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Journal ArticleDOI

Offset-based curvilinear path estimation for mid vehicle collision detection and avoidance system using MARS

21 Mar 2019-Vol. 7, Iss: 2, pp 54-71
TL;DR: A novel curvilinear path estimation model employing multivariate adaptive regression splines (MARS) for mid vehicle collision avoidance is proposed and significantly narrows the gap between the estimated and the true path studied using MSE for different offsets on real (Next Generation Simulation-NGSIM) data.
Abstract: Purpose The purpose of this paper is to propose a novel curvilinear path estimation model employing multivariate adaptive regression splines (MARS) for mid vehicle collision avoidance. The two-phase path estimation scheme initially uses the offset (position) value of the front and the mid (host) vehicle to build the crisp model. The resulting crisp model is MARS regressed to deliver a closely aligned actual model in the second phase. This arrangement significantly narrows the gap between the estimated and the true path analyzed using the mean square error (MSE) for different offsets on Next Generation Simulation Interstate 80 (NGSIM I-80) data set. The presented model also covers parallel parking by encompassing the reverse motion of the host vehicle in the path estimation, thereby, making it amicable for real-road scenarios. Design/methodology/approach The two-phase path estimation scheme initially uses the offset (position) value of the front and the mid (host) vehicle to build the crisp model. The resulting crisp model is MARS regressed to deliver a closely aligned actual model in the second phase. Findings This arrangement significantly narrows the gap between the estimated and the true path studied using MSE for different offsets on real (Next Generation Simulation-NGSIM) data. The presented model also covers parallel parking by encompassing the reverse motion of the host vehicle in the path estimation. Thereby, making it amicable for real-road scenarios. Originality/value This paper builds a mathematical model that considers the offset and host (mid) vehicles for appropriate path fitting.
Citations
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Proceedings ArticleDOI
25 Jan 2022
TL;DR: In this paper , the authors proposed an Internet of Things-based power robbery location and control framework, which offers a more proficient and financially savvy way to deal with remotely move the power consumed by the client.
Abstract: The Internet of Things-based power robbery location and control framework offers a more proficient and financially savvy way to deal with remotely move the power consumed by the client. Buyer extortion in the power business is an extreme issue that all utilities should manage. This remote innovation is utilized to battle power robbery, which is achieved by using an exorbitant amount of control over as far as possible. The significant objective of this study is to follow how much energy used by a model association, like family customers, different organizations, etc. The location and guideline of force has been achieved by utilizing a meter to work out how much power consumed by the client at a specific time. Robbery location unit in the power meter will tell the organization side in case of meter treating or burglary practice, and it will other than send information about theft ID, so they can make an impression on the client's enrolled contact number as an advance notice. Thus, clients will get an admonition message regardless of whether they keep on utilizing unnecessary power, and the power board area will disengage the client's power supply. IoT activities can be completed utilizing a Wi-Fi gadget that sends meter information to a page through an IP address. Power board area utilizes an IOT-based plan to constantly screen power use and charging data determined utilizing a microcontroller.
Journal ArticleDOI
TL;DR: In this article , a framework for a smart vehicle monitoring system to provide smooth mobility of vehicles using roadside units such as streetlights is proposed, which aims to eliminate the necessity of equipping each vehicle with smart sensors, which is typically an expensive scheme in most of the developing and underdeveloped countries around the world.
Abstract: Road accidents cause a large number of public fatalities in developing countries affecting them economically. It has been more than a decade since automobile industries are into developing intelligent vehicles with a major objective of road safety. In order to warn or assist the drivers, various automobile manufacturers have been focusing on developing autonomous or semi-autonomous vehicles, but not much effort has been drawn towards developing the smart infrastructure to supplement the development of intelligent vehicles. This paper proposes a framework for a smart vehicle monitoring system to provide smooth mobility of vehicles using roadside units such as streetlights. Such a system aims to eliminate the necessity of equipping each vehicle with smart sensors, which is typically an expensive scheme in most of the developing and underdeveloped countries around the world. This paper attempts to approach the problem of vehicle collision prediction by designing the system based on the concepts from science and engineering such as relative motion and vectors and implementing it using a machine learning-based model. These concepts led to the generation of datasets over which the prediction model was trained. The performance of the proposed model has been simulated using the software - Virtual Crash.
Journal ArticleDOI
TL;DR: In this paper , the authors study ways of enhancing cross-border transportation capabilities (ECTC) of road freight forwarders: Chong Chom permanent border crossing point, Thailand-Cambodia, to build ECTC model, and to study the effect of eCTC on Transport Entrepreneur Capabilities Performance (ECP) by using mixed methods research design.
Abstract: The objectives of this research are to study ways of Enhancing Cross-Border Transportation Capabilities (ECTC) of road freight forwarders: Chong Chom permanent border crossing point, Thailand-Cambodia, to build ECTC model, and to study the effect of ECTC on Transport Entrepreneur Capabilities Performance (ECP) by using mixed methods research design. Population was 12,546 entrepreneurs and employees of 20 road freight forwarders in Bangkok, Surin and Nakhon Ratchasima. The total of 780 questionnaires was distributed and then 527 were returned. Moreover, in-depth interview was from 18 key informants. The research instrument was tested in terms of reliability and validity. Data was analyzed using a variety of statistics including descriptive statistics, Confirmatory Factor Analysis (CFA), Structural Equation Model (SEM) and Path Analysis. The results have found that Cooperation and Participation (CP) is most important factors for ECTC. In addition, the structure of ECTC model consists of 4 constructs: Enhancing Cross-Border Transportation Capabilities (ECTC) (l= 0.83 – 0.92), Transport Operation (TROP) (l=0.81 – 0.87), Cooperation and Participation (CP) (l=0.85 – 0.89) and Entrepreneur Capabilities Performance (ECP) (l=0.74 – 0.85).
References
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Journal ArticleDOI
TL;DR: The technique of map matching is used to match an aircraft's elevation profile to a digital elevation map and a car's horizontal driven path to a street map and it is shown that the accuracy is comparable with satellite navigation but with higher integrity.
Abstract: A framework for positioning, navigation, and tracking problems using particle filters (sequential Monte Carlo methods) is developed. It consists of a class of motion models and a general nonlinear measurement equation in position. A general algorithm is presented, which is parsimonious with the particle dimension. It is based on marginalization, enabling a Kalman filter to estimate all position derivatives, and the particle filter becomes low dimensional. This is of utmost importance for high-performance real-time applications. Automotive and airborne applications illustrate numerically the advantage over classical Kalman filter-based algorithms. Here, the use of nonlinear models and non-Gaussian noise is the main explanation for the improvement in accuracy. More specifically, we describe how the technique of map matching is used to match an aircraft's elevation profile to a digital elevation map and a car's horizontal driven path to a street map. In both cases, real-time implementations are available, and tests have shown that the accuracy in both cases is comparable with satellite navigation (as GPS) but with higher integrity. Based on simulations, we also argue how the particle filter can be used for positioning based on cellular phone measurements, for integrated navigation in aircraft, and for target tracking in aircraft and cars. Finally, the particle filter enables a promising solution to the combined task of navigation and tracking, with possible application to airborne hunting and collision avoidance systems in cars.

