Photo by Anton Darius | @theSollers on Unsplash

How To Get An Autonomous Driving Internship — With Little Previous Experience

By Peggy (Yuchun) Wang

Autonomous Driving has the potential to hugely transform people’s lives, and many people are excited to make an impact by contributing to the technical development of self-driving cars. However, to my knowledge, there have not yet been any articles published on Medium which help students navigate the field and get an internship with little to no experience. Here are some tips (that I wish I had known before looking last year) on how to get an autonomous driving internship, especially if you don’t have any previous experience in the industry.

Personally, I got started with autonomous driving because it is an interesting technological problem with profound social implications. I’m very interested in the technological development at the forefront of what is currently one of the most exciting innovations of artificial intelligence and robotics, as well as thinking about the ethical and societal issues caused by automation.

Do Your Research

First, try to find out if autonomous driving is actually something you’re interested in. A quick Google search of the recent news surrounding self-driving cars and the top companies and startups working in the industry is a good first step. Here’s a good introductory news article by Wired.

Coffee Chats

If the problems in the field excite you, reach out to people who work in the industry for coffee chats. I found that reaching out to engineers and managers on LinkedIn and cold emailing them was a good way to learn more about the problems facing the industry, and build my network. Reaching out through Stanford’s Alumni networks (such as Handshake or BEAM) is also a good way to get to know alumni in the field. Pro tip — offer to get them coffee in the first email or message.

Learn About the Technology Stack

You will get much more out of your coffee chats and talking with people in the industry if you learn about the technology stack (the architecture of the system and how different components are connected) beforehand, and how to gauge where different companies are in the race. It will definitely be worth your while to read up on LiDARs, sensor fusion, computer vision, mapping, planning, and deep learning methods. Understanding the how the software stack (perception, planning, control, and mapping) works will also help a lot. David Silver’s Medium blog post and the picture below give a quick introduction. The original DARPA Urban Challenge papers from the Carnegie Mellon and Stanford teams would be a good overview of the stack as well.

Technology Stack of the Udacity Self-Driving Car from David Silver’s Medium blog

Comparing Companies

SAE Levels

You may hear people talking about Level 4 and Level 5 self-driving in the industry. This refers to the Society of Automotive Engineers’ (SAE) designations for level of automation in an autonomous car, going from Level 0 (no automation), to partial automation, to Level 5 (full automation). The graphic below does a good job of showing the levels and the definition.

The SAE’s Five Levels, photo by Forbes

When looking at different companies, it is a good idea to think about where each company is aiming in the level of vehicle autonomy (Level 3, Level 4, or Level 5) and what approach they are taking to get there (going from level to level, or straight into development of Level 5).

Other Considerations

Other considerations for comparing different companies include the parts of the autonomy stack they are mostly working on. For example, are they making the full stack software and hardware, software only, the hardware only, a specific part of perception, mapping, and/or planning? It’s also good to look into their business model and how they plan to make a profit based off of autonomous vehicles. Is it by selling their hardware or software, or is it by giving rides to passengers? If you get a chance to have a coffee chat with people working in the industry, you can also ask about the company culture, team size, and intern projects.

Learn More and Try to Get Experience

It’ll be good to have some experience before going into the internship, if for no other reason than to get more out of your internship. The best way would, of course, be getting an internship in the area in the first place. You can also start by taking classes and doing projects outside of class. Some classes at Stanford I would recommend taking are (AA 274: Principles of Robotic Autonomy, CS 238: Decision Making Under Uncertainty, CS 221: Artificial Intelligence, CS 229: Machine Learning, and CS 231N: Convolutional Neural Networks for Visual Recognition). Doing research in a relevant lab (SISL, ASL, DDL) on campus or joining an engineering club (SSI, Robotics Club, SUAVE, Solar Car) are great ways of getting experience as well. If you have free time over break, you can try taking some free introductory self-driving car online courses from Udacity, Coursera, and MIT. Another additional way to gain experience is going to local Hackathons.

Companies That Are Hiring

Companies that have autonomous vehicle internship programs include Waymo, Lyft Level 5, Uber ATG, Tesla, Aurora, ArgoAI, NIO, Zoox, and Aptiv. In addition, a lot of the big automakers also have autonomous vehicle divisions, such as Ford, TRI/Toyota, Nissan Research, Bosch, and GM.

If that isn’t enough, you could look at reaching out to any of the other 263 companies working on autonomous vehicles here, or look for other companies and startups.

Preparing for the Interview

There are many blog posts and articles written about how to prepare for a coding interview, so I won’t go into detail here. However, some resources I used were the Cracking the Coding Interview book, LeetCode, and HackerRank. I would prepare to answer Data Structure, Algorithms, Linear Algebra, and AI/ML questions depending on the specific role or project team in C++ or Python.

I hope this is helpful! Good luck on your journey and feel free to reach out if you have any questions, comments, and concerns.

Peggy is a junior at Stanford studying Computer Science in the Artificial Intelligence track. She is currently the Sponsorship Chair and HackOverflow Co-Chair of Stanford Women in Computer Science (WiCS). Peggy interned at Lyft Level 5 this past summer on the Motion Planning team. You can reach Peggy on LinkedIn or at [at]



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