers
A2RL V\textsubscript{max}: The A2RL autonomous racing dataset for long-range, high-speed perception and multi-vehicle interaction
Marvin Klemp, Dominic Ebner, Cornelius Schröder +15
In autonomous driving development, a perception dataset is crucial, as it provides fundamental data for training, testing, and validating algorithms for an autonomous vehicle's mul…
Taming Perception Jitter: Uncertainty-Aware LiDAR Object Detection for Reliable Motion Classification
Cornelius Schröder, Žygimantas Marcinkus, Markus Lienkamp
Reliable motion classification is critical for autonomous driving, as false dynamic predictions of static objects can cascade into unnecessary planner interventions. Unstable bound…
FlowCalib: LiDAR-to-Vehicle Miscalibration Detection using Scene Flows
Ilir Tahiraj, Peter Wittal, Markus Lienkamp
Accurate sensor-to-vehicle calibration is essential for safe autonomous driving. Angular misalignments of LiDAR sensors can lead to safety-critical issues during autonomous operati…
CaLiV: LiDAR-to-Vehicle Calibration of Arbitrary Sensor Setups
Ilir Tahiraj, Markus Edinger, Dominik Kulmer +1
In autonomous systems, sensor calibration is essential for safe and efficient navigation in dynamic environments. Accurate calibration is a prerequisite for reliable perception and…
Cal or No Cal? -- Real-Time Miscalibration Detection of LiDAR and Camera Sensors
Ilir Tahiraj, Jeremialie Swadiryus, Felix Fent +1
The goal of extrinsic calibration is the alignment of sensor data to ensure an accurate representation of the surroundings and enable sensor fusion applications. From a safety pers…
FlexCloud: Direct, Modular Georeferencing and Drift-Correction of Point Cloud Maps
Maximilian Leitenstern, Marko Alten, Christian Bolea-Schaser +3
Current software stacks for real-world applications of autonomous driving leverage map information to ensure reliable localization, path planning, and motion prediction. An importa…
OpenLiDARMap: Zero-Drift Point Cloud Mapping using Map Priors
Dominik Kulmer, Maximilian Leitenstern, Marcel Weinmann +1
Accurate localization is a critical component of mobile autonomous systems, especially in Global Navigation Satellite Systems (GNSS)-denied environments where traditional methods f…
Multi-LiCa: A Motion and Targetless Multi LiDAR-to-LiDAR Calibration Framework
Dominik Kulmer, Ilir Tahiraj, Andrii Chumak +1
Today's autonomous vehicles rely on a multitude of sensors to perceive their environment. To improve the perception or create redundancy, the sensor's alignment relative to each ot…
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…
MAN TruckScenes: A multimodal dataset for autonomous trucking in diverse conditions
Felix Fent, Fabian Kuttenreich, Florian Ruch +8
Autonomous trucking is a promising technology that can greatly impact modern logistics and the environment. Ensuring its safety on public roads is one of the main duties that requi…
GMMCalib: Extrinsic Calibration of LiDAR Sensors using GMM-based Joint Registration
Ilir Tahiraj, Felix Fent, Philipp Hafemann +2
State-of-the-art LiDAR calibration frameworks mainly use non-probabilistic registration methods such as Iterative Closest Point (ICP) and its variants. These methods suffer from bi…
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…