ADL Track

Autonomous Driving Lab has a cooperation project with Bolt to evaluate the state of self-driving technology. Bolt has sponsored us to buy a Lexus RX450h vehicle fully equipped with sensors and drive-by-wire technology.

As part of that project we are trying to make our test car to drive in real city traffic. We chose Autoware.AI open-source software as a basis for that experiment, for its use of already established technologies (ROS) and large user community.

In practice we have experienced many difficulties and bugs with Autoware, especially with its planner component called OpenPlanner. Therefore we would like to evaluate other open-source options for self-driving.

In this course you will have hands-on experience with one open-source self-driving software. As part of that, you will also learn some theoretical background in autonomous vehicle design and operation. We have chosen following softwares for the evaluation:

• Autoware.Auto (new version of Autoware)
• Baidu Apollo
• NVidia DriveWorks
• Comma.ai Openpilot

Organization

The course is organized such that we will divide students into teams and each team will take one software (if we have too few students, we can skip some of the softwares). Each of the softwares has accompanied online lectures that each team should watch independently. Every week we have a class meeting dedicated on one topic. Everybody should watch the lecture on the topic at home or preferably together with the team. One student prepares a test on the topic for others. In class we all take the test and after the test there is discussion of right answers.

The online materials to learn from are as follows:

• Autoware.Auto: https://www.autoware.org/awf-course
• Baidu Apollo: https://apollo.auto/devcenter/devcenter.html
• NVidia DriveWorks: https://developer.nvidia.com/drive/training
• Comma.ai Openpilot: https://medium.com/@comma_ai,
  https://medium.com/@chengyao.shen/decoding-comma-ai-openpilot-the-driving-model-a1ad3b4a3612,
  https://arxiv.org/abs/2003.06404

Each team is assigned a supervisor from Autonomous Driving Lab.

Teams

	Autoware.Auto	Baidu Apollo
Team	Dmytro Zabolotnii, Kent Zuntov, Ivan Hladkyi	Romet Aidla, Enlik, Tetiana Shtym, Navid Bamdad Roshan
Supervisor	Maxandre Ogeret	Jan Aare van Gent

We have reserved Delta classroom 2006 on Tuesdays 16-20 for you to work on the project and meet your teammates. The classroom has 24 computers with NVIDIA GTX 2070 GPUs, so that you can run the software and simulator on different computers. The software needs Ubuntu Linux and you need to boot it up from external hard drives we will give you. The LGSVL simulator can be run from Windows and should be already on D: drive.

Tutorials

Here are some tutorials that might come handy during experiments:

Tutorial	Autoware.Auto	Baidu Apollo
Installation	Autoware.Auto with LGSVL Simulator Autoware Setup by Max LGSVL Setup by Max	Running latest Apollo with LGSVL Simulator Apollo Setup by Tanya LGSVL Setup by Tanya Full Tutorial by Navid Full Tutorial by Enlik
ROS2/CyberRT Basics	Lecture 1 Lecture 2 Lecture 3 Visualizing data in ROS2 by Max Create and work with workspaces by Max Working with bags by Max ROS2 Workspace Architecture by Max Build and run ROS1 bridge by Max	TBD
Creating point cloud map	Generating a map by Max	How to record lidar point cloud? by Tanya How to turn that lidar point cloud into a map? by Tanya
Localizing on point cloud map	Localization by Max	How to localize yourself on that map (or using GNSS in Apollo) and record waypoints? by Tanya
Recording and following waypoints	Path Following by Max	How to make the car follow previously recorded waypoints? by Tanya
Lexus car instructions	TBD	Testing Apollo on Lexus Car by Navid Testing Apollo on Lexus Car by Enlik

Tests

Tests can be created using Google Forms. Tests need to be sent to supervisors by Monday morning on the week the test is performed, for quality assurance. Guidelines for creating the tests can be found here.

Schedule

All the class meetings are in Delta room 1022 on Wednesdays 14:15-15:45.

Date	Topic	Student	Autoware lectures	Apollo lectures	NVidia webinars
9.09.2020	Course overview (slides)	Tambet
7.10.2020	Intro to self-driving (test)	Tambet	Lecture 5, Lecture 6	Lecture 1	Clip, Webinar 1, Webinar 2
14.10.2020	Mapping (test)	Kent	Lecture 14	Lecture 2	Webinar
21.10.2020	Localization (test) (slides)	Enlik	Lecture 10	Lecture 3	Clip
28.10.2020	Perception: LIDAR (test)	Navid	Lecture 7	Lecture 4	Clip, Webinar 1, Webinar 2
4.11.2020	Perception: Camera (test)	Tetiana	Lecture 8	Lecture 4	DRIVE Networks, Webinar 1, Webinar 2
11.11.2020	Perception: Radar (test)	Maxandre	Lecture 9		Clip
18.11.2020	Prediction (test)	Romet		Lecture 5	Webinar, Clip 1, Clip 2
24.11.2020	Final date for demoing mapping in simulation
25.11.2020	Simulations (test)	Ivan	Lecture 11	CARLA Talks 2020	DRIVE Constellation
2.12.2020	Planning (test)	Dmytro	Lecture 12	Lecture 6	Webinar
9.12.2020	Control (test)	Jan	Lecture 12	Lecture 7	Webinar, Clip
16.12.2020	Hardware		Lecture 4		Webinar 1, Webinar 2
22.12.2020	First date for parking lot demo
23.12.2020	Infrastructure		Lecture 13		Webinar, Clip
27.01.2020	Second date for parking lot demo

Choose the topic you would like to create a test for here.

Deliverables

To earn 6 ECTS from this course each participant should:

• Create a test on one topic for other students and grade it.
• Get at least 60% of points from all tests created by other students.
• Demonstrate the respective software running on their computer against a simulator.
• Each team has to demonstrate the software running on Lexus car by driving a short pre-defined trajectory on parking lot. In case that turns out to be too complicated, the team should produce written report of their experiments and failures.

Follow-up

There is also a follow-up course planned in the next semester, where we make the teams compete to finish a demo track on the parking lot with the least amount of safety driver interventions.

Contact

Autonomous Driving Lab (room 3095)
Tambet Matiisen
tambet.matiisen@ut.ee