61 citations · 89 across the 4 of their papers we have counts for
4 papers
A Causal Probabilistic Framework for Perception-Informed Closed-Loop Simulation of Autonomous Driving
Zhennan Fei, Rickard Johansson, Mikael Andersson +12
Software-in-the-loop (SIL) simulation is a cornerstone for the validation of modern automotive safety functions. However, many current frameworks utilize ideal sensing, which bypas…
Active Learning of Driving Scenario Trajectories
Sanna Jarl, Linus Aronsson, Sadegh Rahrovani +1
Annotated driving scenario trajectories are crucial for verification and validation of autonomous vehicles. However, annotation of such trajectories based only on explicit rules (i…
A Generic Framework for Clustering Vehicle Motion Trajectories
Fazeleh S. Hoseini, Sadegh Rahrovani, Morteza Haghir Chehreghani
The development of autonomous vehicles requires having access to a large amount of data in the concerning driving scenarios. However, manual annotation of such driving scenarios is…
A Deep Learning Framework for Generation and Analysis of Driving Scenario Trajectories
Andreas Demetriou, Henrik Alfsvåg, Sadegh Rahrovani +1
We propose a unified deep learning framework for the generation and analysis of driving scenario trajectories, and validate its effectiveness in a principled way. To model and gene…