24,215 research outputs found
Improving Displacement Measurement for Evaluating Longitudinal Road Profiles
2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper introduces a half-wavelength peak matching (HWPM) model, which improves the accuracy of vehicle based longitudinal road profilers used in evaluating road unevenness and mega-textures. In this application, the HWPM model is designed for profilers which utilize a laser displacement sensor with an accelerometer for detecting surface irregularities. The process of converting acceleration to displacement by double integration (which is used in most rofilers) is error-prone, and although there are techniques to minimize the effect of this error, this paper proposes a novel approach for improving the generated road profile results. The technique amends the vertical displacement derived from the accelerometer samples, by using data from the laser displacement sensor as a reference. The vehicle based profiler developed for this experiment (which uses the HWPM model) shows a huge improvement in detected longitudinal irregularities when compared with pre-processed results, and uses a 3-m rolling straight edge as a benchmark.Peer reviewe
Use of generated artificial road profiles in road roughness evaluation
In the evaluation of road roughness and its effects on vehicles response in terms of ride quality, loads induced on pavement, drivers’ comfort, etc., it is very common to generate road profiles based on the equation provided by ISO 8608 standard, according to which it is possible to group road surface profiles into eight different classes. However, real profiles are significantly different from the artificial ones because of the non-stationary feature of the first ones and the not full capability of the ISO 8608 equation to correctly describe the frequency content of real road profiles. In this paper, the international roughness index, the frequency-weighted vertical acceleration awz according to ISO 2631, and the dynamic load index are applied both on artificial and real profiles, highlighting the different results obtained. The analysis carried out in this work has highlighted some limitation of the ISO 8608 approach in the description of performance and conditions of real pavement profiles. Furthermore, the different sensitivity of the various indices to the fitted power spectral density parameters is shown, which should be taken into account when performing analysis using artificial profiles
A Learning-based Stochastic MPC Design for Cooperative Adaptive Cruise Control to Handle Interfering Vehicles
Vehicle to Vehicle (V2V) communication has a great potential to improve
reaction accuracy of different driver assistance systems in critical driving
situations. Cooperative Adaptive Cruise Control (CACC), which is an automated
application, provides drivers with extra benefits such as traffic throughput
maximization and collision avoidance. CACC systems must be designed in a way
that are sufficiently robust against all special maneuvers such as cutting-into
the CACC platoons by interfering vehicles or hard braking by leading cars. To
address this problem, a Neural- Network (NN)-based cut-in detection and
trajectory prediction scheme is proposed in the first part of this paper. Next,
a probabilistic framework is developed in which the cut-in probability is
calculated based on the output of the mentioned cut-in prediction block.
Finally, a specific Stochastic Model Predictive Controller (SMPC) is designed
which incorporates this cut-in probability to enhance its reaction against the
detected dangerous cut-in maneuver. The overall system is implemented and its
performance is evaluated using realistic driving scenarios from Safety Pilot
Model Deployment (SPMD).Comment: 10 pages, Submitted as a journal paper at T-I
PinMe: Tracking a Smartphone User around the World
With the pervasive use of smartphones that sense, collect, and process
valuable information about the environment, ensuring location privacy has
become one of the most important concerns in the modern age. A few recent
research studies discuss the feasibility of processing data gathered by a
smartphone to locate the phone's owner, even when the user does not intend to
share his location information, e.g., when the Global Positioning System (GPS)
is off. Previous research efforts rely on at least one of the two following
fundamental requirements, which significantly limit the ability of the
adversary: (i) the attacker must accurately know either the user's initial
location or the set of routes through which the user travels and/or (ii) the
attacker must measure a set of features, e.g., the device's acceleration, for
potential routes in advance and construct a training dataset. In this paper, we
demonstrate that neither of the above-mentioned requirements is essential for
compromising the user's location privacy. We describe PinMe, a novel
user-location mechanism that exploits non-sensory/sensory data stored on the
smartphone, e.g., the environment's air pressure, along with publicly-available
auxiliary information, e.g., elevation maps, to estimate the user's location
when all location services, e.g., GPS, are turned off.Comment: This is the preprint version: the paper has been published in IEEE
Trans. Multi-Scale Computing Systems, DOI: 0.1109/TMSCS.2017.275146
Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations
Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions
Smart Traction Control Systems for Electric Vehicles Using Acoustic Road-type Estimation
The application of traction control systems (TCS) for electric vehicles (EV)
has great potential due to easy implementation of torque control with
direct-drive motors. However, the control system usually requires road-tire
friction and slip-ratio values, which must be estimated. While it is not
possible to obtain the first one directly, the estimation of latter value
requires accurate measurements of chassis and wheel velocity. In addition,
existing TCS structures are often designed without considering the robustness
and energy efficiency of torque control. In this work, both problems are
addressed with a smart TCS design having an integrated acoustic road-type
estimation (ARTE) unit. This unit enables the road-type recognition and this
information is used to retrieve the correct look-up table between friction
coefficient and slip-ratio. The estimation of the friction coefficient helps
the system to update the necessary input torque. The ARTE unit utilizes machine
learning, mapping the acoustic feature inputs to road-type as output. In this
study, three existing TCS for EVs are examined with and without the integrated
ARTE unit. The results show significant performance improvement with ARTE,
reducing the slip ratio by 75% while saving energy via reduction of applied
torque and increasing the robustness of the TCS.Comment: Accepted to be published by IEEE Trans. on Intelligent Vehicles, 22
Jan 201
An Automated Machine-Learning Approach for Road Pothole Detection Using Smartphone Sensor Data.
Road surface monitoring and maintenance are essential for driving comfort, transport safety and preserving infrastructure integrity. Traditional road condition monitoring is regularly conducted by specially designed instrumented vehicles, which requires time and money and is only able to cover a limited proportion of the road network. In light of the ubiquitous use of smartphones, this paper proposes an automatic pothole detection system utilizing the built-in vibration sensors and global positioning system receivers in smartphones. We collected road condition data in a city using dedicated vehicles and smartphones with a purpose-built mobile application designed for this study. A series of processing methods were applied to the collected data, and features from different frequency domains were extracted, along with various machine-learning classifiers. The results indicated that features from the time and frequency domains outperformed other features for identifying potholes. Among the classifiers tested, the Random Forest method exhibited the best classification performance for potholes, with a precision of 88.5% and recall of 75%. Finally, we validated the proposed method using datasets generated from different road types and examined its universality and robustness
- …