CitylifeSim.github.io

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The CityLife Simluation A High-Fidelity Pedestrian and Vehicle Simulation with Complex Behaviors

This is the official code repository to accompany the paper “CityLifeSim: A High-Fidelity Pedestrian and Vehicle Simulation with Complex Behaviors”. Here we provide python code for generating and running scenarios using the simulation environment as well as links to the datasets and code used for running our experiments

CityLife is a flexible, high-fidelity simulation that allows users to define complex scenarios with essentially unlimited actors, including both pedestrians and vehicles. This tool allows each vehicle and pedestrian to operate with basic intelligence that governs the \emph{low-level} controls needed to maneuver, avoiding collisions, navigating corners, stopping at traffic lights, etc. The high-level controls for each agent then allows the user to define behaviors in an abstract form controlling their sequence of actions (e.g., hurry to this intersection, then cross the road, turn left at the park, following that wait for a bus at the stop, etc.), their speed changes in different legs of the journey, their stopping distances, their susceptibility to be influenced by their environment and their risk taking behavior.

[Paper] (…) Cheng Yao Wang, Eyal Ofek, Daniel McDuff, Oron Nir, Sai Vemprala, Ashish Kapoor, Mar Gonzalez-Franco (2022) “CityLifeSim: A High-Fidelity Pedestrian and Vehicle Simulation with Complex Behaviors” IEEE ICIR

Video

Dataset

The dataset contains videos (RGB, depth, segmentation frames) of six scenarios. There a total of 128 pedestrians in each video. One of the scenarios is captured from 17 different points of view (i.e., cameras) to simulate static view points, the others are captured from cameras on moving autonomous vehicles. Here are the download link for each scenario:

CityLifeSim Download

You can download the CityLifeSim executable here

Generate Your Own Pedestrians Scenarios:

Run the CityLifeSim

Controlling Environment

Generating Bouding Boxes from Segmentation Images

Multi-object Tracking (MOT) using ByteTrack and MOTChallenge evaluation

The following Colab downloads CityLifeSim into your drive, applies a SoTA MOT and evaluate it. We leverage the work of (Zhang et al., 2021). For more details please refer to the paper or dive into the code…

Contributors

Cheng Yao Wang, Eyal Ofek, Daniel McDuff, Oron Nir, Sai Vemprala, Ashish Kapoor, Mar Gonzalez-Franco Microsoft Research