Journal articles: 'ADAS systems'

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Massow, Kay, and Ilja Radusch. "A Rapid Prototyping Environment for Cooperative Advanced Driver Assistance Systems." Journal of Advanced Transportation 2018 (2018): 1–32. http://dx.doi.org/10.1155/2018/2586520.

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Advanced Driver Assistance Systems (ADAS) were strong innovation drivers in recent years, towards the enhancement of traffic safety and efficiency. Today’s ADAS adopt an autonomous approach with all instrumentation and intelligence on board of one vehicle. However, to further enhance their benefit, ADAS need to cooperate in the future, using communication technologies. The resulting combination of vehicle automation and cooperation, for instance, enables solving hazardous situations by a coordinated safety intervention on multiple vehicles at the same point in time. Since the complexity of such cooperative ADAS grows with each vehicle involved, very large parameter spaces need to be regarded during their development, which necessitate novel development approaches. In this paper, we present an environment for rapidly prototyping cooperative ADAS based on vehicle simulation. Its underlying approach is either to bring ideas for cooperative ADAS through the prototyping stage towards plausible candidates for further development or to discard them as quickly as possible. This is enabled by an iterative process of refining and assessment. We reconcile the aspects of automation and cooperation in simulation by a tradeoff between precision and scalability. Reducing precise mapping of vehicle dynamics below the limits of driving dynamics enables simulating multiple vehicles at the same time. In order to validate this precision, we also present a method to validate the vehicle dynamics in simulation against real world vehicles.

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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 (September 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 considered: drivers’ fatigue level, time pressure, and time of day. Data collection was done using an online survey approach ( n = 386). Results found that fatigue and time pressure significantly affect drivers’ intention to use an ADAS. Results showed that drivers have increased intention to use an ADAS when they are fatigued or when there is no time pressure, as compared to a general driving condition.

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Oviedo-Trespalacios, Oscar, Jennifer Tichon, and Oliver Briant. "Is a flick-through enough? A content analysis of Advanced Driver Assistance Systems (ADAS) user manuals." PLOS ONE 16, no. 6 (June 17, 2021): e0252688. http://dx.doi.org/10.1371/journal.pone.0252688.

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Advanced Driver Assistance Systems (ADAS) are being developed and installed in increasing numbers. Some of the most popular ADAS include blind spot monitoring and cruise control which are fitted in the majority of new vehicles sold in high-income countries. With more drivers having access to these technologies, it is imperative to develop policy and strategies to guarantee the safe uptake of ADAS. One key issue is that ADAS education has been primarily centred on the user manual which are not widely utilised. Moreover, it is unclear if user manuals are an adequate source of education in terms of content and readability. To address this research gap, a content analysis was used to assess the differences in ADAS-related content and readability among the manuals of the highest selling vehicles in Australia. The qualitative findings showed that there are seven themes in the user manuals: differences between driving with and without ADAS, familiarisation requirements, operational limits of the ADAS, potential ADAS errors, behaviour adaptation warnings, confusion warnings, and malfunction warnings. The quantitative analysis found that some of the manuals require several years of education above the recommended for a universal audience (>8 years) to be understood. Additionally, there is a notable number of text diversions and infographics which could make comprehension of the user manual difficult. This investigation shows that there is a lack of standardisation of ADAS user manuals (in both content and delivery of information) which requires regulatory oversight. Driver ADAS education needs to be prioritised by policymakers and practitioners as smart technology continues to increase across the transport system. It seems that current strategies based on user manuals are insufficient to achieve successful adoption and safe use of these technologies.

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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 (June 19, 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 factor in understanding fatal distractions, as well as discerning when it is necessary to warn the driver about risks on the road. In this paper, a real-time gaze tracking system is proposed as part of the development of an ADAS that obtains and communicates the driver’s gaze information. The developed ADAS uses gaze information to determine if the drivers are looking to the road with their full attention. This work gives a step ahead in the ADAS based on the driver, building an ADAS that warns the driver only in case of distraction. The gaze tracking system was implemented as a model-based system using a Kinect v2.0 sensor and was adjusted on a set-up environment and tested on a suitable-features driving simulation environment. The average obtained results are promising, having hit ratios between 96.37% and 81.84%.

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Orlovska, J., C. Wickman, and R. Soderberg. "THE USE OF VEHICLE DATA IN ADAS DEVELOPMENT, VERIFICATION AND FOLLOW-UP ON THE SYSTEM." Proceedings of the Design Society: DESIGN Conference 1 (May 2020): 2551–60. http://dx.doi.org/10.1017/dsd.2020.322.

