Nemodrive brings together a group of motivated researchers with the required resources and support to build a self-driving car that will be tested on the UPB campus streets. The prototype will be developed to tackle the particularities of campus and other local roads. In this context, some of the most recurring challenges of self-driving cars will be empirically put to test, in a small scale, un-controlled environment.

Nemodrive is founded on a desire that drives both its theoretical as well as empirical aspirations: to continuously drive interest and understanding of machine learning.

Given the diversity of perspectives in recent artificial intelligence research, Nemodrive focuses on machine learning techniques, more specifically on developing algorithms that can generate a learning process for every type of sought output. In other words, an algorithm that can build on the initial data set and continuously learn how to drive on its own through experiences.

Among the many challenges posed by fully autonomous cars, we will seek answers to questions such as: Can we enhance training by assisting it with a symbolic representation? Can we bootstrap and improve the learned policy through real human experiences? How much interpretability and explainability can we achieve for the self-driving „brain”?