Using Formal Concept Analysis Tools in Road Environment-Type Detection

Abstract

This paper presents an urban road environment-type (RET) detection algorithm for an ego-vehicle that is equipped with an automatic traffic sign recognition (TSR) system. The RETs considered in the paper are the downtown, the residential, and the business/industrial areas. The Galois-lattice (GL) often used in formal concept analysis (FCA) is employed to specify the RET detection problem in a formal manner. With the help of the GL, a correspondence between the traffic sign (TS) and crossroad (CR) categories, on the one hand, and the RETs, on the other, is established and represented. This correspondence is also characterized by means of a fuzzy matrix structure, and its alpha-cuts. After an FCA-based learning process, in the detection phase, i.e., while the ego-car is driven along the route, the maximum couplings between RETs and TS/CR sets are searched for in an on-the-fly manner. The proposed RET detection method is suitable for real-time implementation. The paper underlines the importance and applicability of GLs in classification problems.

Fuzzy Pretopological Space for Pedestrians’ Risk Perception Modeling

“…Children and people with disabilities will have trouble crossing the road even with the infrastructure set up, and in some cases, crossing the road within the already established infrastructure could be a real challenge [14,19,20]. Here comes the necessity of using location awareness ecosystems [6]. The technology is growing with a high rate, we can notice that the location awareness became more and more fluid, this is due to the usage of connected objects around us, like GPS devices, smart phones, sensors, etc.…”

“…It collects what each individual do, or where he goes, his heartbeat rating, etc. These collected data can be very massive in terms of field diversity and in term of size, so it should be stored in a scalable format where the data can be manipulated and transformed smoothly [6,22]. That said, graph databases provide an excellent infrastructure to link diverse data.…”

“…With easy expression of entities and relationships between data, graph databases make it easier for programmers, users, and machines to understand the data and find insights. This deeper level of understanding is vital for successful machine learning initiatives, where context-based machine learning is becoming important for feature engineering, machine-based reasoning, and inferencing [1,6,13]. These collected data can be used to enhance the quality and comfort of the pedestrian on the pedestrian walkaway.…”

“…This kind of systems are extremely useful nowadays, it could be such a relief in situations of finding extremely fast some points of interest like for example the time when it's safe to go on the pedestrian walkway [6,19,20]. Trajectory-based human mobility data analysis research has largely focused on the trajectories of people and vehicles, driven by the fact that geographic information science has traditionally supported spatial information from moving objects [6,11,13,15]. Currently, the interest of spatiotemporal data analysis for road safety is a real challenge for modern cities [11,19].…”