Difference between revisions of "Introduction to Autonomous Vehicles"
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class is divided in groups of 4 students. Each group has to develop a project at the end of the class. for projects can be found :
Revision as of 08:14, 13 November 2018
Autonomous vehicles, such as the ones being developed by Google, Uber, and Apple, as well as semi-autonomous cars on the market by Tesla, are generating exciting engineering challenges and catching significant public attention. As one of the goals of the Jacobs School of Engineering at the University of California San Diego is to provide students with up-to-date relevant knowledge, MAE/ECE 148 (Introduction to Autonomous Vehicles) will incorporate engineering theory and good practices, industry relevant lectures, and practical application with the development of 1/10 scale autonomous cars that must perform on a simulated city track.
Thank you for your interest in this class. The Fall 2018 section is currently at capacity, but stay tuned because we plan to offer it again in future quarters. See the upcoming Winter Session.
Meetings Fall 2018
- Tuesday from 17:30-18:50,
- Thursday from 17:30-18:50
At the ECE Makerspace and EBU II 333.
We are currently at capacity for the Fall 2018 session. Please apply for the Winter 2019 session.
This course will introduce students to the fundamentals of Autonomous Vehicles using an accelerated and engaging engineering curriculum that leverages the educational benefits of robotics competitions. Students will work in small diverse teams, learning the best engineering methods and practices similar to what they will see in their professional career. Skills to be learned include project management, adhering to a budget and business planning, working within time constraints, designing to specifications, demonstrating performance, and delivering a well documented project that others can build on.
More specifically, students will be introduced and do development in the following areas:
- Computer Vision
- Algorithms for Navigation
- On-vehicle vs. off-vehicle computation
- Computer learning systems such as Neural Networks
- Locomotion Systems
- Vehicle Steering and Traction Control
- Dead Reckoning
- Sensor Fusion
- Global Positioning Based (GPS) Auto-Pilot limitations
- Power Management
Teams of students will be required to build an autonomous car using 1/10 scale R/C based cars that must meet a minimum pre-stabilized performance during a competition on an outdoor scaled track simulating city streets. Through this process of preparing for robotics competitions, students working in small diverse teams will experience a multidisciplinary engineering course that integrates theory and hands-on experience.
Enrollment is based upon instructor approval and limited to 28 students.
Students are encouraged to send an email the instructors with a short introduction and summarizing the reasons why they are interested in the course.
Detailed description on the topics covered will be made available here:
Instruction will be complemented by the following tutorial sections:
Integral to the class is a team project in which the class is divided in groups of 4 students. Each group has to develop a project that incorporates a new feature to the car, for example a new sensor, new algorithm, new motor, etc, develop the project and present it at the end of the class. Past class projects and ideas for new projects can be found in our project's page:
This course requires students to create projects for the development of autonomous vehicles.
Students are encouraged to work in small multidisciplinary teams but have to produce an individual plan for development during the quarter. Projects can cover engineering theory, design and hands-on experience.
Students will work in small diverse teams in which they will learn about best engineering methods and practices. Management skills to be learned include project management, adhering to a budget and business planning, working within time constraints, designing to specifications, demonstrating performance, and delivering a well documented project that others can build on.
Teams are expected to report and present their findings and progress throughout the quarter and produce a final report. The final report shall contain enough detail to provide future students with the knowledge and insights developed throughout the quarter.