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Journal articles on the topic 'Advanced driver assistance system'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Agarwal, Sanskar, and Kavitha R. "Advanced Driver Assistance System Using Machine Learning." International Journal of Research Publication and Reviews 5, no. 3 (2024): 1364–70. http://dx.doi.org/10.55248/gengpi.5.0324.0661.

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Inamdar, Aditi, and Dr Rama Bansode. "Advanced Driver Assistance System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 07 (2024): 1–15. http://dx.doi.org/10.55041/ijsrem36848.

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Abstract—Advanced Driver Assistance System(ADAS) has become a salient feature for safety in modern vehicles. They are also a key underlying technology in emerging autonomous vehicles. State-of-the-art ADASs are primarily vision based,but light detection and ranging(lidar),radio detection and ranging(radar) and other advanced-sensing technologies are also becoming popular. In this article, we present a survey of different hardware and software ADAS technologies and their capabilities and limitations. We discuss approaches used for vision based recognition and sensor fusion in ADAS solutions. We

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Shivale, Pranali, Neha Sonawane, Srushti Dharmale, Taha Lokhandwala, and Prof Dr M. B. Wagh. "Advanced Driver Assistance System." International Journal for Research in Applied Science and Engineering Technology 11, no. 3 (2023): 1111–13. http://dx.doi.org/10.22214/ijraset.2023.49547.

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bstract: Many people die each year in roadway departure crashes caused by driver inattention. Lane detection systems are useful in avoiding these accidents as safety is the main purpose of these systems. Such systems have the goal to detect the lane marks and to warn the driver in case the vehicle tends to depart from the lane. A lane detection system is an important element of many intelligent transport systems. Lane detection is a challenging task because of the varying road conditions that one can come across while driving. In the past few years, numerous approaches for lane detection were

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Chavan, Sakshi, Shubham Pawar, Shreyas Mahajan, and M. S. Gawade. "Advanced Driver Assistance System Using Image Processing For Blindspot Detection." International Journal of Research Publication and Reviews 6, no. 6 (2025): 2462–68. https://doi.org/10.55248/gengpi.6.0625.2052.

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Ayyasamy, S. "A Comprehensive Review on Advanced Driver Assistance System." Journal of Soft Computing Paradigm 4, no. 2 (2022): 69–81. http://dx.doi.org/10.36548/jscp.2022.2.003.

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In recent years, automotive industry is experiencing an unprecedented transformation with the rise of digital technologies. While in the past, acceleration, top speed, and mechanical design were the most essential factors for purchasing an automobile, electronics and software innovations define the characteristics of the future. One among such innovations is the Advanced Driver Assistance System (ADAS). This innovation is now considered as the major drive force of the automotive domain with the intelligent electronic and software architectures. ADAS is primarily designed with an objective to a

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M N, Dr Rekha, Adithya V. Prabhu, Chandana K M, Vaibhavi J K, and Bhagyashree Bhagyashree. "Advanced Driver Assist System for Automobiles." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 008 (2024): 1–15. http://dx.doi.org/10.55041/ijsrem37176.

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A group of technologies known as advanced driver assistance systems (ADAS) aid in keeping drivers safe and preventing collisions. These systems monitor the area surrounding the car using a range of sensors, including cameras, radar, and lidar, and if required, send out alerts or make necessary corrections. The primary area of comfort for the driver is the driver assistance system. This study identifies, investigates, and puts into practice the issues of reduced visibility in curves, particularly in ghats, and visual impairment brought on by fog and abrupt approaching light. The suggested appro

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Ambika, Mrs M. "Lane Deviation Warning System (Advanced Driver Assistance)." International Journal for Research in Applied Science and Engineering Technology 7, no. 3 (2019): 1123–29. http://dx.doi.org/10.22214/ijraset.2019.3199.

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Izquierdo-Reyes, Javier, Ricardo A. Ramirez-Mendoza, Martin R. Bustamante-Bello, Sergio Navarro-Tuch, and Roberto Avila-Vazquez. "Advanced driver monitoring for assistance system (ADMAS)." International Journal on Interactive Design and Manufacturing (IJIDeM) 12, no. 1 (2016): 187–97. http://dx.doi.org/10.1007/s12008-016-0349-9.