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AbstractAdvanced Driver Assistance Systems (ADAS) require a high level of interaction between the driver and the system, depending on driving context at a particular moment. Context-aware ADAS evaluation based on vehicle data is the most prominent way to assess the complexity of ADAS interactions. In this study, we conducted interviews with the ADAS development team at Volvo Cars to understand the role of vehicle data in the ADAS development and evaluation. The interviews’ analysis reveals strategies for improvement of current practices for vehicle data-driven ADAS evaluation.

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Classen, Sherrilene, Mary Jeghers, Jane Morgan-Daniel, Sandra Winter, Luther King, and Linda Struckmeyer. "Smart In-Vehicle Technologies and Older Drivers: A Scoping Review." OTJR: Occupation, Participation and Health 39, no. 2 (February 22, 2019): 97–107. http://dx.doi.org/10.1177/1539449219830376.

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In-vehicle technologies may decrease crash risk in drivers with age-related declines. Researchers determined the impact of in-vehicle information systems (IVIS) or advanced driving assistance systems (ADAS) on driving. Through a scoping review, the effect of IVIS or ADAS on older drivers’ convenience (i.e., meets one’s needs), comfort (i.e., physical or psychological ease), or safety (i.e., absence of errors or crashes) was examined. Researchers synopsized findings from 28 studies, including driving simulators and on-road environments. Findings indicated that IVIS or ADAS enhanced safety and mitigated age-related declines. Notably, IVIS may reduce cognitive workload, but may jeopardize safety if the systems are overly complicated. The ADAS enhanced safety and comfort by increasing speed control, lane maintenance, and braking responses. However, no studies addressed convenience. In-vehicle technologies may enhance safety and comfort while driving, if one’s cognitive workload is not compromised. Naturalistic studies are needed to elucidate the risks and benefits of IVIS and ADAS for older drivers.

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ENDACHEV, Denis V., Sergey V. BAKHMUTOV, Vladimir V. EVGRAFOV, and Nikolay P. MEZENTCEV. "ELECTRONIC SYSTEMS OF INTELLIGENT VEHICLES." Mechanics of Machines, Mechanisms and Materials 4, no. 53 (December 2020): 5–10. http://dx.doi.org/10.46864/1995-0470-2020-4-53-5-10.

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Modern automotive engineering is closely related to the implementation of information systems. In automobile transport, the range of such developments is considerably wide: from driver assistance systems (ADAS — Advanced Driver Assistance System) to full autopilot systems. The article provides a brief overview of the state of the problem and presents the main directions of development of the State Research Center of the Russian Federation FSUE “NAMI” in the field of ADAS and highly automated (unmanned) vehicles. Descriptions of on-board vehicle systems of a high level of automation are given developed by the State Research Center of the Russian Federation FSUE “NAMI” with the participation of manufacturers. The article also describes the key technologies of machine vision systems, test sites for highly automated vehicles.

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Елисеев, Н. "СИСТЕМЫ ADAS – УДОБСТВО И БЕЗОПАСНОСТЬ." ELECTRONICS: SCIENCE, TECHNOLOGY, BUSINESS 203, no. 2 (March 22, 2021): 102–7. http://dx.doi.org/10.22184/1992-4178.2021.203.2.102.107.

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Рассмотрены усовершенствованные системы помощи водителю (Advanced driver-assistance systems, ADAS). Приведена информация о структуре и функциях систем ADAS, а также примеры решений, предлагаемых для них рядом ведущих производителей.

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Abraham, Hillary, Bryan Reimer, and Bruce Mehler. "Learning to Use In-Vehicle Technologies: Consumer Preferences and Effects on Understanding." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 62, no. 1 (September 2018): 1589–93. http://dx.doi.org/10.1177/1541931218621359.

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Advanced Driver Assistance Systems (ADAS) have the potential to increase driver safety. However, driver misuse or failure to use ADAS could mitigate potential benefits. Appropriate training is one established method for encouraging proper use of technology. An online survey of 2364 respondents revealed significant differences between utilized and preferred methods for learning to use technologies. Drivers who learned through their preferred methods reported higher understanding and use of in-vehicle systems. Providing readily available methods of learning that align with learning preferences may improve safe use of ADAS.

