Problem Statement

Let's learn how a self-driving car can "see" its surroundings using semantic image segmentation.


The definition of a self-driving car is a vehicle that drives itself, with little or no human intervention. Its system uses several sensory receptors to perceive the environment. For instance, it identifies the drivable area, weather conditions, obstacles ahead and plans the next move for the vehicle accordingly.

There are different levels of autonomy in such vehicles. Tesla currently implements assisted driving, where the driving is autonomous, but someone is behind the wheel. Waymo (Google’s self-driving car), in contrast, is aiming for complete autonomy under all driving conditions (no driver required). Beyond human transportation, self-driving vehicles can also be utilized as a service for various purposes, e.g., Nuro is building self-driving vehicles for local goods transportation.

📝 Self-driving vehicles are perfect real-world systems for handling multi-sensory inputs that focus primarily on computer vision-based problems (e.g., object classification/ detection/ segmentation), using machine learning.

Now that you have some context, let’s look at the problem statement.

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