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Kumar, Mayank, Niharika A, Kethireddy Anjali Reddy, Harsh Gupta, and Pushpalatha K N. "Advanced Driver Assistance System (ADAS) on FPGA." International Journal of VLSI & Signal Processing 10, no. 2 (2023): 22–26. http://dx.doi.org/10.14445/23942584/ijvsp-v10i2p104.

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OZAKI, Osamu, Tomoya KAWASAKI, Kenichiro AOKI, Kentaro MATSUMOTO, and Masaki KITAGO. "Development of Advanced Drive, an Advanced Driver Assistance System using intelligence technologies." Proceedings of Mechanical Engineering Congress, Japan 2021 (2021): J181–01. http://dx.doi.org/10.1299/jsmemecj.2021.j181-01.

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OZAKI, Osamu, Tomoya KAWASAKI, Kenichiro AOKI, Kentaro MATSUMOTO, and Masaki KITAGO. "Development of Advanced Drive, an Advanced Driver Assistance System using intelligence technologies." Proceedings of Mechanical Engineering Congress, Japan 2021 (2021): F183–04. http://dx.doi.org/10.1299/jsmemecj.2021.f183-04.

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Wilkinson, Mark E., and Daniel V. McGehee. "Auditory Global Positioning System and Advanced Driver Assistance Systems." Optometry and Vision Science 96, no. 2 (2019): 130–32. http://dx.doi.org/10.1097/opx.0000000000001326.

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Crump, Caroline, David Cades, Benjamin Lester, et al. "Differing Perceptions of Advanced Driver Assistance Systems (ADAS)." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 60, no. 1 (2016): 861–65. http://dx.doi.org/10.1177/1541931213601197.

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The extent to which drivers’ attitudes toward the use of autonomous vehicle systems can be influenced by various driver factors (e.g., driver age, experience with the systems, etc.) has yet to be firmly established. To investigate driver perceptions and acceptance of advanced vehicle systems, the current research examined initial and repeated exposure to systems while driving under various commonly encountered on-road situations (e.g., emergency braking). Somewhat surprisingly, driver perceptions of safety when driving vehicles with assistive technologies diminished following repeated exposure

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Et. al., Geetha,. "Advanced Driver Assistance System using Convolutional Neural Network." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (2021): 896–905. http://dx.doi.org/10.17762/turcomat.v12i2.1098.

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Road sign recognition is an essential task in driving process to drive safely and to avoid accidents. Road sign recognition is not a simple task as there are many unfavorable factors such as bad weather, illumination, physical damage etc. The purpose of Road sign is to inform drivers and autonomous vehicles about current state of road and also provide them other important data for navigation. This paper aims to build Convolutional neural network (CNN) model to recognize road signs and to inform the drivers in advance for safe driving. The advantage of using Convolutional neural network (CNN) i

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Palac, Daniel, Iiona D. Scully, Rachel K. Jonas, John L. Campbell, Douglas Young, and David M. Cades. "Advanced Driver Assistance Systems (ADAS): Who’s Driving What and What’s Driving Use?" Proceedings of the Human Factors and Ergonomics Society Annual Meeting 65, no. 1 (2021): 1220–24. http://dx.doi.org/10.1177/1071181321651234.

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The emergence of vehicle technologies that promote driver safety and convenience calls for investigation of the prevalence of driver assistance systems as well as of their use rates. A consumer driven understanding as to why certain vehicle technology is used remains largely unexplored. We examined drivers’ experience using 13 different advanced driver assistance systems (ADAS) and several reasons that may explain rates of use through a nationally-distributed survey. Our analysis focused on drivers’ levels of understanding and trust with their vehicle’s ADAS as well as drivers’ perceived ease,

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Yoshizawa, Akira, and Hirotoshi Iwasaki. "Effects of a Preventive Warning Light System for Near-Miss Incidents." International Journal of Software Science and Computational Intelligence 10, no. 1 (2018): 65–79. http://dx.doi.org/10.4018/ijssci.2018010105.

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This article describes how the number of fatal traffic accidents has been decreasing in Japan because of recent safety technologies of vehicles, such as stiff cabins, antilock braking systems, and seat belts. Automated vehicles and advanced driver assistance systems can advance the trend. However, many traffic accidents occur on narrow streets in residential sections, where it is difficult for even advanced vehicles to drive safely. In this research, this paper utilizes a near-miss incident database to analyze driver gazing. The result showed that preventive warning systems are useful for avoi

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Bendel, Oliver. "Advanced Driver Assistance Systems and Animals." KI - Künstliche Intelligenz 28, no. 4 (2014): 263–69. http://dx.doi.org/10.1007/s13218-014-0332-1.

