6 papers · 1 filter
Uncertainty-Aware Diffusion Model for Multimodal Highway Trajectory Prediction via DDIM Sampling
Marion Neumeier, Niklas RoÃberg, Michael Botsch +1
Accurate and uncertainty-aware trajectory prediction remains a core challenge for autonomous driving, driven by complex multi-agent interactions, diverse scene contexts and the inh…
Online Monitoring Framework for Automotive Time Series Data using JEPA Embeddings
Alexander Fertig, Karthikeyan Chandra Sekaran, Lakshman Balasubramanian +1
As autonomous vehicles are rolled out, measures must be taken to ensure their safe operation. In order to supervise a system that is already in operation, monitoring frameworks are…
Machine Learning Architectures for the Estimation of Predicted Occupancy Grids in Road Traffic
Parthasarathy Nadarajan, Michael Botsch, Sebastian Sardina
This paper introduces a novel machine learning architecture for an efficient estimation of the probabilistic space-time representation of complex traffic scenarios. A detailed repr…
Predicted-occupancy grids for vehicle safety applications based on autoencoders and the Random Forest algorithm
Parthasarathy Nadarajan, Michael Botsch, Sebastian Sardina
In this paper, a probabilistic space-time representation of complex traffic scenarios is predicted using machine learning algorithms. Such a representation is significant for all a…
Probability Estimation for Predicted-Occupancy Grids in Vehicle Safety Applications Based on Machine Learning
Parthasarathy Nadarajan, Michael Botsch
This paper presents a method to predict the evolution of a complex traffic scenario with multiple objects. The current state of the scenario is assumed to be known from sensors and…
Assessing the Completeness of Traffic Scenario Categories for Automated Highway Driving Functions via Cluster-based Analysis
Niklas RoÃberg, Marion Neumeier, Sinan Hasirlioglu +2
The ability to operate safely in increasingly complex traffic scenarios is a fundamental requirement for Automated Driving Systems (ADS). Ensuring the safe release of ADS functions…