Code & Data
Implementations behind my papers, plus an interactive simulator. Everything here is public on GitHub; issues and pull requests are welcome.
Research code
IDM_Bayesian_Calibration
Implements MA-IDM and Bayesian IDM, the dynamic AR+IDM formulation from the Bayesian dynamic regression paper, and multi-vehicle ring-road simulations. Models are built with PyMC and calibrated on the highD naturalistic driving dataset; preprocessed trajectories ship with the repository.
Gaussian_Velocity_Field
Reference implementation of the Gaussian velocity field (GVF), which represents multi-vehicle interaction inside a region of interest around the ego vehicle. Each frame becomes a 13 × 17 × 2 tensor over a meshed ROI, giving a fixed-size description of a scene that varies in the number of surrounding vehicles.
Multivehicle-Interaction
Collects results and media for the general framework of learning multi-vehicle interaction patterns from video, and for the Foundations and Trends review on social interactions in autonomous driving.
Interactive demo
traffic-sim
Cars follow the Intelligent Driver Model on a circular track, and you can switch between three driver-noise models drawn from recent work on Bayesian car-following calibration. Small perturbations grow into stop-and-go waves with no bottleneck present, reproducing the Sugiyama experiment. Inspired by Dr. Martin Treiber's work on stochastic car-following.
Datasets used above are released by their original owners: the highD dataset is distributed by RWTH Aachen and is not redistributed here beyond the preprocessed caches needed to reproduce the published results.