M2M Day 203: Challenge complete!

This post is part of Month to Master, a 12-month accelerated learning project. For May, my goal is to build the software part of a self-driving car.

This month, I challenged myself to build the software part of a self-driving car. In particular, I wanted to build two main things: 1. The car’s steering system and 2. The car’s pedal system (i.e. the throttle and brake).

Two days ago, I finished building the steering system — which can accurately predict steering angle based on a forward-facing video feed of the road.

For the steering system to be considered a success, I set two sub-goals:

1. I needed to adapt and use the self-driving car model on a dataset that it wasn’t specifically designed for. Since I used a model based on NVIDIA’s research paper applied to a dataset provided by Udacity, I fulfilled the requirements for this sub-goal.
2. Secondly, I needed to train the model on one dataset (i.e. set of roads), and the have it perform well on a completely different dataset (i.e. set of new roads). Since I trained the model on the Udacity dataset, and then successfully tested the model on the NVIDIA dataset, I also fulfilled the requirements for this sub-goal.

Then, yesterday, I used the same model, with modified data inputs, to successfully create the throttle and braking systems, which can accurately predict the throttle amount or braking amount based on a forward-facing video feed of the road.

With all these pieces assembled, this month’s challenge is officially complete!

Read the next post. Read the previous post.

Max Deutsch is an obsessive learner, product builder, guinea pig for Month to Master, and founder at Openmind.

If you want to follow along with Max’s year-long accelerated learning project, make sure to follow this Medium account.