Today people increasingly have the opportunity to opt-in to "usage-based" automotive insurance programs for reducing insurance premiums. In these programs, participants install devices in their vehicles that monitor their driving behavior, which raises some privacy concerns. Some devices collect fine-grained speed data to monitor driving habits. Companies that use these devices claim that their approach is privacy-preserving because speedometer measurements do not have physical locations. However, we show that with knowledge of the user's home location, as the insurance companies have, speed data is sufficient to discover driving routes and destinations when trip data is collected over a period of weeks. To demonstrate the real-world applicability of our approach we applied our algorithm, elastic pathing, to data collected over hundreds of driving trips occurring over several months. With this data and our approach, we were able to predict trip destinations to within 250 meters of ground truth in 10 of the traces and within 500 meters in 20 of the traces. This result, combined with the amount of speed data that is being collected by insurance companies, constitutes a substantial breach of privacy because a person's regular driving pattern can be deduced with repeated examples of the same paths with just a few weeks of monitoring.
Related work in the field of dead reckoning does suggest that speedometer traces should have some level of information that can be extracted. Dead reckoning works by using speed or movement data and a known location to deduce a movement path. Dead reckoning has typically been used for map building with mobile robots @cite_2 or as an addition to Global Navigation Satellite Systems (GNSS) such as GPS @cite_41 @cite_20 . When supplementing GNSS data, odometer data for dead reckoning might only be used when GNSS information is unavailable, such as when a vehicle passes through a tunnel or an area with many tall buildings. However, this kind of dead reckoning cannot work without frequent location ground truths as there is no perfect method to match speed data to turns in a road.
@cite_41 @cite_20 @cite_2
2100801339 1599326394 2044831755 1592358051
This paper presents a method for correcting dead reckoning parameters, which are heading and step size, for a pedestrian navigation system. In this method, the compass bias error and the step size error can be estimated during the period that the Global Positioning System (GPS) signal is available. The errors are used for correcting those parameters to improve the accuracy of position determination using only the dead reckoning system when the GPS signal is not available. The results show that the parameters can be estimated with reasonable accuracy. Moreover, the method also helps to increase the positioning accuracy when the GPS signal is available. Most mobile robots use a combination of absolute and relative sensing techniques for position estimation. Relative positioning techniques are generally known as dead-reckoning. Many systems use odometry as their only dead-reckoning means. However, fiber optic gyroscopes have become more affordable and are being used on many platforms to supplement odometry, especially in indoor applications. Still, if the terrain is not level (i.e., rugged or rolling terrain), the tilt of the vehicle introduces errors into the conversion of gyro readings to vehicle heading. In order to overcome this problem vehicle tilt must be measured and factored into the heading computation. The paper introduces a new fuzzy logic expert rule-based navigation (FLEXnav) method for fusing data from multiple low- to medium-cost gyroscopes and accelerometers in order to estimate accurately the heading and tilt of a mobile robot. Experimental results of mobile robot runs over rugged terrain are presented, showing the effectiveness of our FLEXnav method. With the continual miniaturisation of sensors and processing nodes, Pedestrian Dead Reckoning (PDR) systems are becoming feasible options for indoor tracking. These use inertial and other sensors, often combined with domain-specific knowledge about walking, to track user movements. There is currently a wealth of relevant literature spread across different research communities. In this survey, a taxonomy of modern PDRs is developed and used to contextualise the contributions from different areas. Techniques for step detection, characterisation, inertial navigation and step-and-heading-based dead-reckoning are reviewed and compared. Techniques that incorporate building maps through particle filters are analysed, along with hybrid systems that use absolute position fixes to correct dead-reckoning output. In addition, consideration is given to the possibility of using smartphones as PDR sensing devices. The survey concludes that PDR techniques alone can offer good short- to medium- term tracking under certain circumstances, but that regular absolute position fixes from partner systems will be needed to ensure long-term operation and to cope with unexpected behaviours. It concludes by identifying a detailed list of challenges for PDR researchers. A number of positioning identification techniques have been used for mobile robots. Dead reckoning is a popular method, but is not reliable when a robot travels long distances or over an uneven surface because of variations in wheel diameter and wheel slippage. The landmark method, which estimates the current position relative to landmarks, cannot be used in an uncharted environment. The authors propose a new method called "cooperative positioning with multiple robots". For cooperative positioning, the authors divide the robots into two groups, A and B. One group, say A, remains stationary and acts as a landmark while group B moves. The moving group B then stops and acts as a landmark for group A. This "dance" is repeated until the target robot position are reached. Cooperative positioning has a far lower accumulated positioning error than dead reckoning, and can work in three-dimensions which is not possible with dead reckoning. Also, this method has inherent landmarks and therefore works in uncharted environments. This paper discusses the positioning accuracy of the authors' method with error variances for an example with three mobile robots. >