75 citations · 181 across the 6 of their papers we have counts for
6 papers
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…
DeepSTEP -- Deep Learning-Based Spatio-Temporal End-To-End Perception for Autonomous Vehicles
Sebastian Huch, Florian Sauerbeck, Johannes Betz
Autonomous vehicles demand high accuracy and robustness of perception algorithms. To develop efficient and scalable perception algorithms, the maximum information should be extract…
Quantifying the LiDAR Sim-to-Real Domain Shift: A Detailed Investigation Using Object Detectors and Analyzing Point Clouds at Target-Level
Sebastian Huch, Luca Scalerandi, Esteban Rivera +1
LiDAR object detection algorithms based on neural networks for autonomous driving require large amounts of data for training, validation, and testing. As real-world data collection…
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…