10 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…
Anomaly Detection in Autonomous Driving: A Survey
Daniel Bogdoll, Maximilian Nitsche, J. Marius Zöllner
Nowadays, there are outstanding strides towards a future with autonomous vehicles on our roads. While the perception of autonomous vehicles performs well under closed-set condition…
Compressing Sensor Data for Remote Assistance of Autonomous Vehicles using Deep Generative Models
Daniel Bogdoll, Johannes Jestram, Jonas Rauch +3
In the foreseeable future, autonomous vehicles will require human assistance in situations they can not resolve on their own. In such scenarios, remote assistance from a human can…
Description of Corner Cases in Automated Driving: Goals and Challenges
Daniel Bogdoll, Jasmin Breitenstein, Florian Heidecker +4
Scaling the distribution of automated vehicles requires handling various unexpected and possibly dangerous situations, termed corner cases (CC). Since many modules of automated dri…
KIGLIS: Smart Networks for Smart Cities
Daniel Bogdoll, Patrick Matalla, Christoph Füllner +11
Smart cities will be characterized by a variety of intelligent and networked services, each with specific requirements for the underlying network infrastructure. While smart city a…
Towards Sensor Data Abstraction of Autonomous Vehicle Perception Systems
Hannes Reichert, Lukas Lang, Kevin Rösch +6
Full-stack autonomous driving perception modules usually consist of data-driven models based on multiple sensor modalities. However, these models might be biased to the sensor setu…