1,787 citations

Journal ArticleDOI
TL;DR: In this article, two models employing Kalman filtering theory are proposed for predicting short-term traffic volume in Nagoya City, Japan, by taking into account data from a number of links.
Abstract: Two models employing Kalman filtering theory are proposed for predicting short-term traffic volume. Prediction parameters are improved using the most recent prediction error and better volume prediction on a link is achieved by taking into account data from a number of links. Based on data collected from a street network in Nagoya City, average prediction error is found to be less than 9% and maximum error less than 30%. The new models perform substantially (up to 80%) better than UTCS-2.

916 citations

Journal ArticleDOI
TL;DR: The proposed algorithm was at the core of the planning and control software for Team MIT's entry for the 2007 DARPA Urban Challenge, where the vehicle demonstrated the ability to complete a 60 mile simulated military supply mission, while safely interacting with other autonomous and human driven vehicles.
Abstract: This paper describes a real-time motion planning algorithm, based on the rapidly-exploring random tree (RRT) approach, applicable to autonomous vehicles operating in an urban environment. Extensions to the standard RRT are predominantly motivated by: 1) the need to generate dynamically feasible plans in real-time; 2) safety requirements; 3) the constraints dictated by the uncertain operating (urban) environment. The primary novelty is in the use of closed-loop prediction in the framework of RRT. The proposed algorithm was at the core of the planning and control software for Team MIT's entry for the 2007 DARPA Urban Challenge, where the vehicle demonstrated the ability to complete a 60 mile simulated military supply mission, while safely interacting with other autonomous and human driven vehicles.

802 citations

Journal ArticleDOI
TL;DR: The SARIMA model coupled with a Kalman filter is the most accurate model; however, the proposed seasonal support vector regressor turns out to be highly competitive when performing forecasts during the most congested periods.
Abstract: The literature on short-term traffic flow forecasting has undergone great development recently. Many works, describing a wide variety of different approaches, which very often share similar features and ideas, have been published. However, publications presenting new prediction algorithms usually employ different settings, data sets, and performance measurements, making it difficult to infer a clear picture of the advantages and limitations of each model. The aim of this paper is twofold. First, we review existing approaches to short-term traffic flow forecasting methods under the common view of probabilistic graphical models, presenting an extensive experimental comparison, which proposes a common baseline for their performance analysis and provides the infrastructure to operate on a publicly available data set. Second, we present two new support vector regression models, which are specifically devised to benefit from typical traffic flow seasonality and are shown to represent an interesting compromise between prediction accuracy and computational efficiency. The SARIMA model coupled with a Kalman filter is the most accurate model; however, the proposed seasonal support vector regressor turns out to be highly competitive when performing forecasts during the most congested periods.

580 citations

Journal ArticleDOI
TL;DR: In this paper, the application of seasonal time series models to the single-interval traffic flow forecasting problem for urban freeways is addressed and the best-fit Winters exponential smoothing models are also developed for each site.
Abstract: The application of seasonal time series models to the single-interval traffic flow forecasting problem for urban freeways is addressed. Seasonal time series approaches have not been used in previous forecasting research. However, time series of traffic flow data are characterized by definite periodic cycles. Seasonal autoregressive integrated moving average (ARIMA) and Winters exponential smoothing models were developed and tested on data sets belonging to two sites: Telegraph Road and the Woodrow Wilson Bridge on the inner and outer loops of the Capital Beltway in northern Virginia. Data were 15-min flow rates and were the same as used in prior forecasting research by B. Smith. Direct comparisons with the Smith report findings were made and it was found that ARIMA (2, 0, 1)(0, 1, 1)96 and ARIMA (1, 0, 1)(0, 1, 1)96 were the best-fit models for the Telegraph Road and Wilson Bridge sites, respectively. Best-fit Winters exponential smoothing models were also developed for each site. The single-step forecast...

450 citations