17 research outputs found
Sensing vehicle dynamics for determining driver phone use
This paper utilizes smartphone sensing of vehicle dynamics to de-termine 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 cen-tripetal 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 ap-proach is flexible with different turn sizes and driving speeds. Ex-tensive experiments conducted with two vehicles in two different cities demonstrate that our system is robust to real driving envi-ronments. 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
SaferCross: Enhancing Pedestrian Safety Using Embedded Sensors of Smartphone
The number of pedestrian accidents continues to keep climbing. Distraction
from smartphone is one of the biggest causes for pedestrian fatalities. In this
paper, we develop SaferCross, a mobile system based on the embedded sensors of
smartphone to improve pedestrian safety by preventing distraction from
smartphone. SaferCross adopts a holistic approach by identifying and developing
essential system components that are missing in existing systems and
integrating the system components into a "fully-functioning" mobile system for
pedestrian safety. Specifically, we create algorithms for improving the
accuracy and energy efficiency of pedestrian positioning, effectiveness of
phone activity detection, and real-time risk assessment. We demonstrate that
SaferCross, through systematic integration of the developed algorithms,
performs situation awareness effectively and provides a timely warning to the
pedestrian based on the information obtained from smartphone sensors and Direct
Wi-Fi-based peer-to-peer communication with approaching cars. Extensive
experiments are conducted in a department parking lot for both component-level
and integrated testing. The results demonstrate that the energy efficiency and
positioning accuracy of SaferCross are improved by 52% and 72% on average
compared with existing solutions with missing support for positioning accuracy
and energy efficiency, and the phone-viewing event detection accuracy is over
90%. The integrated test results show that SaferCross alerts the pedestrian
timely with an average error of 1.6sec in comparison with the ground truth
data, which can be easily compensated by configuring the system to fire an
alert message a couple of seconds early.Comment: Published in IEEE Access, 202
Analysis and development of a novel algorithm for the in-vehicle hand-usage of a smartphone
Smartphone usage while driving is unanimously considered to be a really
dangerous habit due to strong correlation with road accidents. In this paper,
the problem of detecting whether the driver is using the phone during a trip is
addressed. To do this, high-frequency data from the triaxial inertial
measurement unit (IMU) integrated in almost all modern phone is processed
without relying on external inputs so as to provide a self-contained approach.
By resorting to a frequency-domain analysis, it is possible to extract from the
raw signals the useful information needed to detect when the driver is using
the phone, without being affected by the effects that vehicle motion has on the
same signals. The selected features are used to train a Support Vector Machine
(SVM) algorithm. The performance of the proposed approach are analyzed and
tested on experimental data collected during mixed naturalistic driving
scenarios, proving the effectiveness of the proposed approach
IMU-based smartphone-to-vehicle positioning
This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordIn this paper, we address the problem of using inertial measurements to position a smartphone with respect to a vehicle-fixed accelerometer. Using rigid body kinematics, this is cast as a nonlinear filtering problem. Unlike previous publications, we consider the complete three-dimensional kinematics, and do not approximate the angular acceleration to be zero. The accuracy of an estimator based on the unscented Kalman filter is compared with the Cramer-Rao bound. As is illustrated, the estimates can be expected to be better in the horizontal plane than in the vertical direction of the vehicle frame. Moreover, implementation issues are discussed and the system model is motivated by observability arguments. The efficiency of the method is demonstrated in a field study which shows that the horizontal RMSE is in the order of 0.5 [m]. Last, the proposed estimator is benchmarked against the state-of-the-art in left/right classification. The framework can be expected to find use in both insurance telematics and distracted driving solutions
Road Grade Estimation Using Crowd-Sourced Smartphone Data
Estimates of road grade/slope can add another dimension of information to
existing 2D digital road maps. Integration of road grade information will widen
the scope of digital map's applications, which is primarily used for
navigation, by enabling driving safety and efficiency applications such as
Advanced Driver Assistance Systems (ADAS), eco-driving, etc. The huge scale and
dynamic nature of road networks make sensing road grade a challenging task.
Traditional methods oftentimes suffer from limited scalability and update
frequency, as well as poor sensing accuracy. To overcome these problems, we
propose a cost-effective and scalable road grade estimation framework using
sensor data from smartphones. Based on our understanding of the error
characteristics of smartphone sensors, we intelligently combine data from
accelerometer, gyroscope and vehicle speed data from OBD-II/smartphone's GPS to
estimate road grade. To improve accuracy and robustness of the system, the
estimations of road grade from multiple sources/vehicles are crowd-sourced to
compensate for the effects of varying quality of sensor data from different
sources. Extensive experimental evaluation on a test route of ~9km demonstrates
the superior performance of our proposed method, achieving
improvement on road grade estimation accuracy over baselines, with 90\% of
errors below 0.3.Comment: Proceedings of 19th ACM/IEEE Conference on Information Processing in
Sensor Networks (IPSN'20
Comparison of Methods for Estimating Instantaneous Turn Radius of Ackermann Steering Vehicles
The instantaneous turn radius of an Ackermann steering vehicle is the distance to a point in space about which the vehicle will travel in an arc during a turn. There are at least six ways to estimate instantaneous turn radius and each method uses different inputs and has distinct advantages and disadvantages. In this thesis, six different methods will be used to estimate the instantaneous turn radius of a vehicle traveling on a closed circuit. This testing will clarify similarities and differences between the methods. This thesis clarifies which method will be the most appropriate for a given set of available inputs. The testing will be conducted with commonly available sensors. The research in this thesis will allow developers to choose the best method for their sensor suite
Design, Implementation and a Pilot Study of Mobile Framework for Pedestrian Safety Using Smartphone Sensors
Pedestrian distraction from smartphones is a serious social problem that caused an ever increasing number of fatalities especially as virtual reality (VR) games have gained popularity recently. In this thesis, we present the design, implementation, and a pilot study of WiPedCross, a WiFi direct-based pedestrian safety system that senses and evaluates a risk, and alerts accordingly the user to prevent traffic accidents. In order to develop a non-intrusive, accurate, and energy-efficient pedestrian safety system, a number of technical challenges are addressed: to enhance the positioning accuracy of the user for precise risk assessment, a map-matching algorithm based on a Hidden Markov Model is designed; to minimize energy consumption, an adaptive scheme is developed that dynamically activates the GPS module of a phone according to pedestrian walking speed and the locations of nearby crosswalks; to suppress false alarms, a novel algorithm is developed to accurately identify the user-phone-viewing activity so that collision probability assessment is triggered only when the pedestrian is walking while viewing his or her phone. The prototype of the proposed framework is implemented on an Android platform for a pilot study to evaluate feasibility, reliability, and validity of WiPedCross. Extensive experiments are performed in a parking lot and the results demonstrate that WiPedCross assesses the collision probability effectively and provides warning to the user in a timely manner. The system modules of the proposed framework are expected to benefit numerous other pedestrian safety apps