123,768 research outputs found
Driving style recognition for intelligent vehicle control and advanced driver assistance: a survey
Driver driving style plays an important role in vehicle energy management as well as driving safety. Furthermore, it is key for advance driver assistance systems development, toward increasing levels of vehicle automation. This fact has motivated numerous research and development efforts on driving style identification and classification. This paper provides a survey on driving style characterization and recognition revising a variety of algorithms, with particular emphasis on machine learning approaches based on current and future trends. Applications of driving style recognition to intelligent vehicle controls are also briefly discussed, including experts' predictions of the future development
A Comprehensive Survey on Deep-Learning-based Vehicle Re-Identification: Models, Data Sets and Challenges
Vehicle re-identification (ReID) endeavors to associate vehicle images
collected from a distributed network of cameras spanning diverse traffic
environments. This task assumes paramount importance within the spectrum of
vehicle-centric technologies, playing a pivotal role in deploying Intelligent
Transportation Systems (ITS) and advancing smart city initiatives. Rapid
advancements in deep learning have significantly propelled the evolution of
vehicle ReID technologies in recent years. Consequently, undertaking a
comprehensive survey of methodologies centered on deep learning for vehicle
re-identification has become imperative and inescapable. This paper extensively
explores deep learning techniques applied to vehicle ReID. It outlines the
categorization of these methods, encompassing supervised and unsupervised
approaches, delves into existing research within these categories, introduces
datasets and evaluation criteria, and delineates forthcoming challenges and
potential research directions. This comprehensive assessment examines the
landscape of deep learning in vehicle ReID and establishes a foundation and
starting point for future works. It aims to serve as a complete reference by
highlighting challenges and emerging trends, fostering advancements and
applications in vehicle ReID utilizing deep learning models
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A Survey on Cooperative Longitudinal Motion Control of Multiple Connected and Automated Vehicles
Synergizing Roadway Infrastructure Investment with Digital Infrastructure for Infrastructure-Based Connected Vehicle Applications: Review of Current Status and Future Directions
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The safety, mobility, environmental and economic benefits of Connected and Autonomous Vehicles (CAVs) are potentially dramatic. However, realization of these benefits largely hinges on the timely upgrading of the existing transportation system. CAVs must be enabled to send and receive data to and from other vehicles and drivers (V2V communication) and to and from infrastructure (V2I communication). Further, infrastructure and the transportation agencies that manage it must be able to collect, process, distribute and archive these data quickly, reliably, and securely. This paper focuses on current digital roadway infrastructure initiatives and highlights the importance of including digital infrastructure investment alongside more traditional infrastructure investment to keep up with the auto industry's push towards this real time communication and data processing capability. Agencies responsible for transportation infrastructure construction and management must collaborate, establishing national and international platforms to guide the planning, deployment and management of digital infrastructure in their jurisdictions. This will help create standardized interoperable national and international systems so that CAV technology is not deployed in a haphazard and uncoordinated manner
A survey of fuzzy control for stabilized platforms
This paper focusses on the application of fuzzy control techniques (fuzzy
type-1 and type-2) and their hybrid forms (Hybrid adaptive fuzzy controller and
fuzzy-PID controller) in the area of stabilized platforms. It represents an
attempt to cover the basic principles and concepts of fuzzy control in
stabilization and position control, with an outline of a number of recent
applications used in advanced control of stabilized platform. Overall, in this
survey we will make some comparisons with the classical control techniques such
us PID control to demonstrate the advantages and disadvantages of the
application of fuzzy control techniques
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