53 citations · 192 across the 31 of their papers we have counts for
4 papers · 2 filters
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 Roßberg, 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…