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Yiru-Jiao/readme.md

Hi there 👋

I'm a passionate researcher working in autonomous road safety, methodologically in a geospatial time series data-driven way. My research aims at safer traffic for all modes of road users utilising automated technologies, including but not limited to automated vehicles, data-empowered traffic monitoring, and data-driven infrastructure&policy improvement.

I have a mixed education background in Operations Research and Managament Information Systems. My scientific interests are thus generally in collective patterns emerged in individual interactions, of which the curiousity also motivated me to pursue a PhD degree. I'm currently in the last year and approaching the end of my PhD journey at TU Delft. My doctoral research is about road user interactions. Most of my papers published during PhD are openly accessible thanks to TU Delft Library. For every paper, experiment code and instructions are open-sourced here in GitHub. Below is a more detailed list where we

  • measured two-dimensional spacing between road users [code] [pdf],
  • assessed bias-induced explanations for shorter time gaps when human drivers follow automated vehicles [code] [pdf],
  • are proposing (under single-blind review) a consistent and generalisable approach to traffic conflict detection [code] [pdf],
  • are proposing (under double-blind review) structure-preserving contrastive learning for geospatial time serires to learn representations that facilitate downstream tasks.

I enjoy thinking, reading, writing (in both machine and human language), although unfavourably my mind gets overloaded sometimes. I actively post work-relevant updates on LinkedIn and other thoughts on Mastodon.

My digital CV is avaiable at https://yiru-jiao.github.io/cv -- which is very bibliometrically formed for HR's or quantitative eyes. But please, if in any way possible, use a qualitative view to look at me and my research. I believe all researchers in academia would ask the same. We work for a better world, not for producing publications.

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  1. UnifiedConflictDetection UnifiedConflictDetection Public

    This repository offers code to reuse methodology and repeat experiments in the study "A Unified Probabilistic Approach to Traffic Conflict Detection".

    Python 2 1

  2. Reconstruct100CarNDSData Reconstruct100CarNDSData Public

    This repository reconstructs bird's eye view trajectories of vehicles involved in crashes and near-crashes from 100-Car Naturalistic Driving Study (NDS) radar data.

    Python 9 3

  3. Explaining-headway-reduction-of-HVs-following-AVs Explaining-headway-reduction-of-HVs-following-AVs Public

    This repository offers code to repeat experiments and reuse the method in the study "Beyond behaviour change: investigating alternative explanations for shorter time headways when human drivers fol…

    Python 3

  4. DriverSpaceInference DriverSpaceInference Public

    This repository offers code to repeat experiments and reuse the method in the study "Inferring vehicle spacing in urban traffic from trajectory data".

    Jupyter Notebook 5 1

  5. Two-Dimensional-Time-To-Collision Two-Dimensional-Time-To-Collision Public

    This repository allows for fast computation of two-dimensional Time-To-Collision (2D-TTC).

    Python 36 6

  6. DocumentedKnowledgeSharing DocumentedKnowledgeSharing Public

    Open access to the documented knowledge created or summarised by me and my collaborators.