Abstract
Every day, new applications arise relying on the use of high-resolution road maps in both academic and industrial environments. Autonomous vehicles rely on digital maps to navigate when optical sensors cannot be trusted, such as heavy rainfalls, snowy conditions, fog, and other situations. These situations increase the risks of accidents and disable the potentials of real-time mapping sensors. To tackle those problems, we present a methodology to automatically map anomalies on the road, namely speed bumps in this study, using an off-the-shelf camera (GoPro) and Machine Learning (ML) algorithms. We acquired data over a series of differently shaped speed bumps and applied three classification techniques: Naive Bayes, Multi-Layer Perceptron, and Random Forest (RF). With over 96% of classification accuracy, then RF was able to identify speed bumps on a GoPro dataset automatically. The results show a potential of the proposed methodology to be developed in surveying vehicles to produce highly-detailed maps of vertical road anomalies with a fast and accurate update rate.
Highlights
According to the 2018 Revision of World Urbanization Prospects delivered by the United Nations, the majority of the world population lives in urban areas
The most important features to be mapped in a road inventory are the central road axis, traffic signs, potholes, and speed bumps
This paper proposes a methodology that is organized in 4 main steps, as presented in Fig. 1 for an automatic speed bump mapping using data from a GoPro device in a vehicle, with a post-processed MLbased algorithm
Summary
According to the 2018 Revision of World Urbanization Prospects delivered by the United Nations, the majority of the world population (approximately 55%) lives in urban areas. This percentage is even higher in some parts of the globe, such as Northern America (82%), Latin America (81%), Europe (74%) and Oceania (68%). The road network must have its structure well surveyed, due to its importance for traffic management. These documents are crucial for city planning, road inventory, safe driving, and correlated applications (Aljaafreh et al 2017, He & Orvets 2000, Wang et al 2016 and Zhao et al 2011). As presented by (Aljaafreh et al 2017), road network monitoring and maintenance is crucial to increase the safety of drivers and pedestrians
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