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van den Beukel, Arie, Cornelie van Driel, Anika Boelhouwer, Nina Veders, and Tobias Heffelaar. "Assessment of Driving Proficiency When Drivers Utilize Assistance Systems—The Case of Adaptive Cruise Control." Safety 7, no. 2 (May 7, 2021): 33. http://dx.doi.org/10.3390/safety7020033.

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Driver assistance systems (ADAS), and especially those containing driving automation, change the role of drivers to supervisors who need to safeguard the system’s operation. Despite the aim to increase safety, the new tasks (supervision and intervention) may jeopardize safety. Consequently, safety officers address the need for specific training on ADAS. However, these tasks are not assessed in driver licensing today. Therefore, we developed a framework to assess in-practice driving proficiency when drivers utilize ADAS. This study evaluated whether the proposed framework is able to identify meaningful differences in driving proficiency between driving with and without assistance. We applied the framework to perform a qualitative assessment of driving proficiency with 12 novice drivers in a field experiment, comparable to a license test. The assistance system concerned Adaptive Cruise Control (ACC). The test showed that driving with ACC has a negative influence on self-initiated manoeuvres (especially lane changes) and sometimes led to improved adaptations to manoeuvres initiated by other road users (like merging in traffic). These results are in line with previous research and demonstrate the framework’s successfulness to assess novice drivers’ proficiency to utilize ADAS in road-traffic. Therewith, the proposed framework provides important means for driving instructors and examiners to address the safe operation of ADAS.

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Charness, Neil, Dustin Souders, Ryan Best, Nelson Roque, JongSung Yoon, and Cary Stothart. "Acceptance of Transportation Technologies by Aging Adults." Innovation in Aging 4, Supplement_1 (December 1, 2020): 555. http://dx.doi.org/10.1093/geroni/igaa057.1822.

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Abstract Older adults are at greater risk of death and serious injury in transportation crashes which have been increasing in older adult cohorts relative to younger cohorts. Can technology provide a safer road environment? Even if technology can mitigate crash risk, is it acceptable to older road users? We outline the results from several studies that tested 1) whether advanced driver assistance systems (ADAS) can improve older adult driving performance, 2) older adults’ acceptance of ADAS and Autonomous Vehicle (AV) systems, and 3) perceptions of value for ADAS systems, particularly for blind-spot detection systems. We found that collision avoidance warning systems improved older adult simulator driving performance, but not lane departure warning systems. In a young to middle-aged sample the factor “concern with AV” showed age effects with older drivers less favorable. Older drivers, however, valued an active blind spot detection system more than younger drivers.

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Pradhan, Anuj K., Ganesh Pai, Jaydeep Radadiya, Michael A. Knodler, Cole Fitzpatrick, and William J. Horrey. "Proposed Framework for Identifying and Predicting Operator Errors When using Advanced Vehicle Technologies." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 10 (July 30, 2020): 105–13. http://dx.doi.org/10.1177/0361198120938778.

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Advanced vehicle technologies include systems that are defined by the Society for Automotive Engineers as automated driving features or driver support features. The latter are increasingly available in late model vehicles in the form of advanced driver assistance systems (ADAS). ADAS features remove some responsibilities from drivers, but still depend on the drivers for safe operation. This can result in drivers committing errors while using ADAS, especially if their understanding of these systems, that is, their mental model, is incorrect. To understand how these systems could be used incorrectly it is necessary to have an insight into these mental models. One approach is to characterize users’ mental representations of systems based on the errors that they commit during system use. Such an approach necessitates a classification of potential errors that may be committed, and the underlying cognitive and behavioral reasons for such errors. To that end, a framework is proposed that can, among other goals, help predict user errors while using ADAS based on human factors and task analysis techniques. A methodology is detailed for mapping operator-system interactions using state diagrams, error identification techniques using task analysis are proposed, and a categorization scheme based on classic error taxonomies is described. This proposed framework can subsequently be expanded for error identification for a wider range and versions of ADAS, as well as for future automated driving systems (ADS). Moreover, the framework provides a systematic approach that can be used toward operationalizing mental models, forming the basis for structured user training, and for human-centered design of advanced vehicle technologies.

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Nylen, Ashley B., Michelle L. Reyes, Cheryl A. Roe, and Daniel V. McGehee. "Impacts on Driver Perceptions in Initial Exposure to ADAS Technologies." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 10 (May 18, 2019): 354–60. http://dx.doi.org/10.1177/0361198119847975.