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18

Luce, Thomas. "Benefits of Advanced Driver Assistance Systems." ATZheavy duty worldwide 15, no. 4 (2022): 56. http://dx.doi.org/10.1007/s41321-022-1002-0.

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Yang, Kui, Christelle Al Haddad, Rakibul Alam, Tom Brijs, and Constantinos Antoniou. "Adaptive Intervention Algorithms for Advanced Driver Assistance Systems." Safety 10, no. 1 (2024): 10. http://dx.doi.org/10.3390/safety10010010.

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Advanced driver assistance systems (ADASs) have recently gained popularity as they assist vehicle operators in staying within safe boundaries, helping them thereby to prevent possible collisions. However, despite their success and development, most ADAS use common and deterministic warning thresholds for all drivers in all driving environments. This may occasionally lead to the issuance of annoying inadequate warnings, due to the possible differences between drivers, the changing environments and driver statuses, thus reducing their acceptance and effectiveness. To fill this gap, this paper pr

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Iacomussi, Paola. "Metrology Impact of Advanced Driver Assistance Systems." Electronic Imaging 2020, no. 16 (2020): 202–1. http://dx.doi.org/10.2352/issn.2470-1173.2020.16.avm-200.

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Metrological applications to road environment are usually focused on the characterization of the road, considering as measurands several characteristics related to the road as a whole or the performances of single components, like the road surface, lighting systems, active and/or passive signaling and obviously vehicles equipment. In current standards approach, driving on the road means to navigate ”visually” (for a human being driver), the characterizations are mostly photometric performances oriented for given reference conditions and reference observer (photometric observer observing the ro

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Paiva, Sara, Xabiel García Pañeda, Victor Corcoba, et al. "User Preferences in the Design of Advanced Driver Assistance Systems." Sustainability 13, no. 7 (2021): 3932. http://dx.doi.org/10.3390/su13073932.

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The transport network and mobility aspects are constantly changing, and major changes are expected in the coming years in terms of safety and sustainability purposes. In this paper, we present the main conclusions and analysis of data collected from a survey of drivers in Spain and Portugal regarding user preferences, highlighting the main functionalities and behavior that an advanced driver assistance system must have in order to grant it special importance on the road to prevent accidents and also to enable drivers to have a pleasant journey. Based on the results obtained from the survey, we

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S., Sanjay, Vasantha Kumar S., Mohamed Rifayee Hussain Z., Saravana Kumar K., Dineshkumar S., and Rathika P. D. "Advanced Driver Assistant System." Indian Journal of Computer Science 6, no. 3-4 (2021): 35. http://dx.doi.org/10.17010/ijcs/2021/v6/i3-4/165410.

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23

Kharchenko, I. K., I. G. Borovskoy, and E. А. Shelmina. "Modular Architecture of Advanced Driver Assistance Systems for Effective Traffic Sign Recognition." Vestnik NSU. Series: Information Technologies 21, no. 3 (2023): 56–71. http://dx.doi.org/10.25205/1818-7900-2023-21-3-56-71.

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Analysis of modern approaches to the implementation of driver assistance systems, as well as the implementation of the architecture of the driver assistance system, aimed at recognizing traffic signs at the maximum distance from it under difficult weather conditions, for early feedback to the driver. The paper considers the main signals used in the implementation and operation of the driver assistance system: data from the car's CAN bus, information from a GPS receiver, video fragments from a digital camera. The presented modular architecture uses the listed data sources for estimating the tra

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Abdul Razak, Siti Fatimah, Sumendra Yogarayan, Afizan Azman, Mohd Fikri Azli Abdullah, Anang Hudaya Muhamad Amin, and Mazrah Salleh. "Driver perceptions of advanced driver assistance systems: A case study." F1000Research 10 (November 8, 2021): 1122. http://dx.doi.org/10.12688/f1000research.73400.1.

