75 citations · 246 across the 12 of their papers we have counts for
12 papers
To New Beginnings: A Survey of Unified Perception in Autonomous Vehicle Software
Loïc Stratil, Felix Fent, Esteban Rivera +1
Autonomous vehicle perception typically relies on modular pipelines that decompose the task into detection, tracking, and prediction. While interpretable, these pipelines suffer fr…
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…
SpaRC: Sparse Radar-Camera Fusion for 3D Object Detection
Philipp Wolters, Johannes Gilg, Torben Teepe +3
In this work, we present SpaRC, a novel Sparse fusion transformer for 3D perception that integrates multi-view image semantics with Radar and Camera point features. The fusion of r…
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…
DPFT: Dual Perspective Fusion Transformer for Camera-Radar-based Object Detection
Felix Fent, Andras Palffy, Holger Caesar
The perception of autonomous vehicles has to be efficient, robust, and cost-effective. However, cameras are not robust against severe weather conditions, lidar sensors are expensiv…
Multi-Modal Sensor Fusion and Object Tracking for Autonomous Racing
Phillip Karle, Felix Fent, Sebastian Huch +2
Reliable detection and tracking of surrounding objects are indispensable for comprehensive motion prediction and planning of autonomous vehicles. Due to the limitations of individu…
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…
RadarGNN: Transformation Invariant Graph Neural Network for Radar-based Perception
Felix Fent, Philipp Bauerschmidt, Markus Lienkamp
A reliable perception has to be robust against challenging environmental conditions. Therefore, recent efforts focused on the use of radar sensors in addition to camera and lidar s…
TUM autonomous motorsport: An autonomous racing software for the Indy Autonomous Challenge
Johannes Betz, Tobias Betz, Felix Fent +16
For decades, motorsport has been an incubator for innovations in the automotive sector and brought forth systems like disk brakes or rearview mirrors. Autonomous racing series such…
Indy Autonomous Challenge -- Autonomous Race Cars at the Handling Limits
Alexander Wischnewski, Maximilian Geisslinger, Johannes Betz +15
Motorsport has always been an enabler for technological advancement, and the same applies to the autonomous driving industry. The team TUM Auton-omous Motorsports will participate…