5 papers
Towards Safer Autonomous Driving in an Open World: A Dual-Process Approach
Simon Janssen, Michiel Braat, Chris van der Ploeg +2
Before autonomous driving systems can be deployed on public roads, it is vital that these systems comply with safety standards, traffic rules, and social norms. Although neural net…
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…
What Did I Learn? Operational Competence Assessment for AI-Based Trajectory Planners
Michiel Braat, Maren Buermann, Marijke van Weperen +1
Automated driving functions increasingly rely on machine learning for tasks like perception and trajectory planning, requiring large, relevant datasets. The performance of these al…
LiMTR: Time Series Motion Prediction for Diverse Road Users through Multimodal Feature Integration
Camiel Oerlemans, Bram Grooten, Michiel Braat +3
Predicting the behavior of road users accurately is crucial to enable the safe operation of autonomous vehicles in urban or densely populated areas. Therefore, there has been a gro…
Connecting the Dots: Context-Driven Motion Planning Using Symbolic Reasoning
Chris van der Ploeg, Michiel Braat, Beatrice Masini +2
The introduction of highly automated vehicles on the public road may improve safety and comfort, although its success will depend on social acceptance. This requires trajectory pla…