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Background: Automobile manufacturers need to have an insight and understand how consumers, specifically drivers, respond to the advanced driver assistance systems (ADAS) technology in their manufactured vehicles. This study reveals drivers’ perceptions of Malaysia’s advanced driver assistance systems, which is currently lacking in the literature. So far, other studies have focused on countries that are unlike Malaysia’s multi-culture environment. Methods: A survey was designed and distributed using convenience sampling to obtain responses from licensed drivers. Questions included demographic a

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25

Ledezma, Agapito, Víctor Zamora, Óscar Sipele, M. Paz Sesmero, and Araceli Sanchis. "Implementing a Gaze Tracking Algorithm for Improving Advanced Driver Assistance Systems." Electronics 10, no. 12 (2021): 1480. http://dx.doi.org/10.3390/electronics10121480.

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Car accidents are one of the top ten causes of death and are produced mainly by driver distractions. ADAS (Advanced Driver Assistance Systems) can warn the driver of dangerous scenarios, improving road safety, and reducing the number of traffic accidents. However, having a system that is continuously sounding alarms can be overwhelming or confusing or both, and can be counterproductive. Using the driver’s attention to build an efficient ADAS is the main contribution of this work. To obtain this “attention value” the use of a Gaze tracking is proposed. Driver’s gaze direction is a crucial facto

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Kim, Jong-Hee, Chung-Su Lee, and Hakil Kim. "Mobile Advanced Driver Assistance System using OpenCL : Pedestrian Detection." Journal of the Institute of Electronics and Information Engineers 51, no. 10 (2014): 190–96. http://dx.doi.org/10.5573/ieie.2014.51.10.190.

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Salzmann, Falk, Sofiane Gadi, and Ingmar Gundlach. "Racing line optimisation for an advanced driver assistance system." International Journal of Vehicle Performance 8, no. 1 (2022): 46. http://dx.doi.org/10.1504/ijvp.2022.119437.

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Gundlach, Ingmar, Sofiane Gadi, and Falk Salzmann. "Racing line optimisation for an advanced driver assistance system." International Journal of Vehicle Performance 1, no. 1 (2021): 1. http://dx.doi.org/10.1504/ijvp.2021.10040646.

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Shackleton, Christopher, Rahul Kala, and Kevin Warwick. "Sensor-Based Trajectory Generation for Advanced Driver Assistance System." Robotics 2, no. 1 (2013): 19–35. http://dx.doi.org/10.3390/robotics2010019.

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Kim, Jongbae. "Efficient Vanishing Point Detection for Advanced Driver Assistance System." Advanced Science Letters 23, no. 5 (2017): 4115–18. http://dx.doi.org/10.1166/asl.2017.8277.

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31

Xu, Jiawei, Kun Guo, Federica Menchinelli, and Seop Hyeong Park. "Eye Fixation Location Recommendation in Advanced Driver Assistance System." Journal of Electrical Engineering & Technology 14, no. 2 (2019): 965–78. http://dx.doi.org/10.1007/s42835-019-00091-3.

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Velez, Gorka, Ainhoa Cortés, Marcos Nieto, Igone Vélez, and Oihana Otaegui. "A reconfigurable embedded vision system for advanced driver assistance." Journal of Real-Time Image Processing 10, no. 4 (2014): 725–39. http://dx.doi.org/10.1007/s11554-014-0412-3.

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33

Salve, Shubham, Tejal Jadhav, Siddhi Gajare, and Prof Hani Patil. "Advanced Driver Assistance System for Drivers using Machine Learning and Artificial Intelligence Techniques." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 3885–92. http://dx.doi.org/10.22214/ijraset.2022.43260.

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Abstract: Machine learning techniques have been used in order to predict the condition and emotion of a driver to provide information that will improve safety on the road. It is an application of artificial intelligence. The face, an important part of the body, conveys a lot of information. When a driver is in a state of fatigue, the facial expressions, e.g., the frequency of blinking and yawning, are different from those in the normal state. In this paper, we propose a system called “Advanced Driver Assistant System”, which detects the drivers fatigue status, such as yawning, blinking, and du

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Salve, Shubham, Tejal Jadhav, Siddhi Gajare, and Prof Hani Patil. "Advanced Driver Assistance System for Drivers using Machine Learning and Artificial Intelligence Techniques." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 3885–92. http://dx.doi.org/10.22214/ijraset.2022.43260.

