Instructors
Chen-Nee Chuah (Professor)
Office Hours: TBA
Teja Aluru (Lead TA)
Office Hours: TBA

Lectures
Tue/Thu 9-9:50 am, Kemper 3089
except: Jan 15, 17, 24: Kemper 1007 (9-9:50 am); Jan 22: Kemper 1007 (8:30-9:20 am); Feb 26: Kemper 3089 (3:10-4:00 pm)

Labs
Fri 6:10-10pm, Kemper 2151

Teaching Assistants
Adam Jones (aqjones@ucdavis.edu)
Office Hours: Friday 4:10-6:00 pm Kemper 2151
Minh Truong (mstruong@ucdavis.edu)
Office Hours: TBA

Overview
The goal of this class is to teach students the necessary concepts and programming skills to work in the autonomous driving field. To achieve this, students will be given lectures/labs on a variety of content to familiarize themselves with some of the challenges embedded in the self-driving problem. The scope of the content will include Computer Vision, Deep Learning, Sensor Fusion, and Control Systems. Students will be exposed to a lot of the specific challenges that self-driving cars face and will be challenged to find solutions to these issues.

Available Machines
There are 2 machines dedicated to this course. Each machine has an NVIDIA GPU Titan XP. Access to these machines are granted by the Instructors and TAs. Any hardware problems regarding these machines should be reported to TAs via the course Slack.
Machine names:

• atlas.ece.ucdavis.edu
• kronos.ece.ucdavis.edu

• jetson1.ece.ucdavis.edu
• jetson2.ece.ucdavis.edu
• jetson3.ece.ucdavis.edu

Grading

• Lectures/Attendance: 10%
• Labs: 70%
• Final Report 20%

Extra Credit
There will be weekly opportunities for extra credit should students show extra initiative in learning material related to any of the topics covered. Up to an extra 5% will be awarded to students every lab if they add a topic they learned about in addition to the material that was covered during the week.

Communication
Except for submitting assignments on Canvas, all communication about the course should be done through course Slack. Slack is a great medium to ask questions and setup meetings. Aside from updating the course schedule table, all notifications will be made through Slack.

Open-sourced Material
All course material, including videos, lecture slides and assignments are made open-sourced. The EEC193 course allows registered students to secure seats in class, access to machines and feedbacks from teaching faculty. The materials will be published weekly to guarantee registered students have first access to the assignments. After that, auditing students are free to do the assignments.

• All lab reports must be submitted by the deadline specified in the course schedule. No late submission is accepted unless instructor or TAs are notified at least 2 days ahead of the deadline.
• Unless specified otherwise, all lab work must be done individually. Students will be reported to SJA for suspicion of cheating or unauthorized colaborations. That said, high-level discussions are acceptable, and students are encouraged to help each other during lab (clarifying lab's objective, discussing high-level concepts, approaches, ... ). Consult instructors or TAs if it is unclear whether an activity counts as unauthorized collaboration.
• Plagiarism in lab reports and source codes is strictly forbidden. This applied to material found online. Viewing source codes that can aid finishing the lab from the Internet or other classmates can influenced how students approach solving the lab's problem. There are mechanisms in place that check for plagiarism in students' submitted source codes and lab reports. All incidents of suspected plagiarism will be reported to SJA.
• Each student is provided with a lab key. It is the student's responsibility to return the key at the end of the course. Attempts to replicate the key will be reported.
• Students may only use class hardware and machines strictly for class-related purposes. Any other activities are forbidden.

• Student think there is a grading error. Lab coding solutions will be released for each lab after submission deadline.
• Students get one regrade for each lab. A regrade request must be submitted within one week after receiving the grade/feedback. If the students are able to demonstrate in front of the TAs a better understanding of the material, they will receive a grade boost accordingly. The demonstration session can be scheduled outside of TA office hours, either in person or online through Slack.