8 papers
Beyond Scalar Rewards: Distributional Reinforcement Learning with Preordered Objectives for Safe and Reliable Autonomous Driving
Ahmed Abouelazm, Jonas Michel, Daniel Bogdoll +2
Autonomous driving involves multiple, often conflicting objectives such as safety, efficiency, and comfort. In reinforcement learning (RL), these objectives are typically combined…
Online Performance Assessment of Multi-Source-Localization for Autonomous Driving Systems Using Subjective Logic
Stefan Orf, Sven Ochs, Marc René Zofka +1
Autonomous driving (AD) relies heavily on high precision localization as a crucial part of all driving related software components. The precise positioning is necessary for the uti…
Functionality Assessment Framework for Autonomous Driving Systems using Subjective Networks
Stefan Orf, Sven Ochs, Valentin Marotta +3
In complex autonomous driving (AD) software systems, the functioning of each system part is crucial for safe operation. By measuring the current functionality or operability of ind…
Label-Free Model Failure Detection for Lidar-based Point Cloud Segmentation
Daniel Bogdoll, Finn Sartoris, Vincent Geppert +2
Autonomous vehicles drive millions of miles on the road each year. Under such circumstances, deployed machine learning models are prone to failure both in seemingly normal situatio…
MUVO: A Multimodal Generative World Model for Autonomous Driving with Geometric Representations
Daniel Bogdoll, Yitian Yang, Tim Joseph +2
World models for autonomous driving have the potential to dramatically improve the reasoning capabilities of today's systems. However, most works focus on camera data, with only a…
From One to the Power of Many: Invariance to Multi-LiDAR Perception from Single-Sensor Datasets
Marc Uecker, J. Marius Zöllner
Recently, LiDAR segmentation methods for autonomous vehicles, powered by deep neural networks, have experienced steep growth in performance on classic benchmarks, such as nuScenes…