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Abstract: Machine learning techniques have been used in order to predict the condition and emotion of a driver to provide information that will improve safety on the road. It is an application of artificial intelligence. The face, an important part of the body, conveys a lot of information. When a driver is in a state of fatigue, the facial expressions, e.g., the frequency of blinking and yawning, are different from those in the normal state. In this paper, we propose a system called “Advanced Driver Assistant System”, which detects the drivers fatigue status, such as yawning, blinking, and du

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35

V, Kiranmayee. "Driver Drowsiness Detection System." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (2021): 1226–31. http://dx.doi.org/10.22214/ijraset.2021.36124.

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Drowsiness of drivers are among the critical reasons for accidents. This can be a relatively smaller number still, as among the multiple causes that can lead to an accident. Drowsiness, in general, is not easy to measure unlike drugs and alcohol, which have tests and indicators that are available easily. In this paper, we are presenting a module for Advanced Driver Assistance System (ADAS) to reduce drowsiness related accidents. The system deals with automatic driver drowsiness detection based on visual information. We propose an algorithm to track, analyze and locate both the drivers eyes and

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Nasution, Surya Michrandi, and Fussy Mentari Dirgantara. "Pedestrian Detection System using YOLOv5 for Advanced Driver Assistance System (ADAS)." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 7, no. 3 (2023): 715–21. http://dx.doi.org/10.29207/resti.v7i3.4884.

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The technology in transportation is continuously developing due to reaching the self-driving vehicle. The need of detecting the situation around vehicles is a must to prevent accidents. It is not only limited to the conventional vehicle in which accident commonly happens, but also to the autonomous vehicle. In this paper, we proposed a detection system for recognizing pedestrians using a camera and minicomputer. The approach of pedestrian detection is applied using object detection method (YOLOv5) which is based on the Convolutional Neural Network. The model that we proposed in this paper is t

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Shokirov, Ozod G'aybulla o'g'li. "INTRODUCTION TO ADAS AND UNDERSTANDING THE IMPORTANCE OF ADVANCED DRIVER ASSISTANCE SYSTEM." Educational Research in Universal Sciences 2, no. 6 (2023): 257–59. https://doi.org/10.5281/zenodo.8104380.

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In this article, thye solution to thye problems of thye safety assessment of thye assistance systems for thye experienced driver driving thye car and thye specifications of thye information systems for thye assistance to thye driver have beyen developed.

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Vangi, Dario, Antonio Virga, and Michelangelo-Santo Gulino. "Adaptive intervention logic for automated driving systems based on injury risk minimization." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 234, no. 13 (2020): 2975–87. http://dx.doi.org/10.1177/0954407020931228.

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Performance improvement of advanced driver assistance systems yields two major benefits: increasingly rapid progress towards autonomous driving and a simultaneous advance in vehicle safety. Integration of multiple advanced driver assistance systems leads to the so-called automated driving system, which can intervene jointly on braking and steering to avert impending crashes. Nevertheless, obstacles such as stationary vehicles and buildings can interpose between the opponent vehicles and the working field of advanced driver assistance systems’ sensors, potentially resulting in an inevitable col

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Mahmudur Rahman, Md, Lesley Strawderman, and Daniel W. Carruth. "Effect of Driving Contexts on Driver Acceptance of Advanced Driver Assistance Systems." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 61, no. 1 (2017): 1944–48. http://dx.doi.org/10.1177/1541931213601965.

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Advanced Driver Assistance Systems (ADASs) has been developed to enhance driver performance and comfort and improve transportation safety. The potential benefits of these technologies include: reduction in the number of crashes, enhanced vehicle control for drivers, reduced environmental impact, etc. However, for these technologies to achieve their potential, drivers must accept them and use them appropriately in traffic. This study investigated the effect of driving contexts on driver acceptance, more specifically, on the intention to use such technologies. Three contextual factors were consi

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Abraham, Hillary, Bryan Reimer, and Bruce Mehler. "Advanced Driver Assistance Systems (ADAS): A Consideration of Driver Perceptions on Training, Usage & Implementation." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 61, no. 1 (2017): 1954–58. http://dx.doi.org/10.1177/1541931213601967.

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As the first phase of a larger project, drivers were recruited to drive for a month one of two different vehicles with a range of advanced driver assistance systems (ADAS). Training methods for introducing the systems and questionnaire and structured interview methods were tested for collecting driver perceptions and understanding of the technologies. Participant perceptions and selected observations are detailed.

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Neuhuber, N., P. Pretto, and B. Kubicek. "Interaction strategies with advanced driver assistance systems." Transportation Research Part F: Traffic Psychology and Behaviour 88 (July 2022): 223–35. http://dx.doi.org/10.1016/j.trf.2022.05.013.

