On the use of smartphone sensors for developing advanced driver assistance systems

Technological evolution impacts several industries, including automotive. The combination of software with advancements in sensory capabilities results in new Advanced Driver Assistance System (ADAS). The pervasiveness of smartphones and their sensory capabilities makes them an solid platform for the development of ADAS. Our work is motivated by concerns on the reliability of data acquired from such devices for developing ADAS. We performed a number of controlled experiments to understand which factors impact the collection of accelerometer data with smartphones. We conclude that the quality of data acquired is not significantly affected by using different smartphones, car mounts, rates of sampling, or vehicles for the purpose of developing ADAS. Our results indicate that smartphone sensors can be used to develop ADAS.Research sponsored by the Portugal Incentive System for Research and TechnologicalDevelopment. Project in co-promotion no. 002797/2015 (INNOVCAR 2015-2018)

Characterisation of road bumps using smartphones

Introduction: Speed bumps are used as the main means of controlling vehicle speeds all over the world. It is not too infrequent, especially in the emerging economies, to have unmarked bumps that can be perilous for the passengers. Fortuitously, the roadways and mobile phone networks have grown simultaneously in emerging economies. This paper demonstrates the capability of smartphones placed inside the vehicles in characterisation of road bumps. The smart mobile phones have accelerometers and position sensors that can be useful for autonomous monitoring roads. This can empower the user community in monitoring of roads. However, the capability of the smartphone in discerning different types of speed bumps while travelling in heterogeneous vehicle types needs to be examined. Methods: A range of road vehicles is mathematically modelled as mass, spring, and damper systems. The mathematical model of the vehicle is excited with parameters analogous to some common speed bumps and its acceleration response is calculated. The accelerometer of a smartphone is validated by comparing it with high precision accelerometers. The acceleration response of the phone while passing over the corresponding road bumps, which was used in the model earlier, is recorded using an Android based application. The experiment is repeated for different classes of vehicles. Filters have been used to reduce noise in the signals. A time averaging technique has been employed to compress the collected data.Results and conclusions: The acceleration signals have been digitally processed to capture road bumps. The importance of using a mathematical model to understand the acceleration response of a vehicle has been established. Also, the use of pass filters to extract the signal of concern from the noisy data has been exhibited. The ability of the technique to discern different types of speed bumps while travelling in a variety of vehicle types has been demonstrated. This investigation demonstrates the potential to automatically monitor the condition of roadways obviating costly manual inspections. As smartphones are ubiquitous, the methodology has the potential to empower the user community in the maintenance of infrastructure

Detection of Lurkers in Online Social Networks

In this work, we propose a novel data model that integrates and combines information on users belonging to one or more heterogeneous Online Social Networks (OSNs), together with the content that is generated, shared and used within the related environments, using an hypergraph-based approach. Then, we discuss how the most diffused centrality measures – that have been defined over the introduced model – can be efficiently applied for a number of data privacy issues, such as lurkers detection, especially in “interest-based” social networks. Some preliminary experiments using the Yelp dataset are finally presented