This paper utilizes smartphone sensing of vehicle dynamics to determine driver phone use, which can facilitate many traffic safety applications. Our system uses embedded sensors in smartphones, i.e., accelerometers and gyroscopes, to capture differences in centripetal acceleration due to vehicle dynamics. These differences combined with angular speed can determine whether the phone is on the left or right side of the vehicle. Our low infrastructure approach is flexible with different turn sizes and driving speeds. Extensive experiments conducted with two vehicles in two different cities demonstrate that our system is robust to real driving environments. Despite noisy sensor readings from smartphones, our approach can achieve a classification accuracy of over $90\%$ with a false positive rate of a few percent. We also find that by combining sensing results in a few turns, we can achieve better accuracy (e.g., $95\%$) with a lower false positive rate.

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Sensing vehicle dynamics for determining driver phone use

This paper describes WalkCompass, a system that exploits smartphone sensors to estimate the direction in which a user is walking. We find that several smartphone localization systems in the recent past, including our own, make a simplifying assumption that the user's walking direction is known. In trying to relax this assumption, we were not able to find a generic solution from past work. While intuition suggests that the walking direction should be detectable through the accelerometer, in reality this direction gets blended into various other motion patterns during the act of walking, including up and down bounce, side-to-side sway, swing of arms or legs, etc. Moreover, the walking direction is in the phone's local coordinate system (e.g., along Y axis), and translation to global directions, such as 45 degree North, can be challenging when the compass is itself erroneous. WalkCompass copes with these challenges and develops a stable technique to estimate the user's walking direction within a few steps. Results drawn from 15 different environments demonstrate median error of less than 8 degrees, across 6 different users, 3 surfaces, and 3 holding positions. While there is room for improvement, we believe our current system can be immediately useful to various applications centered around localization and human activity recognition.

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Demo: I am a smartphone and i can tell my user's walking direction

Road crashes are a growing concern of governments and are rising to become one of the leading preventable causes of death, especially in developing countries. The ubiquitous presence of smartphones provides a new platform on which to implement sensor networks and driver-assistance systems, as well as other intelligent transportation system (ITS) applications. In this study, existing approaches of using smartphones for ITS applications are analysed and compared. Particular focus is placed on vehicle-based monitoring systems, such as driving behaviour and style recognition, accident detection and road condition monitoring systems. Further opportunities for use of smartphones in ITS systems are highlighted, and remaining challenges in this emerging field of research are identified.

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Survey of smartphone-based sensing in vehicles for intelligent transportation system applications

Just as it has irrevocably reshaped social life, the fast growth of smartphone ownership is now beginning to revolutionize the driving experience and change how we think about automotive insurance, vehicle safety systems, and traffic research. This paper summarizes the first ten years of research in smartphone-based vehicle telematics, with a focus on user-friendly implementations and the challenges that arise due to the mobility of the smartphone. Notable academic and industrial projects are reviewed, and system aspects related to sensors, energy consumption, and human–machine interfaces are examined. Moreover, we highlight the differences between traditional and smartphone-based automotive navigation, and survey the state of the art in smartphone-based transportation mode classification, vehicular ad hoc networks, cloud computing, driver classification, and road condition monitoring. Future advances are expected to be driven by improvements in sensor technology, evidence of the societal benefits of current implementations, and the establishment of industry standards for sensor fusion and driver assessment.

Smartphone-Based Vehicle Telematics: A Ten-Year Anniversary

One of the major problems in developing countries is maintenance of roads. Well maintained roads contribute a major portion to the country’s economy. Identification of pavement distress such as potholes and humps not only helps drivers to avoid accidents or vehicle damages, but also helps authorities to maintain roads. This paper discusses previous pothole detection methods that have been developed and proposes a cost-effective solution to identify the potholes and humps on roads and provide timely alerts to drivers to avoid accidents or vehicle damages. Ultrasonic sensors are used to identify the potholes and humps and also to measure their depth and height, respectively. The proposed system captures the geographical location coordinates of the potholes and humps using a global positioning system receiver. The sensed-data includes pothole depth, height of hump, and geographic location, which is stored in the database (cloud). This serves as a valuable source of information to the government authorities and vehicle drivers. An android application is used to alert drivers so that precautionary measures can be taken to evade accidents. Alerts are given in the form of a flash messages with an audio beep.

Automatic Detection and Notification of Potholes and Humps on Roads to Aid Drivers

The importance of the road infrastructure for the society could be compared with importance of blood vessels for humans. To ensure road surface quality it should be monitored continuously and repaired as necessary. The optimal distribution of resources for road repairs is possible providing the availability of comprehensive and objective real time data about the state of the roads. Participatory sensing is a promising approach for such data collection. The paper is describing a mobile sensing system for road irregularity detection using Android OS based smart-phones. Selected data processing algorithms are discussed and their evaluation presented with true positive rate as high as 90% using real world data. The optimal parameters for the algorithms are determined as well as recommendations for their application.

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Real time pothole detection using Android smartphones with accelerometers

Different machine vision, image analysis systems and navigation sensor modules were tested during the physical model development, the most conformable was selected. Different route planning algorithms were implemented and analyzed. The best result is obtained using the Ant-colony algorithm. The physical model is used for an autonomous outdoor mobile robot software development and student education. Ill. 3, bibl. 5 (in English; abstracts in English and Lithuanian). http://dx.doi.org/10.5755/j01.eee.111.5.363

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Investigation of Time and Energy Consumption using the Physical Model

Monitoring of specific attributes such as vehicle speed and fuel consumption as well as cargo safety is an important problem for transport domain. This task is performed using specific multiagent monitoring systems. To ensure secure operation of such systems they should have autonomous and adaptive behaviour.

Adaptive Vehicle Mode Monitoring Using Embedded Devices with Accelerometers

Video camera module. MKI used CmuCam3 built by Carnegie Mellon Univercity. MKII uses placed on HTC Flyer High-Quality 8Mpix camera module. Navigation system. For the navigation purposes first robot used electronic compass CMPS09, encoder system, and range finders. New system uses Sparkfun 9DOF AHRS module instead of CMPS09. CU. Main control unit of the first robot was ATmega32. As a control unit for a new robot HTC Flyer was chosen because of its powerful processor. Communication abilities. MKII platform is capable of communication using 3G or Wi-Fi networks. It helps an operator to control robot and get telemetry data from any place in the world where the internet connection is available.

Development of Mobile Research Robot

The design methodology for autonomous mobile research robot is proposed. It includes four design stages. At the first stage, the cluster model of sensors and actuators is used to represent the structure of the robot, and the real-time analysis is performed at different periods of robot activity phases. To determine timeliness of the robot, two methods are used. With the help of the first method, the robot is determined as a hard real-time system, when all phases are defined as hard deadlines and executed sequentially. With the help of the second method, the robot is determined as a system having activities with hard and soft deadline execution times. Timeliness of the system is calculated using the time/utility function (TUF). At the second stage, hardware-software architecture is selected and estimated according to the real-time system analysis. At the third stage, the flexible robot physical model is developed and investigated. The fourth stage is devoted to the design of prototype.

Georgijs Kanonirs

Papers

Real time pothole detection using Android smartphones with accelerometers

Investigation of Time and Energy Consumption using the Physical Model

Adaptive Vehicle Mode Monitoring Using Embedded Devices with Accelerometers

Development of Mobile Research Robot

The Design of Autonomous Mobile Unmanned Outdoor Research Robot