LEADER

This is the implementation of the LEADER introduced in this paper LEADER: Learning Attention over Driving Behaviors for Planning under Uncertainty. Please take a look at more driving videos here: https://sites.google.com/view/leader-paper/home

If you find it useful in your research, please cite it using :

@inproceedings{daneshleader,
  title={LEADER: Learning Attention over Driving Behaviors for Planning under Uncertainty},
  author={Danesh, Mohamad H and Cai, Panpan and Hsu, David},
  booktitle={6th Annual Conference on Robot Learning},
  year={2022}
}

Overview

We have used SUMMIT as the simulation of driving in real-world. SUMMIT captures the full complexity of real-world, unregulated, densely-crowded urban environments, such as complex road structures and traffic behaviors, and are thus insufficient for testing or training robust driving algorithms. It is a high-fidelity simulator that facilitates the development and testing of crowd-driving algorithm extending CARLA to support the following additional features:

1. Real-World Maps: generates real-world maps from online open sources (e.g. OpenStreetMap) to provide a virtually unlimited source of complex environments.

2. Unregulated Behaviors: agents may demonstrate variable behavioral types (e.g. demonstrating aggressive or distracted driving) and violate simple rule-based traffic assumptions (e.g. stopping at a stop sign).

3. Dense Traffic: controllable parameter for the density of heterogeneous agents such as pedestrians, buses, bicycles, and motorcycles.

4. Realistic Visuals and Sensors: extending off CARLA there is support for a rich set of sensors such as cameras, Lidar, depth cameras, semantic segmentation etc.

Planner: DESPOT

We used an expert planner in SUMMIT that explicitly reasons about interactions among traffic agents and the uncertainty on human driver intentions and types. The core is a POMDP model conditioned on human hidden states and urban road contexts. The model is solved using an efficient parallel planner, HyP-DESPOT. A detailed description of the model can be found in this paper.

Architecture and Components

The repository structure has the following conceptual architecture:

• summit connector: A python package for communicating with SUMMIT. It publishes ROS topics on state and context information.

• crowd pomdp planner: The POMDP planner package. It receives ROS topics from the Summit_connector package and executes the belief tracking and POMDP planning loop.

• car hyp despot: A static library package that implements the context-based POMDP model and the HyP-DESPOT solver. It exposes planning and belief tracking functions to be called in crowd_pomdp_planner.

• py scripts: Where model definition and initialization, model training and testing, and communication with the compiled simulation and planner happens.

Installation

Pre-requisites

• Ubuntu 18.04
• CUDA 10.0
• Python 3.6
• ROS-Melodic
• catkin_tools: catkin_tools is used for code building in this package instead of the default catkin_make that comes with ROS.

Setup planner

Prepare catkin workspace

cd && mkdir -p catkin_ws/src
cd catkin_ws
catkin config --merge-devel
catkin build

Clone the repo

cd ~/catkin_ws/src
git clone https://github.com/modanesh/LEADER.git
mv LEADER/* .
mv LEADER/.git .
mv LEADER/.gitignore .
rm -r LEADER

Now all ROS packages should be in ~/catkin_ws/src.

Compile the repository

cd ~/catkin_ws
catkin config --merge-devel
catkin build --cmake-args -DCMAKE_BUILD_TYPE=Release

And then run: source ~/catkin_ws/devel/setup.bash

Set up the SUMMIT simulator

Download the SUMMIT simulator. Compile from source or download a stable release. Install the simulator to ~/summit.

Running the code

Launch the planner and start training the agent using the following commands:

cd ~/catkin_ws/src/py_scripts
./run_training.sh

Once the training is done, it will save models in the ~/catkin_ws/src/py_scripts/models. Then, the following command may test the agent:

./run_testing.sh

Getting statistics

cd ~/catkin_ws/src/py_scripts
python statistics.py --folder /path/to/driving_data/joint_pomdp_baseline/ 

Acknowledgement

This code base is inspired by the Context-POMDP implemenetation, accessible here: https://github.com/AdaCompNUS/context-pomdp