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Advanced driver assistance systems (ADAS) offer great promise in improving the safety of our roadways. Although ADAS have rapidly entered the U.S. passenger vehicle market, little is known about driver understanding and attitudes toward ADAS, especially the impact of their initial exposure to the technologies. Whereas some ADAS may be easy to learn and use, others are more complex and have limitations that may not be obvious to the driver. The Technology Demonstration Study was conducted to evaluate how the ways in which drivers learn about ADAS affect their knowledge and perceptions of the technology. Two base learning methods were utilized for the study, both of which are traditional forms of learning for the average driver: reading the owner’s manual and making observations inside the vehicle. From these base learning methods, four learning protocols were developed, two of which included both methods. This paper investigates how drivers’ perceptions of usefulness, apprehension, and trust with regard to ADAS functionality were affected by initial exposure to the technology. Participants who observed ADAS during a demonstration drive had more positive perceptions relative to those who only read about them, particularly for ADAS that provide vehicle control.

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Ball, John E., and Bo Tang. "Machine Learning and Embedded Computing in Advanced Driver Assistance Systems (ADAS)." Electronics 8, no. 7 (July 2, 2019): 748. http://dx.doi.org/10.3390/electronics8070748.

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Wang, Tao, Yuzhi Chen, Xingchen Yan, Jun Chen, and Wenyong Li. "The Relationship between Bus Drivers’ Improper Driving Behaviors and Abnormal Vehicle States Based on Advanced Driver Assistance Systems in Naturalistic Driving." Mathematical Problems in Engineering 2020 (August 20, 2020): 1–12. http://dx.doi.org/10.1155/2020/9743504.

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In order to improve the adaptation of driver to the advanced driver assistance system (ADAS) and optimize the active safety control technology of vehicle under man-computer cooperative driving, this paper investigated the correlation between driver’s improper driving behaviors and abnormal vehicle states under the ADAS. Based on the warning data collected from the driver’s assistance warning system equipped on buses, the interaction between improper behaviors, between abnormal vehicle states, and between improper behaviors and abnormal vehicle states were quantitatively analyzed through the hierarchical clustering method and improved Apriori algorithm. The results showed that eye closure and yawn were high in concurrency (probability: 0.888) and interaction (average probability: 0.946); the interaction among lane departure, rapid acceleration, and rapid deceleration are frequent (average probability: 0.7224); eye closure (average probability: 0.452) and yawn (average probability: 0.444) are likely to induce abnormal vehicle states such as rapid acceleration and rapid deceleration. Some suggestions proposed based on the results are as follows. First, it is suggested that the ADAS should combine the warning modes of eye closure and yawn; second, when the driver closes eyes or yawns, the control of the ADAS over the lateral and longitudinal performance of vehicle should be enhanced; third, the extent of control by the ADAS should be determined according to the relationship probability; finally, the lateral control over the vehicle by the ADAS should be strengthened when there is a forward collision warning.

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Crump, Caroline, David Cades, Benjamin Lester, Scott Reed, Brandon Barakat, Laurene Milan, and Douglas Young. "Differing Perceptions of Advanced Driver Assistance Systems (ADAS)." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 60, no. 1 (September 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 to the technologies. However, when drivers were afforded more extensive experience, they reported a heightened appreciation of the systems—especially drivers who might benefit most from this assistance (e.g., distracted or older drivers).

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Dreger, Felix A., Joost C. F. de Winter, and Riender Happee. "How do drivers merge heavy goods vehicles onto freeways? A semi-structured interview unveiling needs for communication and support." Cognition, Technology & Work 22, no. 4 (October 4, 2019): 825–42. http://dx.doi.org/10.1007/s10111-019-00601-3.

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Abstract Freeway merging of heavy goods vehicles (HGV) is a safety–critical manoeuvre. However, at present, it is largely unknown how HGV drivers perceive and execute the merging manoeuvre, and how current advanced driver support and automation systems (ADAS) contribute. We performed semi-structured in-depth interviews with 15 HGV drivers to assess their visual and cognitive processes while merging, interactions with other road users, and attitudes towards ADAS as a basis for future support and automation system design. Results show that the reported execution of merging varies substantially between drivers. Drivers reported reliance on courtesy of other traffic but stated that car drivers are often causing conflicts, whereas other HGV drivers are more cooperative. Current ADAS were perceived as useful in general, with remarks about misuse and abundance of systems. We recommend the introduction of driver support and automation systems which facilitate cooperative behaviour and support effective communication.

