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Extracting Relevant Features That Determine Collision Avoidance in Shared Spaces

Extracting Relevant Features That Determine Collision Avoidance in Shared Spaces

Leitung:  Cheng
Jahr:  2018
Förderung:  DFG Graduiertenkolleg SocialCars

In distinction to classic traffic designs which, in general, separately dedicate road resources to road users by time or space division, an alternative solution—shared space—has been proposed by traffic engineers. Pedestrians, cyclists, and vehicles interact with each other and self-organize to give or take right-of-way. The safety of shared spaces need to be thoroughly investigated, namely, how road users adapt their speed and/or orientation in the interactions with others in their vicinity to avoid collisions.

In order to extract the most relevant features that reflect how a road user adjust his/her motion to avoid potential collisions with others in shared spaces, real-world trajectories will be analysed using statistical and machine learning approaches. For instance, the safe distance may differ significantly across different types of road users. Can we quantify such differences and impacts? Currently, however, user attributes are not yet available in the dataset, which will be incorporated in future work.




  1. time step for each user. A python code running on a pre-processed dataset is already available.
  2. Using statistical tests to quantitatively analyze the impacts of those features mentioned above.
  3. Using probabilistic navigation function (S. Hacohen et al. 2018) or any feasible machine learning approach proposed by the applicant(s) to predict the critical point, where significant adjustments have been made by the ego-user to avoid any potential collision.

Basic terminologies of collision and probabilistic navigation function can be found in the following literature.
Hacohen, S., Shvalb, N. and Shoval, S., 2018. Dynamic model for pedestrian crossing in congested traffic based on probabilistic navigation function. Transportation Research Part C: Emerging Technologies86, pp.78-96.


  • Python and R would be a plus


Basic knowledge of statistics and machine learning


Hao Cheng (Email, Tel. +49 511.762-5215)

Institute of Cartography and Geoinformatics, Appelstraße 9a, 30167 Hannover, room 616