Stage de recherche (physique + IA): traversée d'une foule par un véhicule autonome
Proposition de stage de recherche à l'Institut Lumière Matière, institut public affilié au CNRS et à l'Univ. Lyon 1, pour un niveau Master:
Context. Autonomous cars have started to appear on the streets of several cities across the globe and may become the dominant form of urban transport by 2040 . So far, most research has focused on their interactions with the built environment and other cars in their vicinity, but interactions with pedestrians are also a subject of major concern for public safety, for sparse pedestrians  as well as for denser crowds. Navigation through dense crowds has recently started to attract academic interest [3-4], but these endeavours are faced with the intricacy of modelling the response of such crowds. In particular, reproducing the crossing of a static crowd by an intruder is still a challenge for models of pedestrian dynamics [5-7].
Goal. The research internship is aimed at unlocking this situation by modelling admissible and non-admissible trajectories of a (simplified) autonomous car through a crowd. More precisely, agent-based models will be exploited and further developed to simulate a realistic crowd’s response to the traversing motion of an autonomous vehicle. More precisely, the intern will tackle the following problem : Assuming that the car follows a given trajectory or obeys simple equations of motion, is there a risk of collision with pedestrians in the crowd ? Can one delineate the crowd's responses that are admissible (i.e., those that do not lead to any collision) and those that are not?
To this end, the intern will
make use and further develop agent-based models for pedestrian motion
develop a theoretical method to delineate admissible crowd’s responses, notably by putting forward quantitative indicators to gauge how acceptable a crossing is
contribute to the development of a 3D visualisation tool to illustrate the output of the model.
Profile and skills. We are looking for a motivated and autonomous intern
with a solid background in Physics (Complex Systems and/or Statistical Physics and/or Condensed Matter Physics)
with a good grasp of numerical tools and programming (ideally, C++ and Python)
Previous experience with 3D modelling tools (like the Unity Platform) would certainly be an asset, but is not a requirement in any way.
The intern will be co-supervised by Alexandre NICOLAS (Institut Lumière Matière) and Olivier SIMONIN (Citi-lab) and will be based in one of these two labs. The project takes place in the frame of a joint programme funded by Fédération d’Informatique de Lyon (CROSS).
References  Cugurullo, F., Acheampong, R. A., Gueriau, M., & Dusparic, I. (2020). The transition to autonomous cars, the redesign of cities and the future of urban sustainability. Urban Geography, 1-27  Poibrenski, A., Klusch, M., Vozniak, I., & Müller, C. (2021). Multimodal multi-pedestrian path prediction for autonomous cars. ACM SIGAPP Applied Computing Review, 20(4), 5-17.  Bresson, R., Saraydaryan, J., Dugdale, J., & Spalanzani, A. (2019, June). Socially Compliant Navigation in dense crowds. In 2019 IEEE Intelligent Vehicles Symposium (IV) (pp. 64-69). IEEE;
 Prédhumeau, M., Mancheva, L., Dugdale, J., & Spalanzani, A. (2022). Agent-Based Modeling for Predicting Pedestrian Trajectories Around an Autonomous Vehicle. Journal of Artificial Intelligence Research, 73, 1385-1433.  Nicolas, A., Kuperman, M., Ibañez, S., Bouzat, S., & Appert-Rolland, C. (2019). Mechanical response of dense pedestrian crowds to the crossing of intruders. Scientific reports, 9(1), 1-10.
 Bonnemain, T., Butano, M., Bonnet, T., Echeverría-Huarte, I., Seguin, A., Nicolas, A., ... & Ullmo, D. (2023). Pedestrians in static crowds are not grains, but game players. Physical Review E, 107(2), 024612.  Echeverría-Huarte, I., Roge, A., Simonin, O., & Nicolas, A. (2023). Foundations of continuous agent-based modelling frameworks for pedestrian dynamics and their implications. arXiv preprint arXiv:2309.12798.