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Lin, Tzu-Chi, Siyuan Ji, Charles E. Dickerson, and David Battersby. "Coordinated control architecture for motion management in ADAS systems." IEEE/CAA Journal of Automatica Sinica 5, no. 2 (March 2018): 432–44. http://dx.doi.org/10.1109/jas.2017.7510814.

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Inagaki, Toshiyuki, and Makoto Itoh. "Human’s Overtrust in and Overreliance on Advanced Driver Assistance Systems: A Theoretical Framework." International Journal of Vehicular Technology 2013 (April 16, 2013): 1–8. http://dx.doi.org/10.1155/2013/951762.

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This paper gives a theoretical framework to describe, analyze, and evaluate the driver’s overtrust in and overreliance on ADAS. Although “overtrust” and “overreliance” are often used as if they are synonyms, this paper differentiates the two notions rigorously. To this end, two aspects, (1) situation diagnostic aspect and (2) action selection aspect, are introduced. The first aspect is to describe overtrust, and it has three axes: (1-1) dimension of trust, (1-2) target object, and (1-3) chances of observation. The second aspect, (2), is to describe overreliance on the ADAS, and it has other three axes: (2-1) type of action selected, (2-2) benefits expected, and (2-3) time allowance for human intervention.

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Peiris, Sujanie, Janneke Berecki-Gisolf, Stuart Newstead, Bernard Chen, and Brian Fildes. "Development of a Methodology for Estimating the Availability of ADAS-Dependent Road Infrastructure." Sustainability 13, no. 17 (August 24, 2021): 9512. http://dx.doi.org/10.3390/su13179512.

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Advanced driver assist systems are being promoted with the expectation that enhanced driver support will mitigate road trauma. While these technologies are optimised for certain road and traffic conditions, not all roads across Australasia are equipped with ADAS-supportive infrastructure. This study developed a desk-top methodology for using road classes (disaggregated by remoteness levels) to estimate the presence of quality roads, road delineation and speed signage in Victoria, Australia. Aerial imagery and mapping data were used to assess a number of random locations based on a developed protocol. The methodology demonstrated that in Victoria, major and arterial roads across all remoteness levels had high-quality sealed surfaces but 42% of all remote roads were unsealed. Delineation (crucial for lane support systems) were absent across 73% of sub-arterial roads independent of remoteness, and absent across 96% of sub-arterial roads in regional and remote areas. Speed sign availability across remote and regional areas was sparse, with only 65% of all roads assessed having signage. Results are reflective of Victoria’s road funding model and consistent with on-road audits conducted by other researchers. This methodology enables the proportion ADAS-ready roads to be estimated so the benefits of ADAS technologies can be quantified and investments into ADAS-supportive infrastructure be readily allocated.

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Šucha, Matúš, Ralf Risser, and Kristýna Honzíčková. "Advanced Driver Assistant Systems Focused on Pedestrians’ Safety: A User Experience Approach." Sustainability 13, no. 8 (April 12, 2021): 4264. http://dx.doi.org/10.3390/su13084264.

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Globally, pedestrians represent 23% of all road deaths. Many solutions to protect pedestrians are proposed; in this paper, we focus on technical solutions of the ADAS–Advanced Driver Assistance Systems–type. Concerning the interaction between drivers and pedestrians, we want to have a closer look at two aspects: how to protect pedestrians with the help of vehicle technology, and how pedestrians–but also car drivers–perceive and accept such technology. The aim of the present study was to analyze and describe the experiences, needs, and preferences of pedestrians–and drivers–in connection with ADAS, or in other words, how ADAS should work in such a way that it would protect pedestrians and make walking more relaxed. Moreover, we interviewed experts in the field in order to check if, in the near future, the needs and preferences of pedestrians and drivers can be met by new generations of ADAS. A combination of different methods, specifically, an original questionnaire, on-the-spot interviewing, and expert interviews, was used to collect data. The qualitative data was analyzed using qualitative text analysis (clustering and categorization). The questionnaire for drivers was answered by a total of 70 respondents, while a total of 60 pedestrians agreed to complete questionnaires concerning pedestrian safety. Expert interviews (five interviews) were conducted by means of personal interviews, approximately one hour in duration. We conclude that systems to protect pedestrians–to avoid collisions of cars with pedestrians–are considered useful by all groups, though with somewhat different implications. With respect to the features of such systems, the considerations are very heterogeneous, and experimentation is needed in order to develop optimal systems, but a decisive argument put forward by some of the experts is that autonomous vehicles will have to be programmed extremely defensively. Given this argument, we conclude that we will need more discussion concerning typical interaction situations in order to find solutions that allow traffic to work both smoothly and safely.

