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<!DOCTYPE html>
<html>
<head>
<title>EEC193</title>
<style>
table {
font-family: arial, sans-serif;
border-collapse: collapse;
width: 100%;
}
td, th {
border: 1px solid #dddddd;
text-align: center;
padding: 8px;
}
tr:nth-child(even) {
background-color: #dddddd;
}
</style>
</head>
<body>
<center>
<img src="images/intro.jpg" style="width:1600px;
border-color: #0080FF" border="5">
<h1>EEC193: AI System - Autonomous Car</h1>
<h2>Winter 2019</h2>
</center>
<h1><font color="#0080FF">Course Information</h1></font>
<font size="5">
<ul>
<li>
<p><font color="#0080FF">Instructors</font>
<br>Chen-Nee Chuah (Professor)
<br> Office Hours: TBA
<br> Teja Aluru (Lead TA)
<br>Office Hours: TBA
<br>
</p>
</li>
<li>
<p><font color="#0080FF">Lectures</font>
<br>Tue/Thu 9-9:50 am, Kemper 3089
<br>
<font color="#FF0000">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)</font>
</p>
</li>
<li>
<p><font color="#0080FF">Labs</font>
<br>Fri 6:10-10pm, Kemper 2151
</p>
</li>
<li>
<p><font color="#0080FF">Teaching Assistants</font>
<br>Adam Jones (aqjones@ucdavis.edu)
<br>Office Hours: Friday 4:10-6:00 pm Kemper 2151
<br>Minh Truong (mstruong@ucdavis.edu)
<br>Office Hours: TBA
</p>
</li>
<li>
<p><font color="#0080FF">Overview</font>
<br>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.
</p>
</li>
<li>
<p><font color="#0080FF">Available Machines</font>
<br>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.
<br>Machine names:
<ul>
<li>atlas.ece.ucdavis.edu</li>
<li>kronos.ece.ucdavis.edu</li>
</ul>
<br>There are 3 NVIDIA Jetsons dedicated to the course. These boards are
located in lab. Unless with the consent of instructor or TAs, students
may not remove these boards from the lab.
<br>Machine names:
<ul>
<li>jetson1.ece.ucdavis.edu</li>
<li>jetson2.ece.ucdavis.edu</li>
<li>jetson3.ece.ucdavis.edu</li>
</ul>
</p>
</li>
<li>
<p><font color="#0080FF">Grading</font>
<ul>
<li>Lectures/Attendance: 10%</li>
<li>Labs: 70%</li>
<li>Final Report 20%</li>
</ul>
</p>
</li>
<li>
<p><font color="#0080FF">Extra Credit</font>
<br>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.
</p>
</li>
<li>
<p><font color="#0080FF">Communication</font>
<br>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.
</li>
<li>
<p><font color="#0080FF">Open-sourced Material</font>
<br>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.
</li>
</ul>
</font>
<h1><font color="#0080FF">Course Policies</h1></font>
<font size="5">
<ul>
<li><font color="#0080FF">Labs</font>
<ul>
<li>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.
</li>
<li>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.
</li>
<li>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.
</li>
<li>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.
</li>
<li>Students may only use class hardware and machines strictly for
class-related purposes. Any other activities are forbidden.
</li>
</ul>
</li>
<li><font color="#0080FF">Grading Errors and Post-submission Discussion</font>
<br>The majority of the students' grade comes from their lab work and
reports. The course provides opportunities for students to do well by
giving students a chance to discuss their grades.
It is possible for students to request regrades, if one of the following
conditions applied:
<ul>
<li>Student think there is a grading error. Lab coding solutions will
be released for each lab after submission deadline.
</li>
<li>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.
</li>
</ul>
</li>
</ul>
</font>
<h1><font color="#0080FF">Additional Materials</h1></font>
<font size="5">
While not required, these materials are helpful resources that can aid
students, making sure the course is easier to learn. In truth, students
have to study these resources in order to do well in industry.
<ul>
<li>
<a
href="https://www.youtube.com/watch?v=VyLihutdsPk">Lane Line Detection
Lecture (only first 27 mins; last 11 mins are Q/A)</a>
<li>
<a href="https://www.youtube.com/watch?v=B2qzYCeT9oQ&list=PLpUPoM7Rgzi_7YWn14Va2FODh7LzADBSm">Introduction to SLAM Algorithm</a>
</li>
<li>
<a href="https://www.youtube.com/watch?v=vT1JzLTH4G4&list=PLC1qU-LWwrF64f4QKQT-Vg5Wr4qEE1Zxk">Introduction to Computer vision</a>
</li>
<li>
<a href="https://www.edx.org/course/sensor-fusion-and-non-linear-filtering-for-automotive-systems">Sensor Fusion</a>
</li>
<li>
<a href="https://www.coursera.org/learn/machine-learning">Introduction
to Machine Learning</a>
</li>
</ul>
</font>
<h1><font color="#0080FF">Schedule and Assignments</h1></font>
<font size="5">
A week's worth of material will be published every Tuesday after class. Any
new update to the schedule will be noted in <font
color="#FF0000">red</font>. Any requirements that are no longer needed will
be <strike>crossout</strike>.
