  Duckiebots are low-cost mobile robots that are widely used in the fields of
research and education. Although there are existing self-driving algorithms for
the Duckietown platform, they are either too complex or perform too poorly to
navigate a multi-lane track. Moreover, it is essential to give memory and
computational resources to a Duckiebot so it can perform additional tasks such
as out-of-distribution input detection. In order to satisfy these constraints,
we built a low-cost autonomous driving algorithm capable of driving on a
two-lane track. The algorithm uses traditional computer vision techniques to
identify the central lane on the track and obtain the relevant steering angle.
The steering is then controlled by a PID controller that smoothens the movement
of the Duckiebot. The performance of the algorithm was compared to that of the
NeurIPS 2018 AI Driving Olympics (AIDO) finalists, and it outperformed all but
one finalists. The two main contributions of our algorithm are its low
computational requirements and very quick set-up, with ongoing efforts to make
it more reliable.