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Davoli, Luca, Marco Martalò, Antonio Cilfone, Laura Belli, Gianluigi Ferrari, Roberta Presta, Roberto Montanari, et al. "On Driver Behavior Recognition for Increased Safety: A Roadmap." Safety 6, no. 4 (December 12, 2020): 55. http://dx.doi.org/10.3390/safety6040055.

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Advanced Driver-Assistance Systems (ADASs) are used for increasing safety in the automotive domain, yet current ADASs notably operate without taking into account drivers’ states, e.g., whether she/he is emotionally apt to drive. In this paper, we first review the state-of-the-art of emotional and cognitive analysis for ADAS: we consider psychological models, the sensors needed for capturing physiological signals, and the typical algorithms used for human emotion classification. Our investigation highlights a lack of advanced Driver Monitoring Systems (DMSs) for ADASs, which could increase driving quality and security for both drivers and passengers. We then provide our view on a novel perception architecture for driver monitoring, built around the concept of Driver Complex State (DCS). DCS relies on multiple non-obtrusive sensors and Artificial Intelligence (AI) for uncovering the driver state and uses it to implement innovative Human–Machine Interface (HMI) functionalities. This concept will be implemented and validated in the recently EU-funded NextPerception project, which is briefly introduced.

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Khan, Muhammad Qasim, and Sukhan Lee. "Gaze and Eye Tracking: Techniques and Applications in ADAS." Sensors 19, no. 24 (December 14, 2019): 5540. http://dx.doi.org/10.3390/s19245540.

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Tracking drivers’ eyes and gazes is a topic of great interest in the research of advanced driving assistance systems (ADAS). It is especially a matter of serious discussion among the road safety researchers’ community, as visual distraction is considered among the major causes of road accidents. In this paper, techniques for eye and gaze tracking are first comprehensively reviewed while discussing their major categories. The advantages and limitations of each category are explained with respect to their requirements and practical uses. In another section of the paper, the applications of eyes and gaze tracking systems in ADAS are discussed. The process of acquisition of driver’s eyes and gaze data and the algorithms used to process this data are explained. It is explained how the data related to a driver’s eyes and gaze can be used in ADAS to reduce the losses associated with road accidents occurring due to visual distraction of the driver. A discussion on the required features of current and future eye and gaze trackers is also presented.

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Mata-Carballeira, Óscar, Jon Gutiérrez-Zaballa, Inés del Campo, and Victoria Martínez. "An FPGA-Based Neuro-Fuzzy Sensor for Personalized Driving Assistance." Sensors 19, no. 18 (September 17, 2019): 4011. http://dx.doi.org/10.3390/s19184011.

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Advanced driving-assistance systems (ADAS) are intended to automatize driver tasks, as well as improve driving and vehicle safety. This work proposes an intelligent neuro-fuzzy sensor for driving style (DS) recognition, suitable for ADAS enhancement. The development of the driving style intelligent sensor uses naturalistic driving data from the SHRP2 study, which includes data from a CAN bus, inertial measurement unit, and front radar. The system has been successfully implemented using a field-programmable gate array (FPGA) device of the Xilinx Zynq programmable system-on-chip (PSoC). It can mimic the typical timing parameters of a group of drivers as well as tune these typical parameters to model individual DSs. The neuro-fuzzy intelligent sensor provides high-speed real-time active ADAS implementation and is able to personalize its behavior into safe margins without driver intervention. In particular, the personalization procedure of the time headway (THW) parameter for an ACC in steady car following was developed, achieving a performance of 0.53 microseconds. This performance fulfilled the requirements of cutting-edge active ADAS specifications.

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Dragoi, Beniamin. "Optimizing the front-end power solution for automotive ADAS systems." Advances in Science, Technology and Engineering Systems Journal 2, no. 3 (August 2017): 1647–53. http://dx.doi.org/10.25046/aj0203204.

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CHAABAN, Khaled, Mohamed SHAWKY, and Paul CRUBILLE. "A distributed embedded architecture for the evaluation of ADAS systems." IFAC Proceedings Volumes 42, no. 15 (2009): 237–44. http://dx.doi.org/10.3182/20090902-3-us-2007.0095.

