The Internet of Vehicles and Sustainability—Reflections on Environmental, Social, and Corporate Governance

Kostrzewski, Mariusz; Marczewska, Magdalena; Uden, Lorna

doi:10.3390/en16073208

Cited by 10 publications

(9 citation statements)

References 151 publications

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“…A critical analysis of the intersection of the Internet of Vehicles (IoV) and sustainability is provided in [32]. The main contribution of the paper lies in the examination of the relationship between the IoV and sustainability, specifically focusing on the Environmental, Social and Corporate Governance (ESG) aspects.…”

Section: Related Workmentioning

confidence: 99%

A Method for Mapping V2X Communication Requirements to Highly Automated and Autonomous Vehicle Functions

Takacs,

Haidegger

2024

Future Internet

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The significance of V2X (Vehicle-to-Everything) technology in the context of highly automated and autonomous vehicles can hardly be overestimated. While V2X is not considered a standalone technology for achieving high automation, it is recognized as a safety-redundant component in automated driving systems. This article aims to systematically assess the requirements towards V2X input data to highly automated and autonomous systems that can individually, or in combination with other sensors, enable certain levels of autonomy. It addresses the assessment of V2X input data requirements for different levels of autonomy defined by SAE International, regulatory challenges, scalability issues in hybrid environments, and the potential impact of Internet of Things (IoT)-based information in non-automotive technical fields. A method is proposed for assessing the applicability of V2X at various levels of automation based on system complexity. The findings provide valuable insights for the development, deployment and regulation of V2X-enabled automated systems, ultimately contributing to enhanced road safety and efficient mobility.

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Section: Related Workmentioning

confidence: 99%

A Method for Mapping V2X Communication Requirements to Highly Automated and Autonomous Vehicle Functions

Takacs,

Haidegger

2024

Future Internet

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“…The conference presents a complete review of the current paradigms in the Internet of Vehicles (IoV) and expects further advancements. It provides a conceptual masterpiece, presenting a universal architectural system that strengthens numerous communication models to tackle various challenges [38].…”

Section: Iov As a Part Of Smart Transportationmentioning

confidence: 99%

A Review and Analysis of IoT Enabled Smart Transportation Using Machine Learning Techniques

Mukhopadhyay,

Kumar,

Gupta

et al. 2024

IJTDI

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This study represents the complex terrain of smart transport applications, focusing on the synergistic potential that emerges from the strategic confluence of Machine Learning (ML) and Internet of Things (IoT) methodologies. This review provides insight into how the dynamic nature and large volume of data created by IoT systems make them an excellent environment for the integration of ML approaches by exploring the interplay between these areas. Notably, a wide range of ML algorithms have been reviewed and suggested in the context of smart transportation, with a focus on critical areas such as route optimization, parking management, and accident detection/prevention. A crucial finding from this investigation is the noticeable gap in ML coverage throughout the range of smart lighting systems and parking applications. This highlights the need to refocus on these topics from an ML standpoint, opening the path for future investigation and innovation. This research tackles important topics including sustainability, cost-effectiveness, safety, and time efficiency, highlighting the fascinating possibilities of fusing IoT, ML, and smart mobility. Proactively preventing accidents, expedited parking reservations, cutting-edge street lighting, and accurate route suggestions are just a few benefits of the integration of these technologies. The study does, however, highlight the need for more research, particularly in unexplored areas like parking applications and smart lighting. By bridging these gaps and improving ML and IoT cooperation, smart transportation will be greatly improved and creative solutions for improved urban mobility will be offered.

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“…Affordable accelerometers (specifically the MPU6050 tested-InvenSense, California, United States) have excellent performance up to 80 Hz, achieving extremely accurate results in laboratory tests compared to professional piezoelectric accelerometers. At higher frequencies, signal loss occurs at some peaks due to the limitation of the scan rate [3,4].…”

Section: Literature Reviewmentioning

confidence: 99%

Influence of Accelerometer Sensor Position for Measurement of Lateral Acceleration of Delivery Van for Cargo Securement

Jagelcak,

Kubanova

2023

Sensors

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The use of sensors in monitoring lateral accelerations in delivery van transport focuses on measuring lateral accelerations on routes with roundabouts and curves to increase road safety. Using microelectromechanical system (MEMS) sensors, it measures the lateral accelerations acting on the vehicle and the load being transported during the test drives to study vehicle dynamics of delivery van for cargo securing, which is essential to the decision of where accelerometer sensors should be placed when monitoring accelerations or performing cargo securing tests. Using an accelerometer and position tracking, accelerations can be detected when traversing curves and roundabouts at selected locations on the vehicle and load. Manual labeling of acceleration events has been used to identify different lateral acceleration events and regression analysis to determine the relationship between lateral accelerations at different sensor positions. The level of acceleration on the roof of the vehicle was found to be like that occurring on a lashed load with limited movements. If we compare the mean values of the lateral accelerations of the individual events between the sensors, the sensor on the side of the vehicle body at the height of the sensor on the load had approximately 5% lower mean values than the sensor on the roof. The sensor on the load measured approximately 5% higher mean values than the sensor on the roof. Hence, the mean lateral accelerations of the individual events for the sensor on the load are 10% higher than for the sensor at the same height on the vehicle body. The values of the mean lateral accelerations of the delivery van from the sensor on the roof of the vehicle are closer to the values of the accelerations of the sensor on the load than to the values of the sensor on the body of the vehicle at the same height.

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The Internet of Vehicles and Sustainability—Reflections on Environmental, Social, and Corporate Governance

Cited by 10 publications

References 151 publications

A Method for Mapping V2X Communication Requirements to Highly Automated and Autonomous Vehicle Functions

A Method for Mapping V2X Communication Requirements to Highly Automated and Autonomous Vehicle Functions

A Review and Analysis of IoT Enabled Smart Transportation Using Machine Learning Techniques

Influence of Accelerometer Sensor Position for Measurement of Lateral Acceleration of Delivery Van for Cargo Securement

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