Self-Driving Cars

As part of the SAE Autodrive Challenge, students in our lab will be working on designing, developing, and testing a self-driving car over the next three years. This will require the students to create solutions to problems such as Localization & Mapping, Pose Estimation, Lane & Road keeping, and Obstacle Detection & Tracking. The team will be receiving a Chevrolet Bolt EV from GM as well as several state-of-the art sensors as part of the competition sponsorship. More information of the team’s progress can found on their website and Facebook page.

 

Related Publications

Building a winning self-driving car in six months
K. Burnett, A. Schimpe, S. Samavi, M. Gridseth, C. W. Liu, Q. Li, Z. Kroeze, and A. P. Schoellig
in Proc. of the IEEE International Conference on Robotics and Automation (ICRA), 2019. Submitted.
[View BibTeX] [View Abstract] [Download PDF] [View Video] [More Information]
The SAE AutoDrive Challenge is a three-year competition to develop a Level 4 autonomous vehicle by 2020. The first set of challenges were held in April of 2018 in Yuma, Arizona. Our team (aUToronto/Zeus) placed first. In this paper, we describe our complete system architecture and specialized algorithms that enabled us to win. We show that it is possible to develop a vehicle with basic autonomy features in just six months relying on simple, robust algorithms. We do not make use of a prior map. Instead, we have developed a multi-sensor visual localization solution. All of our algorithms run in real-time using CPUs only. We also highlight the closed-loop performance of our system in detail in several experiments.

@INPROCEEDINGS{burnett-icra19,
author = {Keenan Burnett and Andreas Schimpe and Sepehr Samavi and Mona Gridseth and Chengzhi Winston Liu and Qiyang Li and Zachary Kroeze and Angela P. Schoellig},
title = {Building a Winning Self-Driving Car in Six Months},
booktitle = {{Proc. of the IEEE International Conference on Robotics and Automation (ICRA)}},
year = {2019},
note = {Submitted},
urlvideo = {http://tiny.cc/zeus-y1},
urllink = {https://arxiv.org/abs/1811.01273},
abstract = {The SAE AutoDrive Challenge is a three-year competition to develop a Level 4 autonomous vehicle by 2020. The first set of challenges were held in April of 2018 in Yuma, Arizona. Our team (aUToronto/Zeus) placed first. In this paper, we describe our complete system architecture and specialized algorithms that enabled us to win. We show that it is possible to develop a vehicle with basic autonomy features in just six months relying on simple, robust algorithms. We do not make use of a prior map. Instead, we have developed a multi-sensor visual localization solution. All of our algorithms run in real-time using CPUs only. We also highlight the closed-loop performance of our system in detail in several experiments.},
}

[DOI] Robust constrained learning-based NMPC enabling reliable mobile robot path tracking
C. J. Ostafew, A. P. Schoellig, and T. D. Barfoot
International Journal of Robotics Research, vol. 35, iss. 13, pp. 1547-1563, 2016.
[View BibTeX] [View Abstract] [Download PDF] [View Video]

This paper presents a Robust Constrained Learning-based Nonlinear Model Predictive Control (RC-LB-NMPC) algorithm for path-tracking in off-road terrain. For mobile robots, constraints may represent solid obstacles or localization limits. As a result, constraint satisfaction is required for safety. Constraint satisfaction is typically guaranteed through the use of accurate, a priori models or robust control. However, accurate models are generally not available for off-road operation. Furthermore, robust controllers are often conservative, since model uncertainty is not updated online. In this work our goal is to use learning to generate low-uncertainty, non-parametric models in situ. Based on these models, the predictive controller computes both linear and angular velocities in real-time, such that the robot drives at or near its capabilities while respecting path and localization constraints. Localization for the controller is provided by an on-board, vision-based mapping and navigation system enabling operation in large-scale, off-road environments. The paper presents experimental results, including over 5 km of travel by a 900 kg skid-steered robot at speeds of up to 2.0 m/s. The result is a robust, learning controller that provides safe, conservative control during initial trials when model uncertainty is high and converges to high-performance, optimal control during later trials when model uncertainty is reduced with experience.

