11 citations · 15 across the 18 of their papers we have counts for
18 papers
Stability Control for Real World Testing in Autonomous Racing
Phillip Pitschi, Simon Sagmeister, Frederik Werner +2
Controlling an autonomous vehicle at the limits of handling is a challenging task. Due to external influences, such as road conditions or weather, a vehicle can easily become unsta…
Beyond the Plane: Coupling Planar Vehicle Dynamics with Three-Dimensional Road Geometry
Simon Sagmeister, Phillip Pitschi, Nico Haja +1
Simulation is crucial for developing and testing autonomous driving systems. In particular, the development of localization and control algorithms relies on an accurate vehicle dyn…
Is Energy Guidance All You Need? Training-Free Norm Injection for Driving World Models
Xiyan Su, Frank Diermeyer, Markus Lienkamp
Driving world models built on large video-diffusion backbones generate realistic scenes but are hard to control: enforcing a traffic norm typically means retraining the backbone or…
Drifting in the Future: Stabilizing Path Following Drifting on High-Latency Vehicle Systems
Frederik Werner, Till Heintzenberg, Markus Lienkamp +1
Autonomously controlling and handling a vehicle at and beyond its stability limit is a mathematically and computationally demanding task. Prior demonstrations of automated drifting…
SPARK: Low Latency Single-Camera 3D Pose Estimation for Autonomous Racing using Keypoints
Dominic Ebner, Markus Lienkamp
In autonomous racing, fast detection of other participants' movements is required to plan safe, collision-free trajectories with non-cooperative opponents. LiDAR detection is inher…
FAR-LIO: Enabling High-Speed Autonomy through Fast, Accurate, and Robust LiDAR-Inertial Odometry
Maximilian Leitenstern, Marcel Weinmann, Patrick Haft +3
Robust and accurate odometry estimation is essential in modern robotics. In environments characterized by highly dynamic motion and sensor noise, odometry estimation becomes increa…
Head-to-Head autonomous racing at the limits of handling in the A2RL challenge
Simon Hoffmann, Simon Sagmeister, Tobias Betz +17
Autonomous racing presents a complex challenge involving multi-agent interactions between vehicles operating at the limit of performance and dynamics. As such, it provides a valuab…
Analyzing the Impact of Simulation Fidelity on the Evaluation of Autonomous Driving Motion Control
Simon Sagmeister, Panagiotis Kounatidis, Sven Goblirsch +1
Simulation is crucial in the development of autonomous driving software. In particular, assessing control algorithms requires an accurate vehicle dynamics simulation. However, rece…
RSLCPP -- Deterministic Simulations Using ROS 2
Simon Sagmeister, Marcel Weinmann, Phillip Pitschi +1
Simulation is crucial in real-world robotics, offering safe, scalable, and efficient environments for developing a variety of robotic applications. While the Robot Operating System…
Calibrating the Full Predictive Class Distribution of 3D Object Detectors for Autonomous Driving
Cornelius Schröder, Marius-Raphael Schlüter, Markus Lienkamp
In autonomous systems, precise object detection and uncertainty estimation are critical for self-aware and safe operation. This work addresses confidence calibration for the classi…
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…
VESPA: Towards un(Human)supervised Open-World Pointcloud Labeling for Autonomous Driving
Levente Tempfli, Esteban Rivera, Markus Lienkamp
Data collection for autonomous driving is rapidly accelerating, but manual annotation, especially for 3D labels, remains a major bottleneck due to its high cost and labor intensity…
HeAL3D: Heuristical-enhanced Active Learning for 3D Object Detection
Esteban Rivera, Surya Prabhakaran, Markus Lienkamp
Active Learning has proved to be a relevant approach to perform sample selection for training models for Autonomous Driving. Particularly, previous works on active learning for 3D…
Inconsistency-based Active Learning for LiDAR Object Detection
Esteban Rivera, Loic Stratil, Markus Lienkamp
Deep learning models for object detection in autonomous driving have recently achieved impressive performance gains and are already being deployed in vehicles worldwide. However, c…
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…
Bayesian Optimization-based Tire Parameter and Uncertainty Estimation for Real-World Data
Sven Goblirsch, Benedikt Ruhland, Johannes Betz +1
This work presents a methodology to estimate tire parameters and their uncertainty using a Bayesian optimization approach. The literature mainly considers the estimation of tire pa…
Longitudinal Control for Autonomous Racing with Combustion Engine Vehicles
Phillip Pitschi, Simon Sagmeister, Sven Goblirsch +2
Usually, a controller for path- or trajectory tracking is employed in autonomous driving. Typically, these controllers generate high-level commands like longitudinal acceleration o…
Approaching Current Challenges in Developing a Software Stack for Fully Autonomous Driving
Simon Sagmeister, Simon Hoffmann, Tobias Betz +3
Autonomous driving is a complex undertaking. A common approach is to break down the driving task into individual subtasks through modularization. These sub-modules are usually deve…