The advent of autonomous vehicles is one of the most anticipated technological developments of our time. But despite much progress in recent years, highly automated driving systems are still a long way from being able to drive freely on the streets of our cities. A major bottleneck on the road to the many potential benefits of these systems is their ability to share the road safely and efficiently with human-driven cars and pedestrians. This is a very complex issue, in which AI can play a decisive role.
HERALD Lab develops novel AI methods that will enable automated driving systems to interact responsibly and robustly with the people around them. To do this we will combine machine learning, cognitive modelling, control theory and multi-agent simulations. Ultimately, our goal is to lay the groundwork for a new generation of autonomous vehicles that are truly reliable partners for the people around them.
The HERALD Lab is part of the TU Delft AI Labs programme.
The team
Directors
Luca Laurenti
Faculty of ME
Lab director
Arkady Zgonnikov
Faculty of ME
Lab director
PhD candidates
Steven Adams
PhD candidate
Eduardo Figueiredo Mota
PhD candidate
Ashwin George
PhD candidate
Frederik Mathiesen
PhD candidate
Julian Schumann
PhD candidate
Associated faculty
David Abbink
Associated faculty
Jens Kober
Associated faculty
Luciano Siebert
Associated faculty
Manuel Mazo Espinosa
Associated faculty
Simeon Calvert
Associated faculty
Education
Courses
Resources
Master projects
Openings
- Cognitive modeling of traffic interactions
-
Automated discovery of behavior prediction metrics for autonomous vehicles
- In addition to the projects above, we are always open to conversations with MSc students interested in doing a research assignment/graduation project aligned with our research directions
Ongoing
- Formal Control of an Inverted Pendulum
Maarten ten Voorde - The role of attention in drivers’ decision making during merging
Merijn van Niekerk - Adversarial examples for Image Recognition by Automated Vehicles
Yuxing Gao - More MSc projects
by Federico Scari, Jeroen Hagenus, Kristen Johnson, Tom Weinans, and Yongxi Cao
Finished
- Analyzing the relation between the gap acceptance behavior and the response time of drivers' decisions during overtaking, Annemartijne Sevenster
- Towards Personalization of Robot-Assisted Motor Learning Based on User Characteristics, Caspar Boersma
- Modeling metacognition during decision making in traffic, Floor Bontje
- Hybrid AI for pedestrian behavior prediction, Frederik Westerhout
- Significance of Static Backgrounds for Video Object Detection, Godwin Rayan Chandran
- Trustworthy AI Assessment of Quality of movements in Trunk Control Rehabilitation Exercises for children, Joseph Sherman
- Dissipating phantom traffic jams with haptic shared control for longitudinal vehicle motion, Klaas Koerten
- Deep Learning for Abnormal Driving Behaviour Detection, Lanxin Zhang
- Investigating interactive merging behavior in a coupled driving simulator, Loran Bogaart
- Aligning AI with Human Norms, Markus Peschl
- Control of Dynamical Systems via Deep Kernel Learning, Martin Tan
- The role of attention in drivers' decision making during merging, Merijn van Niekerk
- In the driver's mind: cognitive modeling of human overtaking behavior when interacting with oncoming automated vehicles, Samir Mohammad
- Noise Analysis for Biomolecular Signal Differentiators, Shuxin Chen
Partners
