German Castignani

Driver Behavior Profiling Using Smartphones: A Low-Cost Platform for Driver Monitoring

Todays smartphones and mobile devices typically embed advanced motion sensors. Due to their increasing market penetration, there is a potential for the development of distributed sensing platforms. In particular, over the last few years there has been an increasing interest in monitoring vehicles and driving data, aiming to identify risky driving maneuvers and to improve driver efficiency. Such a driver profiling system can be useful in fleet management, insurance premium adjustment, fuel consumption optimization or CO2 emission reduction. In this paper, we analyze how smartphone sensors can be used to identify driving maneuvers and propose SenseFleet, a driver profile platform that is able to detect risky driving events independently from the mobile device and vehicle. A fuzzy system is used to compute a score for the different drivers using real-time context information like route topology or weather conditions. To validate our platform, we present an evaluation study considering multiple drivers along a predefined path. The results show that our platform is able to accurately detect risky driving events and provide a representative score for each individual driver.

Bluetooth Low Energy: An alternative technology for VANET applications

Vehicles are getting increasingly connected. Several technologies have emerged over the last decade that allow cars to communicate with each other and with the Internet. In this paper we propose to use the new Bluetooth Low Energy (BLE) standard as an alternative technology to exchange data between vehicles. By the means of experiments we show that smartphones with BLE radios can be used to send information at low latency from one car to another even while driving. A communication range of up to 100m can be achieved depending on the scenario and environment. Those promising first results are then used as basis for discussion to identify the potential of BLE for different types of vehicular applications.

Driver behavior profiling using smartphones

The proliferation of smartphones and mobile devices embedding different types of sensors sets up a prodigious and distributed sensing platform. In particular, in the last years there has been an increasing necessity to monitor drivers to identify bad driving habits in order to optimize fuel consumption, to reduce CO2 emissions or, indeed, to design new reliable and fair pricing schemes for the insurance market. In this paper, we analyze the driver sensing capacity of smartphones. We propose a mobile tool that makes use of the most common sensors embedded in current smartphones and implement a Fuzzy Inference System that scores the overall driving behavior by combining different fuzzy sensing data.

Bluetooth Low Energy performance and robustness analysis for Inter-Vehicular Communications

Bluetooth Low Energy (BLE) is quickly and steadily gaining importance for a wide range of applications. In this paper we investigate the potential of BLE in a vehicular context. By means of experiments, we first evaluate the characteristics of the wireless channel, then we define a set of driving scenarios to analyse how BLE is affected by varying speed, distance and traffic conditions. We found that the maximum communication range between two devices can go beyond 100?m and that a robust connection, capable of handling sudden signal losses or interferences, can be achieved up to a distance of 50?m even for varying traffic and driving conditions. We then present a proof-of-concept mobile application for off-the-shelf smartphones that can be used to transmit data over multiple hops. Next, we analyse how BLE handles other interferences on the same frequency band by building and validating an interference testbed based on the IEEE 802.11 technology. Finally we discuss the advantages and limitations of BLE for Inter-Vehicular Communications (IVC) and propose potential applications.

Wi2Me: A Mobile Sensing Platform for Wireless Heterogeneous Networks

With the increasing popularity of WiFi technologies, mobile users may now take advantage of heterogeneous wireless networks. In contrast to cellular networks, community networks, based on sharing WiFi residential accesses, show a high access points density in urban areas but uncontrolled performance. We present Wi2Me Traces Explorer, an extensible mobile sensing application to characterize current deployments. This application allows any mobile user to gather not only access point locations but also their performance in terms of bandwidth, link quality and successful connection rate.

An evaluation study of driver profiling fuzzy algorithms using smartphones

Profiling driving behavior has become a relevant aspect in fleet management, automotive insurance and eco-driving. Detecting inefficient or aggressive drivers can help reducing fleet degradation, insurance policy cost and fuel consumption. In this paper, we present a Fuzzy-Logic based driver scoring mechanism that uses smartphone sensing data, including accelerometers and GPS. In order to evaluate the proposed mechanism, we have collected traces from a testbed consisting in 20 vehicles equipped with an Android sensing application we have developed to this end. The results show that the proposed sensing variables using smartphones can be merged to provide each driver with a single score.

A novel eco-driving application to reduce energy consumption of electric vehicles

Electro-mobility is becoming increasingly important in nowadays transportation systems. However, due to the limited range of electric vehicles, drivers need to adopt an energy-efficient driving attitude. In this paper, we present a novel eco-driving application that informs the driver about his energy efficiency. We implement an Android application that is able to gather relevant data from the vehicles CAN bus using an OBD Bluetooth adapter. We evaluate the retrieved data together with topographic information retrieved from the Internet in order to provide the driver with a representative eco-score based on a Fuzzy-System. In order to validate our approach, an experimental evaluation is proposed for different drivers in a predefined path. The results show that the computed eco-score accurately reflects driving efficiency. Further, we show that eco-driving concepts can significantly reduce the overall energy consumption and thus extend the electric vehicles range.

Smartphone-Based Adaptive Driving Maneuver Detection: A Large-Scale Evaluation Study

The proliferation of connected mobile devices together with advances in their sensing capacity has enabled a new distributed telematics platform. In particular, smartphones can be used as driving sensors to identify individual driver behavior and risky maneuvers. However, in order to estimate driver behavior with smartphones, the system must deal with different vehicle characteristics. This is the main limitation of existing sensing platforms, which are principally based on fixed thresholds for different sensing parameters. In this paper, we propose an adaptive driving maneuver detection mechanism that iteratively builds a statistical model of the driver, vehicle, and smartphone combination using a multivariate normal model. By means of experimentation over a test track and public roads, we first explore the capacity of different sensor input combinations to detect risky driving maneuvers, and we propose a training mechanism that adapts the profiling model to the vehicle, driver, and road topology. A large-scale evaluation study is conducted, showing that the model for maneuver detection and scoring is able to adapt to different drivers, vehicles, and road conditions.

Bluetooth low energy for inter-vehicular communications

Bluetooth Low Energy (BLE) is quickly and steadily gaining importance for a wide range of applications. In this paper we investigate the potential of BLE in a vehicular context. By means of experiments, we first evaluate the characteristics of the wireless channel, then we define a set of driving scenarios to analyze how BLE is affected by varying speed, distance and traffic conditions. We found that the maximum communication range between two devices can go beyond 100 meters and that a robust connection can be achieved up to a distance of 50 meters even for varying traffic and driving conditions. Next, we present a proof-of-concept mobile application for off-the-shelf smartphones that can be used to transmit data over multiple hops. Finally we discuss the advantages and limitations of BLE for Inter-Vehicular Communications (IVC) and propose potential applications.

Access Point discovery in 802.11 networks

This paper analyzes the scanning process in IEEE 802.11 networks in an urban setting characterized by a high Access Point (AP) density. Most of these APs belong to a community network, known as a collection of APs announcing the same network name (Service Set Identifier or SSID). The owner of an AP can optionally configure the community network of his/her AP, resulting in an irregular topology for each community network as there is no central planning authority. We investigate the relationship between the time spent in each channel while scanning for available APs and the number of AP actually detected. In particular we show that, in order to discover all available APs at a given location, we need to combine the results of multiple scans. Based on this result we argue that the efficiency of the scanning process could be greatly improved by using a database shared by all the users of a community network, containing the available APs at different locations.