Connected vehicle safety science, system, and framework

Chen, Kuan-Wen; Tsai, Hsin-Mu; Hsieh, Cheng-Yu; Lin, Shou-De; Wang, Chieh-Chih; Yang, Shao-Wen; Chien, Shao-Yi; Lee, Chia-Han; Su, Yu-Chi; Chou, Chun-Ting; Lee, Yuh-Jye; Pao, Hsing-Kuo; Guo, Ruey‐Shan; Chen, Chung‐Jen; Yang, Ming–Hsuan; Chen, Bing‐Yu; Hung, Yi‐Ping

doi:10.1109/wf-iot.2014.6803165

Cited by 9 publications

(2 citation statements)

References 14 publications

(10 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Indeed, several works have tried to estimate dynamic parameters using conventional methods. In particular, multibody models are widely used to estimate roll angle [ 27 ], lateral dynamics forces [ 28 ], and attitude estimation [ 29 , 30 ].…”

Section: Cornering Assistance System Formulationmentioning

confidence: 99%

An Advanced Rider-Cornering-Assistance System for PTW Vehicles Developed Using ML KNN Method

Jalti

Hajji

Acri

et al. 2023

Sensors

View full text Add to dashboard Cite

The dynamic behavior of a Powered Two-Wheeler (PTW) is much more complicated than that of a car, which is due to the strong coupling between the longitudinal and lateral dynamics produced by the large roll angles. This makes the analysis of the dynamics, and therefore the design and synthesis of the controller, particularly complex and difficult. In relation to assistance in dangerous situations, several recent manuscripts have suggested devices with limitations of cornering velocity by proposing restrictive models. However, these models can lead to repulsion by the users of PTW vehicles, significantly limiting vehicle performance. In the present work, the authors developed an Advanced Rider-cornering Assistance System (ARAS) based on the skills learned by riders running across curvilinear trajectories using Artificial Intelligence (AI) and Neural Network (NN) techniques. New algorithms that allow the value of velocity to be estimated by prediction accuracy of up to 99.06% were developed using the K-Nearest Neighbor (KNN) Machine Learning (ML) technique.

show abstract

Section: Cornering Assistance System Formulationmentioning

confidence: 99%

An Advanced Rider-Cornering-Assistance System for PTW Vehicles Developed Using ML KNN Method

Jalti

Hajji

Acri

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Intelligent transport schemes have been extensively studied over the last few decades for providing advanced and motivated facilities for traffic organization and steering security concerns. Chen et al [84] have developed a proximate vehicle map building procedure for localization, in which the accuracy is highly reliant on the location outcomes of every vehicle. Actually, maximum vehicles conveying approaches are omnidirectional, affecting the information sharing among the vehicles when their accurate location is not easily sensed.…”

Section: Smart Transportation and Mobilitymentioning

confidence: 99%

Survey of Localization for Internet of Things Nodes: Approaches, Challenges and Open Issues

2021

View full text Add to dashboard Cite

With exponential growth in the deployment of Internet of Things (IoT) devices, many new innovative and real-life applications are being developed. IoT supports such applications with the help of resource-constrained fixed as well as mobile nodes. These nodes can be placed in anything from vehicles to the human body to smart homes to smart factories. Mobility of the nodes enhances the network coverage and connectivity. One of the crucial requirements in IoT systems is the accurate and fast localization of its nodes with high energy efficiency and low cost. The localization process has several challenges. These challenges keep changing depending on the location and movement of nodes such as outdoor, indoor, with or without obstacles and so on. The performance of localization techniques greatly depends on the scenarios and conditions from which the nodes are traversing. Precise localization of nodes is very much required in many unique applications. Although several localization techniques and algorithms are available, there are still many challenges for the precise and efficient localization of the nodes. This paper classifies and discusses various state-of-the-art techniques proposed for IoT node localization in detail. It includes the different approaches such as centralized, distributed, iterative, ranged based, range free, device-based, device-free and their subtypes. Furthermore, the different performance metrics that can be used for localization, comparison of the different techniques, some prominent applications in smart cities and future directions are also covered.

show abstract