Review of University of Toronto’s Coursera Specialization on Self-Driving Cars

The courses

For 1st course, the bicycle model of a car appears amazing simple yet powerful when it comes to modeling the trajectory of a car. I liked how it shows one can approach an engineering problem by layering and abstractions, e.g. by simplifying four wheels into two without losing generality. Also as an introductory course, it contains many real-world engineers and entrepreneurs’ opinions on the industry, the project, the problem domain, and future expectations. I specifically like Paul Newman’s take on the industry and why building a self-driving car which can adapt to all variety of infrastructure instead of building an infrastructure to some specific spec is necessary.

Room for improvement

I feel like the programming assignments could’ve been more well-rounded, because there are sometimes bugs in provided utility functions, a Python version mismatch that broke the Jupyter-Hub, and also the feedback from wrong submissions was very minimal — a better prepared assignment could’ve included more intermediate steps of submissions so that learners could sanity check their progress as check-ups.

Afterthoughts

This area is almost white-hot in recent years. I can count a few high-profile startups as well as big names (Pony.ai, Tesla, Zoox, drive.ai, Waymo, Momenta, Tusimple, Baidu’s Apollo, Uber’s ATG, Cruise, comma.ai, Mobileye, etc. just to name a few without a specific order), each with a different approach and focus area. They are also taking in huge amount of investment money and resources and racing to build a larger fleet of autonomous cars by the day.

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Jiayu Liu

Jiayu Liu

Hi there! I’m Jiayu Liu, currently an engineering manager at Airbnb China, located in Beijing.