Vehicle-Pedestrian Interaction (VPI) in Crossing Scenarios

This is the official repository for the following submission in IV2020: A Multi-State Social Force Based Framework for Vehicle-Pedestrian Interaction in Uncontrolled Pedestrian Crossing Scenarios.

Affiliations:

• Control and Intelligent Transportation Research (CITR) Lab
• Department of Electrical and Computer Engineering
• Center for Automotive Research (CAR)

Update Log:

• 2020-05-15: Released with the final submission of IV2020.

Introduction

Paper Link: A Multi-State Social Force Based Framework for Vehicle-Pedestrian Interaction in Uncontrolled Pedestrian Crossing Scenarios.

The framework focuses on uncontrolled pedestrian crossing scenarios:

Below is the structure of the vehicle-pedestrian interaction framework:

Video demostration of two different vehicle control strategies:

• Model predctive control (MPC)
• Obstacle avoidance control (OAC)

Getting Started

Environment Configuration:

• python3.6+ is required.
• To generate the animation, you system must install FFmpeg. If you haven't done so, please go to https://ffmpeg.org/, download and install FFmpeg in your operating system.
• You need to install the packages specified in requirements.txt file. You can either use pip install -r requirements.txt to automatically install all the required packages or install them manually one by one.

Running the program:

• simulate.py: run this script to start a simulation.
  • Before running, in if __name__ == '__main__': section, change the configuration:
    • Control Method: you can select from different vehicle control method by specifying control_method variable. mpc is model predictive control, oac is obstacle avoidance control, and vkc is velocity keep control. (Details of control strategy see the associated paper.)
  • Initial Vehicle State: change the init_state variable. The first component is the longitudinal position, the second component is the longitudinal velocity (which is also the desired speed).
  • This simulation result will be stored as a .p file in results folder.
• evaluate.py: run this script to start evaluation.
  • Before running, you need to change the global variable data_path to point to the pickle .p file generated by simulate.py script.
  • After evaluation, you will find the results in the results folder.

Questions

Feel free to create an issue or shot me an email: Dongfang Yang ([email protected])