@ARTICLE{ostafew-ijrr16,
author = {Chris J. Ostafew and Angela P. Schoellig and Timothy D. Barfoot},
title = {Robust Constrained Learning-Based {NMPC} Enabling Reliable Mobile Robot Path Tracking},
year = {2016},
journal = {{International Journal of Robotics Research}},
volume = {35},
number = {13},
pages = {1547-1563},
doi = {10.1177/0278364916645661},
url = {http://dx.doi.org/10.1177/0278364916645661},
eprint = {http://dx.doi.org/10.1177/0278364916645661},
urlvideo = {https://youtu.be/3xRNmNv5Efk},
abstract = {This paper presents a Robust Constrained Learning-based Nonlinear Model Predictive Control (RC-LB-NMPC) algorithm for path-tracking in off-road terrain. For mobile robots, constraints may represent solid obstacles or localization limits. As a result, constraint satisfaction is required for safety. Constraint satisfaction is typically guaranteed through the use of accurate, a priori models or robust control. However, accurate models are generally not available for off-road operation. Furthermore, robust controllers are often conservative, since model uncertainty is not updated online. In this work our goal is to use learning to generate low-uncertainty, non-parametric models in situ. Based on these models, the predictive controller computes both linear and angular velocities in real-time, such that the robot drives at or near its capabilities while respecting path and localization constraints. Localization for the controller is provided by an on-board, vision-based mapping and navigation system enabling operation in large-scale, off-road environments. The paper presents experimental results, including over 5 km of travel by a 900 kg skid-steered robot at speeds of up to 2.0 m/s. The result is a robust, learning controller that provides safe, conservative control during initial trials when model uncertainty is high and converges to high-performance, optimal control during later trials when model uncertainty is reduced with experience.},
}

[DOI] Speed daemon: experience-based mobile robot speed scheduling
C. J. Ostafew, A. P. Schoellig, T. D. Barfoot, and J. Collier
in Proc. of the International Conference on Computer and Robot Vision (CRV), 2014, pp. 56-62. Best Robotics Paper Award.
[View BibTeX] [View Abstract] [Download PDF] [View Video]

A time-optimal speed schedule results in a mobile robot driving along a planned path at or near the limits of the robot’s capability. However, deriving models to predict the effect of increased speed can be very difficult. In this paper, we present a speed scheduler that uses previous experience, instead of complex models, to generate time-optimal speed schedules. The algorithm is designed for a vision-based, path-repeating mobile robot and uses experience to ensure reliable localization, low path-tracking errors, and realizable control inputs while maximizing the speed along the path. To our knowledge, this is the first speed scheduler to incorporate experience from previous path traversals in order to address system constraints. The proposed speed scheduler was tested in over 4 km of path traversals in outdoor terrain using a large Ackermann-steered robot travelling between 0.5 m/s and 2.0 m/s. The approach to speed scheduling is shown to generate fast speed schedules while remaining within the limits of the robot’s capability.

@INPROCEEDINGS{ostafew-crv14,
author = {Chris J. Ostafew and Angela P. Schoellig and Timothy D. Barfoot and J. Collier},
title = {Speed daemon: experience-based mobile robot speed scheduling},
booktitle = {{Proc. of the International Conference on Computer and Robot Vision (CRV)}},
pages = {56-62},
year = {2014},
doi = {10.1109/CRV.2014.16},
urlvideo = {https://youtu.be/Pu3_F6k6Fa4?list=PLC12E387419CEAFF2},
abstract = {A time-optimal speed schedule results in a mobile robot driving along a planned path at or near the limits of the robot's capability. However, deriving models to predict the effect of increased speed can be very difficult. In this paper, we present a speed scheduler that uses previous experience, instead of complex models, to generate time-optimal speed schedules. The algorithm is designed for a vision-based, path-repeating mobile robot and uses experience to ensure reliable localization, low path-tracking errors, and realizable control inputs while maximizing the speed along the path. To our knowledge, this is the first speed scheduler to incorporate experience from previous path traversals in order to address system constraints. The proposed speed scheduler was tested in over 4 km of path traversals in outdoor terrain using a large Ackermann-steered robot travelling between 0.5 m/s and 2.0 m/s. The approach to speed scheduling is shown to generate fast speed schedules while remaining within the limits of the robot's capability.},
note = {Best Robotics Paper Award}
}

University of Toronto Institute for Aerospace Studies