<center>
<table>
<tr>
<th>Date</th>
<th>Lecture Video</th>
<th>Slides</th>
<th>Assignments</th>
<th>Deadline</th>
</tr>
<tr>
<th>Week 1: T, Jan 8</th>
<th>_</th>
<th><a href="https://drive.google.com/file/d/1RpbVUq--fKoe8si3yyyTr6IQQJn8xOcu/view?usp=sharing"> Intro to Self-driving Car</a></th>
<th>N/a</th>
<th>N/a</th>
</tr>
<tr>
<th>Week 1: R, Jan 10</th>
<th>_</th>
<th><a href=https://drive.google.com/file/d/1w-h6DUiCPJ1vuJcb5gF49L9gBjQsZj0z/view?usp=sharing> Intro to Computer Vision</a> </th>
<th><a href="https://drive.google.com/file/d/1br_XGF3FRn6xmlNK2DVguc-Cs_xiAik_/view?usp=sharing">Lab 1: Part 1 Intro to Docker</a></th>
<th>11:59 PM, Sun, Jan 13</th>
</tr>
<tr>
<th>Week 1: F, Jan 11</th>
<th>N/a</th>
<th>N/a</th>
<th>
<a href="labs/lab1/README.html">Lab 1: Part 2 Lane Line Detection Instructions</a>
<br>
<a href="https://drive.google.com/open?id=1ktQyIcX1gAEgCDZ6BqGj7YPowJyDx1LA">Lab 1: Part 2 Zip File</a>
</th>
<th>6:09 PM, Sat, Jan 19</th>
</tr>
<tr>
<th>Week 2: T, Jan 15</th>
<th></th>
<th><a href="https://drive.google.com/file/d/1YaQtZv8womQzl0EeXmADd-5sIhBGn3S9/view?usp=sharing">Training Neural Network </a></th>
<th></th>
<th></th>
</tr>
<tr>
<th>Week 2: R, Jan 17</th>
<th></th>
<th><a href = "https://drive.google.com/file/d/1ccE04mwcMWiHTVqigL-zOMP3EQz7O85Y/view?usp=sharing">Camera Calibration</a>
<br>
<a href = "https://drive.google.com/file/d/1lsbFmL4065CIK9oSHORXOQrQolMVhXRj/view?usp=sharing">Intro to Deep Learning Framework</a></th>
<th></th>
<th></th>
</tr>
<tr>
<th>Week 2: F, Jan 18</th>
<th></th>
<th>
<a href = "https://drive.google.com/file/d/1O2bi83oDvmJ6IQ74kVpzRM3iTzxdrdQe/view?usp=sharing"> Intro to CNN and Pytorch
</th>
<th>
<a href="labs/lab2/README.html">Lab 2 MNIST & Vehicle Classification Instructions</a>
<br>
<a href="https://drive.google.com/file/d/1UjMR6An6Ne8HwchKyMWUHwLggggkkW-V/view?usp=sharing">Lab 2: Phase 1 Zip file</a>
<br>
<a href="https://drive.google.com/file/d/1SZ7U6hXcaZvNM7sEaTCa53KfUrq2HYo-/view?usp=sharing">Lab 2: Phase 2 Zip file</a>
</th>
<th>11:59 PM, Fri, Jan 25</th>
</tr>
<tr>
<th>Week 3: T, Jan 22 </th>
<th></th>
<th>
<a href = "https://docs.google.com/presentation/d/1WozmZytVJ518a4H4gFFycMIJFXmBTEPZJ_Rm7epSpO0/edit?usp=sharing"> Convolutional Neural Networks
<br>
</th>
<th></th>
<th></th>
</tr>
<tr>
<th> Week 3: R/F, Jan 24-25 </th>
<th> </th>
<th>
<a href = "https://docs.google.com/presentation/d/12FgCCYn79sZHVCRBUWNxno2MJQn6Q1HdG0D8gASsb9o/edit?usp=sharing"> Lab 3: Overview
</th>
<th>
<a href="labs/lab3/README.html"> Lab 3: Vehicle Detection Instructions</a>
<br>
<a href = "https://drive.google.com/a/ucdavis.edu/file/d/1sESACT_bX-YQdAnye9Y-ccjwQ0h86BxP/view?usp=sharing"> Lab 3: Zip File
</th>
<th>
11:59 PM, Sat, Feb 2
</th>
</tr>
<tr>
<th> Week 4: T, Jan 29 </th>
<th></th>
<th>
<a href="https://docs.google.com/presentation/d/12c_XYbjfMoqA9PFbcBgK-Kxh_zyzA3t2D1ti7TOkHEc/edit?usp=sharing"> Segmentation/Object Localization
</th>
<th>
</th>
<th>
</th>
</tr>
<tr>
<th> Week 4: Th, Jan 31 </th>
<th></th>
<th>
<a href="https://docs.google.com/presentation/d/1aa-pkyWQ0U5luLaTS2COb_vHKpjYclp2Vla_uCJbQu8/edit?usp=sharing"> Object Detection
