ITS+DM Hackathon (ITSC 2017): lane departure prediction with naturalistic driving data

Abstract

On October 16th, 2017, in Yokohama, Japan, from 8:00 to 18:00, the first Intelligent Transportation Systems plus Data Mining challenge was organized under the umbrella of the 2017 IEEE Intelligent Transportation Systems Conference, the flagship conference of the IEEE Intelligent Transportation Systems Society. This activity was organized thanks to a three way collaboration between the ITS Society, Nagoya University, and the IEEE ITSC 2017 organizers. The twenty-three contestants, coming from eleven different countries, faced a classic Naturalistic Driving problem: Lane Departure detection. This paper presents the three best solutions produced. The solutions submitted by most of the participants were very diverse and interesting, but overall, the top ones concurred in the use of ensemble learning after a very interesting feature engineering phase. This hackathon formulation was complex in several ways. It was complex in terms of class imbalance, the challenge time duration and the fact that the provided dataset included only numerical measurements coming from the inertial unit in the testing car. That restriction made it difficult to expect outstanding results – the best one was only slightly over 3% above baseline. However, the organizers thought that such complexities pushed participants to show their repertoire as data scientists, taking into consideration for example computer power load of the different algorithms tested, and overall yielding more interesting approaches to share with the community. Additionally, the most interesting learned lessons were shared, from both an organizational and technical point of view