Analysis of Machine Learning Techniques Applied to Sensory Detection of Vehicles in Intelligent Crosswalks

Domínguez, José Manuel Lozano; Al-Tam, Faroq; Sanguino, Tomás de Jesús Mateo; Correia, N.

doi:10.3390/s20216019

Cited by 12 publications

(10 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…An important area of smart cities is intelligent transport systems (ITS). These are made up of a set of technological solutions designed to coordinate, improve and increase transport safety on public roads [ 5 , 6 ]. At present, road safety of pedestrians represents a weak point of smart cities as stated in a study carried out by the General Administration of Traffic (DGT), in Spain.…”

Section: Introductionmentioning

confidence: 99%

Walking Secure: Safe Routing Planning Algorithm and Pedestrian’s Crossing Intention Detector Based on Fuzzy Logic App

Domínguez

Sanguino

2021

Sensors

Self Cite

View full text Add to dashboard Cite

Improving road safety through artificial intelligence is now crucial to achieving more secure smart cities. With this objective, a mobile app based on the integration of the smartphone sensors and a fuzzy logic strategy to determine the pedestrian’s crossing intention around crosswalks is presented. The app developed also allows the calculation, tracing and guidance of safe routes thanks to an optimization algorithm that includes pedestrian areas on the paths generated over the whole city through a cloud database (i.e., zebra crossings, pedestrian streets and walkways). The experimentation carried out consisted in testing the fuzzy logic strategy with a total of 31 volunteers crossing and walking around a crosswalk. For that, the fuzzy logic approach was subjected to a total of 3120 samples generated by the volunteers. It has been proven that a smartphone can be successfully used as a crossing intention detector system with an accuracy of 98.63%, obtaining a true positive rate of 98.27% and a specificity of 99.39% according to a receiver operating characteristic analysis. Finally, a total of 30 routes were calculated by the proposed algorithm and compared with Google Maps considering the values of time, distance and safety along the routes. As a result, the routes generated by the proposed algorithm were safer than the routes obtained with Google Maps, achieving an increase in the use of safe pedestrian areas of at least 183%.

show abstract

Section: Introductionmentioning

confidence: 99%

Walking Secure: Safe Routing Planning Algorithm and Pedestrian’s Crossing Intention Detector Based on Fuzzy Logic App

Domínguez

Sanguino

2021

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…To improve road safety, a deep RL agent is being developed through a collection of machine learning (ML) concepts. The results of this study indicate that the ML models can effectively replace the fuzzy classifier [29]. The Pina et al [22] study aimed to investigate the effects of RL on the path planning tasks performed in intelligent vehicle control systems.…”

Section: The Use Of Reinforcement Learning With Extended Realitymentioning

confidence: 86%

Augmented Reality-Assisted Deep Reinforcement Learning-Based Model towards Industrial Training and Maintenance for NanoDrop Spectrophotometer

Alatawi,

Albalawi,

Shahata

et al. 2023

Sensors

View full text Add to dashboard Cite

The use of augmented reality (AR) technology is growing in the maintenance industry because it can improve efficiency and reduce costs by providing real-time guidance and instruction to workers during repairs and maintenance tasks. AR can also assist with equipment training and visualization, allowing users to explore the equipment’s internal structure and size. The adoption of AR in maintenance is expected to increase as hardware options expand and development costs decrease. To implement AR for job aids in mobile applications, 3D spatial information and equipment details must be addressed, and calibrated using image-based or object-based tracking, which is essential for integrating 3D models with physical components. The present paper suggests a system using AR-assisted deep reinforcement learning (RL)-based model for NanoDrop Spectrophotometer training and maintenance purposes that can be used for rapid repair procedures in the Industry 4.0 (I4.0) setting. The system uses a camera to detect the target asset via feature matching, tracking techniques, and 3D modeling. Once the detection is completed, AR technologies generate clear and easily understandable instructions for the maintenance operator’s device. According to the research findings, the model’s target technique resulted in a mean reward of 1.000 and a standard deviation of 0.000. This means that all the rewards that were obtained in the given task or environment were exactly the same. The fact that the reward standard deviation is 0.000 shows that there is no variability in the outcomes.

show abstract

“…Dollars in costs for treating injuries. With a burgeoning population and a quest for a more comfortable life, congestion in cities has taken a worse shape in the twenty-first century [8]. One of the key promises of AI-based self-driving vehicles is the potential to dramatically reduce these derived costs.…”

Section: Fig 9 Ai/ml -Intelligent Traffic Managementmentioning

confidence: 99%

Towards Carbon-Free Automotive Futures: Leveraging AI And ML For Sustainable Transformation

Mandala,

Surabhi,

Kommisetty

et al. 2024

eatp

View full text Add to dashboard Cite

This paper delves into the transformative role of artificial intelligence (AI) and machine learning (ML) in reshaping the automotive industry towards sustainability and environmental consciousness. It begins by examining the integration of intelligent infrastructure, such as sensor-equipped traffic signal lights and cameras, aimed at enhancing road safety and efficiency. Furthermore, the development of autonomous driving and connected vehicle technologies, in conjunction with advancements in V2X communication and 5G technology, presents promising avenues for reducing energy consumption and CO2 emissions in transportation. A critical analysis of the current state of carbon emissions in the automotive industry underscores the pressing need to transition towards carbon-free alternatives, with AI and ML identified as essential tools for predicting emissions, optimizing energy demand, managing electric vehicles, and implementing eco-driving strategies to achieve zero-carbon emissions. Additionally, the paper explores various AI/ML applications for vehicle efficiency improvement, battery optimization, electric vehicle charging, and intelligent traffic management, highlighting their contributions to emissions reduction and improved traffic flow. Nonetheless, the integration of AI/ML into carbon-free automotive futures poses challenges such as regulatory hurdles, infrastructure development, and ethical considerations. Despite these challenges, AI and ML offer promising solutions to mitigate climate change and drive towards a sustainable automotive industry.

show abstract

Analysis of Machine Learning Techniques Applied to Sensory Detection of Vehicles in Intelligent Crosswalks

Cited by 12 publications

References 39 publications

Walking Secure: Safe Routing Planning Algorithm and Pedestrian’s Crossing Intention Detector Based on Fuzzy Logic App

Walking Secure: Safe Routing Planning Algorithm and Pedestrian’s Crossing Intention Detector Based on Fuzzy Logic App

Augmented Reality-Assisted Deep Reinforcement Learning-Based Model towards Industrial Training and Maintenance for NanoDrop Spectrophotometer

Towards Carbon-Free Automotive Futures: Leveraging AI And ML For Sustainable Transformation

Contact Info

Product

Resources

About