40 citations · 40 across the 10 of their papers we have counts for
4 papers · 1 filter
Deep Learning-Driven State Correction: A Hybrid Architecture for Radar-Based Dynamic Occupancy Grid Mapping
Max Peter Ronecker, Xavier Diaz, Michael Karner +1
This paper introduces a novel hybrid architecture that enhances radar-based Dynamic Occupancy Grid Mapping (DOGM) for autonomous vehicles, integrating deep learning for state-class…
Dynamic Occupancy Grids for Object Detection: A Radar-Centric Approach
Max Peter Ronecker, Markus Schratter, Lukas Kuschnig +1
Dynamic Occupancy Grid Mapping is a technique used to generate a local map of the environment containing both static and dynamic information. Typically, these maps are primarily ge…
Deep Q-Network Based Decision Making for Autonomous Driving
Max Peter Ronecker, Yuan Zhu
Currently decision making is one of the biggest challenges in autonomous driving. This paper introduces a method for safely navigating an autonomous vehicle in highway scenarios by…
Dual-Weight Particle Filter for Radar-Based Dynamic Bayesian Grid Maps
Max Peter Ronecker, Michael Stolz, Daniel Watzenig
Through constant improvements in recent years radar sensors have become a viable alternative to lidar as the main distancing sensor of an autonomous vehicle. Although robust and wi…