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AutoCastSim

AutoCastSim is an end-to-end cooperative perception and collaborative driving simulation framework. It builds on top of the CARLA Simulator, using vehicle-to-vehicle (V2V) communication, to enable sensor sharing and vehicle collaboration. One feature of AutoCastSim is that it includes designed scenarios targeting long-tail events where single-vehicle-based solutions are incapable of consistently making safe decisions.

Overtaking	Unprotected Left-turn	Red-light Violation

Getting Started

Prerequisites

Current setup: Ubuntu 20.04, Cuda 11.0, Python 3.7, PyTorch == 1.7.1, PyTorch Geometrics

Carla 0.9.11

Minkowski Engine CPU version

Others

sudo apt-get install mosquitto libopenblas-dev
sudo apt remove python3-networkx
pip3 install paho-mqtt scipy pygame py-trees==0.8.3 networkx==2.2 xmlschema numpy shapely imageio ray tqdm numba pandas scikit-image scikit-learn opencv-python h5py

Running Demo Scenarios

Run Carla

cd /opt/carla_simulator/bin
./CarlaUE4.sh

Scenario: Overtake

python3 scenario_runner.py \
  --route srunner/data/routes_training_town01_autocast6.xml \
  srunner/data/towns01_traffic_scenarios_autocast6.json  \
  --reloadWorld \
  --bgtraffic 0 \
  --bgtraffic_initspd \
  --agent AVR/autocast_agents/simple_agent.py \
  [--hud --sharing]

Scenario: Unprotected Left Turn

python3 scenario_runner.py \
  --route srunner/data/routes_training_town03_autocast8.xml \
  srunner/data/towns03_traffic_scenarios_autocast8.json   \
  --reloadWorld \
  --bgtraffic 0 \
  --agent AVR/autocast_agents/simple_agent.py \
  [--hud --sharing]

Scenario: Red-light Violation

python3 scenario_runner.py \
  --route srunner/data/routes_training_town03_autocast10.xml \
  srunner/data/towns03_traffic_scenarios_autocast10.json   \
  --reloadWorld \
  --bgtraffic 0 \
  --agent AVR/autocast_agents/simple_agent.py \
  [--hud --sharing]

Note:

• --hud: record the display images and compile into a video at the end
• --sharing: for extended vision

For a full list of config flags, please see config.md. If running remotely, the above commands need X server forwarding. (e.g. use ssh -x)

Running Multi-instance AutoCastSim in Parallel across Multiple GPUs

In order to run multiple AutoCast instances, there are three communication network needed to be separated to avoid interference: 1) Carla <-> Python_Client, 2) TrafficManager, 3) Pub-sub Network. By default they are using port 2000, 8000, 8883. For each instance, these three ports need to be unique.

PORT=2000
TMPORT=$(($PORT+50))
MQTTPORT=$(($PORT+100))

Launch MQTT borker using unique MQTTPORT:

mosquitto -p $MQTTPORT -d

Launch Carla using unique PORT and GPU card:

bash run_carla.sh [GPU_ID] [PORT]

Run one example instance:

python3 scenario_runner.py \
        --route srunner/data/routes_training_town01_autocast6.xml \
        srunner/data/towns01_traffic_scenarios_autocast6.json  \
        --reloadWorld \
        --agent AVR/autocast_agents/simple_agent.py \ 
        --bgtraffic 0 \
        --bgtraffic_initspd \
        --port $PORT \
        --trafficManagerPort $TMPORT \
        --mqttport $MQTTPORT \                
        --commlog --sharing

Repeat above to launch a second instance using a different GPU or the same one if memory footprint permits (~3G each instance).

Training Your Own Agent

Please refer to Coopernaut as an example agent trained using AutoCastSim. We provide a training dataset for behavior cloning based on a simple rule-based agent. You can also collect your own data and train your own agents following these instructions.

Citation

If you want to use AutoCastSim to invent novel collaborative driving models, please refer to Coopernaut, and cite

@inproceedings{coopernaut,
    title = {Coopernaut: End-to-End Driving with Cooperative Perception for Networked Vehicles},
    author = {Jiaxun Cui and Hang Qiu and Dian Chen and Peter Stone and Yuke Zhu},
    booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    series = {CVPR '22},
    year = {2022},
}

If AutoCastSim helps your research in V2V/V2X communication, security, etc., please refer to AutoCast, and cite

@inproceedings{autocast,
  title={AutoCast: Scalable Infrastructure-less Cooperative Perception for Distributed Collaborative Driving},
  author={Hang Qiu and Pohan Huang and Namo Asavisanu and Xiaochen Liu and Konstantinos Psounis and Ramesh Govindan},
  booktitle = {Proceedings of the 20th Annual International Conference on Mobile Systems, Applications, and Services},
  series = {MobiSys '22},
  year={2022},
}