Abstract:

In 2016 we proposed a new course syllabus for the laboratory sessions of the subject Artificial Vision and Robotics [1]. Our proposal included one activity where the students must implement an algorithm to autonomously drive a car in a simulated environment. To encourage the students to do their best, two measures were taken. First, the students must compete against each other upon the deadline. Second, the students were inducted at different levels (strongly inducted, lightly inducted, no induction at all) on how to solve the problem during the years, but always leaving open the option to do research and implement the algorithm they think is best.

In this work we show the study of the last measure. We collected the number of similar approaches, grouped by five different categories (rule-based, traditional mapping, deep learning, deep learning evolved and reinforcement learning) for the years 2016, 2017 and 2018, and we found out that the students aimed to and learned more sophisticated and complex solutions as the level of induction is reduced.

References:
[1] F. Escalona, F. Gomez-Donoso, D. Viejo, S. Orts-Escolano, M. Cazorla, “PRACTICAL CLASSES IN A ROBOTICS AND COMPUTER VISION SUBJECT”, INTED 2017 Proceedings, ISSN: 2340-1079, 2016

Keywords:

Research projects, computer vision, proactivity.