activity
20172025
most citedSearch-based optimal motion planning for automated driving

86 citations · 102 across the 4 of their papers we have counts for

collaborators

5 papers

eess.SY2025

A nonlinear dead-time compensation method for path tracking control

Karin Festl, Michael Stolz

In the realm of autonomous vehicle technologies and advanced driver assistance systems, precise and reliable path tracking controllers are vital for safe and efficient navigation.…

eess.SY2023

Multi-objective path tracking control for car-like vehicles with differentially bounded n-smooth output

Karin Festl, Michael Stolz, Daniel Watzenig

When designing path tracking controllers for car-like vehicles, two main aspects are the tracking performance and the characteristics of the actuation signal. Our work bases on an…

cs.RO2023

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…

cs.RO2018★ 86 cited

Search-based optimal motion planning for automated driving

Zlatan Ajanovic, Bakir Lacevic, Barys Shyrokau +2

This paper presents a framework for fast and robust motion planning designed to facilitate automated driving. The framework allows for real-time computation even for horizons of se…

math.OC2017★ 16 cited

A novel model-based heuristic for energy optimal motion planning for automated driving

Zlatan Ajanovic, Michael Stolz, Martin Horn

Predictive motion planning is the key to achieve energy-efficient driving, which is one of the main benefits of automated driving. Researchers have been studying the planning of ve…