8 accepted papers
Performance in automated driving tasks improves significantly with the incorporation of location-specific prior knowledge. This is because agent behavior usually strongly correlates with location features. A common example is the strong tendency of vehicles to follow their lane, but less obvious int…
In the domain of machine learning, the assumption that training and test data share the same distribution is often violated in real-world scenarios, requiring effective out-of-distribution (OOD) detection. This paper presents a novel OOD detection method that leverages the unique local neuroplastic…
Accurate trajectory prediction is crucial for autonomous driving, yet uncertainty in agent behavior and perception noise makes it inherently challenging. While multi-modal trajectory prediction models generate multiple plausible future paths with associated probabilities, effectively quantifying unc
The prediction of surrounding agents' motion is a key for safe autonomous driving. In this paper, we explore navigation maps as an alternative to the predominant High Definition (HD) maps for learning-based motion prediction. Navigation maps provide topological and geometrical information on road-le…
Understanding traffic scenes requires considering heterogeneous information about dynamic agents and the static infrastructure. In this work we propose SCENE, a methodology to encode diverse traffic scenes in heterogeneous graphs and to reason about these graphs using a heterogeneous Graph Neural Ne
Predicting the motion of surrounding vehicles is essential for autonomous vehicles, as it governs their own motion plan. Current state-of-the-art vehicle prediction models heavily rely on map information. In reality, however, this information is not always available. We therefore propose CRAT-Pred,…
This work presents a model based traversability analysis method which employs a detailed vehicle model to perform real-time path planning in complex environments. The vehicle model represents the vehicle's wheels and chassis, allowing it to accurately predict the vehicles 3D pose, detailed contact i…
Fast and accurate obstacle detection is a crucial component for autonomous robot navigation. It becomes even more important for a shared control vehicle like an electric wheeled walker, since the safety of the vehicle and the user depend on the correct classification of obstacles. This work describe…