Simone Faggi

Simone Faggi

MSc Intelligent Systems 🧠

Duckietown - AI Driving Olympics

The “AI Driving Olympics” (AI-DO) is a competition with the objective of evaluating the state of the art in machine learning and artificial intelligence for mobile robotics. The goal of the competition is to build a machine learning (ML) model that allows a self-driving “car”, called Duckiebot, to drive on streets within Duckietown.

The AI Driving Olympics competition is structured into the following separate challenges:

  • Lane Following - LF
  • Lane Following with Vehicles - LFV
  • Lane following with Vehicles and Intersections- LFVI
  • Autonomous Mobility-on-Demand - AMoD

This project is a solution for the aido-LF challenge:

  • Control of a Duckiebot to drive on the right lane on streets within Duckietown without other moving Duckiebots present.

More info about the Duckietown Project and aido-LF challenge here.

Description

The approach is to train a Reinforcement Learning (RL) agent to build an expert that drives perfectly within an environment, then use this expert to collect data. Data are pairs <observation, actions>collected in different maps/environments used to train an agent that imitates the expert’s behaviour (Imitation Learning / Behavioural Cloning) . Finally, you have a self-driving car that navigates within Duckietown using only one single sensor, the camera.

Check out the full project on GitHub

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