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42

Gietelink, O., B. De Schutter, and M. Verhaegen. "PROBABILISTIC VALIDATION OF ADVANCED DRIVER ASSISTANCE SYSTEMS." IFAC Proceedings Volumes 38, no. 1 (2005): 97–102. http://dx.doi.org/10.3182/20050703-6-cz-1902.02068.

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Forster, Frank. "Heterogeneous Processors for Advanced Driver Assistance Systems." ATZelektronik worldwide 9, no. 1 (2014): 14–18. http://dx.doi.org/10.1365/s38314-014-0220-3.

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Itoh, Makoto. "Toward overtrust-free advanced driver assistance systems." Cognition, Technology & Work 14, no. 1 (2011): 51–60. http://dx.doi.org/10.1007/s10111-011-0195-2.

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Holzinger, Jürgen, and Erik Bogner. "Objective Assessment of Advanced Driver Assistance Systems." ATZ worldwide 119, no. 9 (2017): 16–19. http://dx.doi.org/10.1007/s38311-017-0089-x.

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46

Pañeda, Xabiel G., Roberto García Fernandez, David Melendi Palacio, Dan García Carrillo, David Martínez Alvarez, and Víctor Corcoba. "Testbed for industrial advanced driver assistance systems." IEEE Latin America Transactions 21, no. 5 (2023): 613–20. http://dx.doi.org/10.1109/tla.2023.10130832.

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47

Gerstmair, Michael, Martin Gschwandtner, Rainer Findenig, Oliver Lang, Alexander Melzer, and Mario Huemer. "Miniaturized Advanced Driver Assistance Systems: A Low-Cost Educational Platform for Advanced Driver Assistance Systems and Autonomous Driving." IEEE Signal Processing Magazine 38, no. 3 (2021): 105–14. http://dx.doi.org/10.1109/msp.2021.3051939.

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48

Kankoriya, Shreyas, and Manojkumar Khatal. "Advanced Driver-Assistance Systems: Features Journey for Tomorrow." International Journal of Advanced Engineering and Nano Technology 11, no. 1 (2024): 1–9. http://dx.doi.org/10.35940/ijaent.e7968.11010124.

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Intelligent associated vehicles (ICVs) are accepted to completely change people sooner rather than later by making the transportation more secure, cleaner and more agreeable. Albeit numerous models of ICVs have been created to demonstrate the idea of independent driving and the plausibility of further developing traffic effectiveness, there actually exists a critical hole prior to accomplishing large scale manufacturing of undeniable level ICVs. The goal of this study is to introduce an outline of both the cutting edge and future viewpoints of key necessary advances for future ICVs. It is a mo

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Shreyas, Kankoriya. "Advanced Driver-Assistance Systems: Features Journey for Tomorrow." International Journal of Advanced Engineering and Nano Technology (IJAENT) 11, no. 1 (2024): 1–9. https://doi.org/10.35940/ijaent.E7968.11010124.

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<strong>Abstract</strong>−Intelligent associated vehicles (ICVs) are accepted to completely change people sooner rather than later by making the transportation more secure, cleaner and more agreeable. Albeit numerous models of ICVs have been created to demonstrate the idea of independent driving and the plausibility of further developing traffic effectiveness, there actually exists a critical hole prior to accomplishing large scale manufacturing of undeniable level ICVs. The goal of this study is to introduce an outline of both the cutting edge and future viewpoints of key necessary adva

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Yang, Eunmok, and Okyeon Yi. "Enhancing Road Safety: Deep Learning-Based Intelligent Driver Drowsiness Detection for Advanced Driver-Assistance Systems." Electronics 13, no. 4 (2024): 708. http://dx.doi.org/10.3390/electronics13040708.

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Driver drowsiness detection is a significant element of Advanced Driver-Assistance Systems (ADASs), which utilize deep learning (DL) methods to improve road safety. A driver drowsiness detection system can trigger timely alerts like auditory or visual warnings, thereby stimulating drivers to take corrective measures and ultimately avoiding possible accidents caused by impaired driving. This study presents a Deep Learning-based Intelligent Driver Drowsiness Detection for Advanced Driver-Assistance Systems (DLID3-ADAS) technique. The DLID3-ADAS technique aims to enhance road safety via the detec

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