most citedGeneration of skill-specific maps from graph world models for robotic systems

1 citations · 2 across the 5 of their papers we have counts for

collaborators

5 papers

cs.RO2024

Adaptive Dual-Headway Unicycle Pose Control and Motion Prediction for Optimal Sampling-Based Feedback Motion Planning

Aykut İşleyen, Abhidnya Kadu, René van de Molengraft +1

Safe, smooth, and optimal motion planning for nonholonomically constrained mobile robots and autonomous vehicles is essential for achieving reliable, seamless, and efficient autono…

cs.RO2024

SDS++: Online Situation-Aware Drivable Space Estimation for Automated Driving

Manuel Muñoz Sánchez, Gijs Trots, Robin Smit +4

Autonomous Vehicles (AVs) need an accurate and up-to-date representation of the environment for safe navigation. Traditional methods, which often rely on detailed environmental rep…

cs.RO20241 cited

Generation of skill-specific maps from graph world models for robotic systems

Koen de Vos, Gijs van den Brandt, Jordy Senden +3

With the increase in the availability of Building Information Models (BIM) and (semi-) automatic tools to generate BIM from point clouds, we propose a world model architecture and…

cs.RO20241 cited

Prediction Horizon Requirements for Automated Driving: Optimizing Safety, Comfort, and Efficiency

Manuel Muñoz Sánchez, Chris van der Ploeg, Robin Smit +3

Predicting the movement of other road users is beneficial for improving automated vehicle (AV) performance. However, the relationship between the time horizon associated with these…

cs.AI2023

Robustness Benchmark of Road User Trajectory Prediction Models for Automated Driving

Manuel Muñoz Sánchez, Emilia Silvas, Jos Elfring +1

Accurate and robust trajectory predictions of road users are needed to enable safe automated driving. To do this, machine learning models are often used, which can show erratic beh…