Safe Occlusion Aware Planning

This repository contains the experiment code for our RSS 2021 paper, Safe Occlusion-aware Autonomous Driving via Game-Theoretic Active Perception.

Usage and Limitation

In order to reproduce our results, you need to install modified octomap-python and opendrive2lanelet packages provided in the ThridParty folder.

In addition, you will need Carla to run the simulation. We tested our code using Carla 0.9.11 with Python3.6 on Ubuntu 18.04.

You can run test _xxx.py file to see different examples.

Since we use this code as proof-of-concept purpose for our framework there are several parts needs significant improvement

1. The occluded regions are deteced useing brutal-force method.
2. The safe set are calculated in close-form with simplified dynamics. A more general safe set check step can be implemented by looking up pre-calculated reachable sets through HJI analysis.
3. The A* search in the trajectroy space is slow, and does not provide real-time performance.

We are currently working on an extension of this work.

Citation

@INPROCEEDINGS{Zhan-RSS-21, 
    AUTHOR    = {Zixu Zhang AND Jaime F Fisac}, 
    TITLE     = {{Safe Occlusion-Aware Autonomous Driving via Game-Theoretic Active Perception}}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2021}, 
    ADDRESS   = {Virtual}, 
    MONTH     = {July}, 
    DOI       = {10.15607/RSS.2021.XVII.066} 
}