AutoDRIVE Hunter SE Dataset

Dynamics and Perception Dataset of "Hunter SE" Robot

Note

• For accessing the off-road dataset, switch to the off-road-dataset branch of the repository.
• For accessing the greensward dataset, switch to the greensward-dataset branch of the repository.

Straight Maneuver	Skidpad Maneuver
Fishhook Maneuver	Slalom Maneuver

This repository uses AutoDRIVE Ecosystem to capture data from a 1:5 scale Ackerman-steered vehicle called Hunter SE. The source repository for AutoDRIVE Ecosystem can be found here.

Dataset Structure:

The vehicle dataset comprises the following:

DATA	timestamp	throttle	steering	leftTicks	rightTicks	posX	posY	posZ	roll	pitch	yaw	speed	angX	angY	angZ	accX	accY	accZ
UNIT	yyyy_MM_dd_HH_mm_ss_fff	norm%	rad	count	count	m	m	m	rad	rad	rad	m/s	rad/s	rad/s	rad/s	m/s^2	m/s^2	m/s^2

Vehicle Parameters:

• Wheelbase (m): 0.55

• Track width (m): 0.52

• Center of mass* (m): [x: 0.330, y: 0.000, z: 0.087]

  *Center of mass is measured w.r.t. the center of rear axle.

• Suspension stiffness (N/m): 22700

• Suspension damping (Ns/m): 7000

• Throttle Limit (norm%): 1.0000

• Steering Limit (rad): 0.5236

• Linear Velocity Limit (m/s): 3.5611

• Angular Velocity Limit (rad/s): 2.0708

• Throttle vs. Velocity Mapping:

  

Automated Data Collection:

The open_loop_control.py script makes use of AutoDRIVE Devkit's Python API. The script is capable of selecting a maneuver and its direction, and controlling the vehicle actuators within the prescribed limits in an open-loop setting.

python3 open_loop_control.py --maneuver={straight, skidpad, fishhook, slalom} --direction={cw, ccw} --throttle=[-1, 1] --steering=[0, 0.5236] --throttle_noise=[0, 0.001] --steering_noise=[0, 0.001]

Control Input Variations:

• Throttle Gradations (norm%): 0.2, 0.4, 0.6, 0.8, 1.0 (straight maneuver has additional throttle gradations: 0.1, 0.3, 0.5, 0.7, 0.9)
• Steering Gradations (rad): 0.1047, 0.2094, 0.3142, 0.4189, 0.5236 (straight maneuver does not have any steering gradations)

Maneuver Visualization

Straight Maneuver	Skidpad Maneuver
Fishhook Maneuver	Slalom Maneuver

Data Visualization

Single Maneuver Data Visualization

Straight Maneuver	Skidpad Maneuver
Fishhook Maneuver	Slalom Maneuver

Collective Maneuver Data Visualization

Straight Maneuver	Skidpad Maneuver
Fishhook Maneuver	Slalom Maneuver
All Maneuvers

Citation

We encourage you to read and cite the following papers if you use any part of this dataset for your research:

AutoDRIVE: A Comprehensive, Flexible and Integrated Digital Twin Ecosystem for Enhancing Autonomous Driving Research and Education

@article{AutoDRIVE-Ecosystem-2023,
author = {Samak, Tanmay and Samak, Chinmay and Kandhasamy, Sivanathan and Krovi, Venkat and Xie, Ming},
title = {AutoDRIVE: A Comprehensive, Flexible and Integrated Digital Twin Ecosystem for Autonomous Driving Research & Education},
journal = {Robotics},
volume = {12},
year = {2023},
number = {3},
article-number = {77},
url = {https://www.mdpi.com/2218-6581/12/3/77},
issn = {2218-6581},
doi = {10.3390/robotics12030077}
}

This work has been published in MDPI Robotics. The open-access publication can be found on MDPI.

AutoDRIVE Simulator: A Simulator for Scaled Autonomous Vehicle Research and Education

@inproceedings{AutoDRIVE-Simulator-2021,
author = {Samak, Tanmay Vilas and Samak, Chinmay Vilas and Xie, Ming},
title = {AutoDRIVE Simulator: A Simulator for Scaled Autonomous Vehicle Research and Education},
year = {2021},
isbn = {9781450390453},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3483845.3483846},
doi = {10.1145/3483845.3483846},
booktitle = {2021 2nd International Conference on Control, Robotics and Intelligent System},
pages = {1–5},
numpages = {5},
location = {Qingdao, China},
series = {CCRIS'21}
}

This work has been published in 2021 International Conference on Control, Robotics and Intelligent System (CCRIS). The publication can be found on ACM Digital Library.