</th>
<th>
</th>
<th>
</th>
</tr>
<tr>
<th> Week 4: F, Feb 1 </th>
<th></th>
<th>
<a href="https://docs.google.com/presentation/d/1p6s_3prP_pEaY8S8LExfFeQr_iloebsAT1kBgdwOGAI/edit?usp=sharing"> Car Setup </a>
<br>
<a href="https://drive.google.com/file/d/1FyHZ9pJaNJQH0fRzFR50sGWs0d9wQP84/view?usp=sharing"> Communication Protocol Discussion </a>
</th>
<th>
<a href="labs/lab4/README.html"> Lab 4: Car Setup</a>
<br>
<a href="https://drive.google.com/a/ucdavis.edu/file/d/1C5lu8oBxAk-drDg5VItAobCxSmulEwzB/view?usp=sharing"> Lab 4: Zip File</a>
<br>
<a href="https://www.youtube.com/playlist?list=PLeO675amulddfekQVu3z6nIZnpxkg_q9R"> Car Construction Videos
</th>
<th>
11:59 PM, Sat, Feb 16
</tr>
<tr>
<th>Week 5: T, Feb 5</th>
<th></th>
<th>
<a href="https://docs.google.com/presentation/d/1Haq_Eok9_aWtIkONJdNlC_Y4j9E2PddeaAVxVXtaRkE/edit?usp=sharing"> Intro to State Estimation
</th>
<th>
</th>
<th></th>
</tr>
<tr>
<th> Week 5: Th, Feb 7 </th>
<th></th>
<th>
<a href="https://docs.google.com/presentation/d/1AZbF_Fnrcg4miUSPPUgy4VzfLQxjlKMDaAPkMO85-QM/edit?usp=sharing"> Kalman Filtering
</th>
<th></th>
<th></th>
</tr>
<tr>
<th> Week 6: T, Feb 12 </th>
<th></th>
<th>
<a href="https://docs.google.com/presentation/d/13kC6P97K4rcidNP-9X4VKvHiCwjQzt4LjUgzJfa2H4g/edit?usp=sharing"> Intro to SLAM
</th>
<th></th>
<th></th>
</tr>
<tr>
<th> Week 6: F, Feb 15 </th>
<th></th>
<th>
<a href="https://docs.google.com/presentation/d/1vUrr_u_X3UDI3W9C__g6kfStfp6NtSBts4DkqqCobJI/edit?usp=sharing"> Lab 5 Overview
</th>
<th>
<a href="https://drive.google.com/a/ucdavis.edu/file/d/1g1hnNlRzFGqlqaUy6cNoFxmXydAVyFFy/view?usp=sharing"> Lab 5 Phase 1: Zip File
</th>
<th>
11:59 PM, Sat, Mar 2
</th>
</tr>
<tr>
<th> Week 7: T, Feb 19</th>
<th></th>
<th>
<a href="https://docs.google.com/presentation/d/1Bj58LU4FIauTMZwP4H9tK2jNMNReymMbY70jmNQVwiY/edit?usp=sharing"> Path Planning
</th>
<th></th>
<th></th>
</tr>
<tr>
<th> Week 7: Th, Feb 21</th>
<th></th>
<th>
<a href="https://docs.google.com/presentation/d/1KSc9S20VoNd2P9xPbMGBNzbocBITOcrAgZycUNYJGjs/edit?usp=sharing"> Intro to ROS
</th>
<th>
</th>
<th></th>
</tr>
<tr>
<th> Week 7: F, Feb 22 </th>
<th></th>
<th></th>
<th>
<a href="labs/lab5p2/README.html"> Lab 5 Phase 2 Instructions
</th>
<th>
11:59 PM, Sat, Mar 16
</th>
</tr>
<tr>
<th> Week 8: T, Feb 26</th>
<th></th>
<th>
<a
href="https://drive.google.com/open?id=1kdkXHO2vh5x3G_EZ8HqdndA6gcfZ-dL-">
PID Guest Lecture (Hooman Rashtian)
</th>
<th>
</th>
<th></th>
</tr>
<tr>
<th> Week 8: Th, Feb 28</th>
<th></th>
<th>
<a
href="https://drive.google.com/open?id=1TJNH09AZfNgB9dMrqOYumSW1gAZOB54r">
PID/LiPo Safety Guest Lecture Slides (Lance Halsted)</a>
<br>
<a
href="https://drive.google.com/open?id=1dSTTHsEOzbGcveHcPxpyLczqc81p0BI9">
LiPo Battery Safety Guide</a>
</th>
<th>
</th>
<th></th>
</tr>
<tr>
<th>Week 9: F, Mar 8</th>
<th></th>
<th></th>
<th>
<a href="labs/lab6/README.html">Lab 6: PID Controller Instructions</a>
<br>
<a
href="https://drive.google.com/open?id=1F2qlj0yJ6wACEohxZnRpeUjpTYORqf01">Lab
6: Zip File</a>
</th>
<th>11:59 PM, Sat, Mar 16</th>
</tr>
</table>
</center>
</font>
</body>
</html>