Learning-Based Driving Events Recognition and Its Application to Digital Roads

Automatic recognition of driving events, e.g., approaching roundabouts, is important both for the truck design process based on simulated road data and for advanced driver assistance systems. However, the problem faced is extremely challenging as only in-vehicle driving data must be used, whereas the number of driving events is usually quite large. In this paper, we propose a learning-based driving events classification method, which is trained and tested with a real driving events database. The proposed method includes definition of driving events relevant to our final application, selection of discriminating features, and classification, using two machine-learning techniques, namely, decision trees and linear logistic regression. We then introduce the digital road concept. This consists of simulated road data used in the truck design process to quantify the behavior of a truck, particularly in terms of fuel consumption. While a digital road typically contains far less driving information, we show that we can still apply the proposed driving events recognition models learnt on real driving data and pave the way for a more realistic assessment of truck characteristics via simulation tools.