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Ziyatdinov, R. R., and R. A. Biktimirov. "Automated system of recognition of road signs for ADAS systems." IOP Conference Series: Materials Science and Engineering 412 (October 23, 2018): 012081. http://dx.doi.org/10.1088/1757-899x/412/1/012081.

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Toropov, YE I., YU P. Trusov, A. S. Vashurin, and P. S. Moshkov. "Modern approach to testing and validation of driver assistance systems." Izvestia MGTU MAMI 1, no. 1 (2021): 63–72. http://dx.doi.org/10.31992/2074-0530-2021-47-1-63-72.

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The ever-increasing demands of vehicle safety are forcing vehicle manufacturers to develop ADAS systems (intelligent assistants). However, before starting sales of vehicle equipped with such functions it is necessary to fully test and validate the algorithms for the operation of electronic sys-tems in various conditions. Nowadays, the most popular methods for debugging and testing ADAS systems are based on model-, software- and hardware-in-the-loop simulation, which allows devel-opers to identify and eliminate errors in the early stages of product development, thus saving mon-ey. Despite the enormous advantages of simulation, full-scale tests remain the fundamental and final stage before the start of mass production. This is due to the fact that international and Russian government standards currently don’t provide the certification of electronic assistants through virtu-al tests. In this regard, manufacturers of measuring equipment and engineers face the task of devel-oping a new test setup that provides fixing the GNSS-position of vehicles in dynamic modes with centimeter accuracy, as well as making synchronized video recording. This article describes the ap-proach of the employees of the Nizhny Novgorod State Technical University n.a. R.E. Alekseev to the solution of this problem: the design of the concept and its implementation on a light commercial vehicle with ADAS systems. To fix coordinates with centimeter accuracy we have used Racelogic Vbox 100 Hz, operating in the RTK-mode, synchronized video recording was made using Racelogic Video Vbox Pro, which includes 4 wide-angle cameras. The approbation of the measuring installation was carried out during testing the following systems: a blind spot detector, a lane departure warning and a parking space search assistant. The article presents a number of graphs of the functioning of one of the systems.

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Hoyos, Christian, Benjamin D. Lester, Caroline Crump, David M. Cades, and Douglas Young. "Consumer perceptions, understanding, and expectations of Advanced Driver Assistance Systems (ADAS) and vehicle automation." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 62, no. 1 (September 2018): 1888–92. http://dx.doi.org/10.1177/1541931218621429.

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Consumers are faced with an increasingly complex decision process as novel safety technologies become commonplace in new vehicles. Consumers’ knowledge of these systems is potentially limited given the recent introduction and constant evolution of ADAS. We examined consumers’ understanding and perceptions of ADAS and vehicle automation in a national survey. Our analysis focused on consumers’ understanding of how certain driving tasks that can be automated (e.g., steering, braking, navigation, etc.) maps onto proposed levels of vehicle automation. Additionally, we report what sources of information and methods of education consumers prefer to utilize when learning about new safety systems. These data can inform new driver training, public policy and transportation goals of how best to educate the motoring public about new vehicle technologies and the capabilities of next-generation automated vehicles as well as facilitate public acceptance of higher levels of vehicle automation.

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Kim, Hyungkyu, Byungkon Kim, and Doyoung Jung. "Effect Evaluation of Forward Collision Warning System Using IoT Log and Virtual Driving Simulation Data." Applied Sciences 11, no. 13 (June 29, 2021): 6045. http://dx.doi.org/10.3390/app11136045.

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Advanced driver-assistance systems (ADAS) are primarily known for their positive impact in improving the safety of drivers. Previous studies primarily analyzed the positive effects of ADAS with short-term experiments and accident data without considering the long-term changes in drivers’ safety perception. The human factor is the most dominant among factors that cause traffic accidents, and safety effect evaluation should be performed considering changes in human errors. To this end, this study classified the safety effect of ADAS-forward collision warning (FCW) on taxi drivers in Seoul into behavioral control and attitude change to perform analysis on respective factors. With regard to behavioral control, virtual driving simulation was used to analyze the reaction time of drivers and deceleration rate, and for attitude change, autoregressive integrated moving average (ARIMA) time series analysis was employed to predict the long-term perception change of drivers. The analysis results indicated that, in terms of behavioral control, ADAS-FCW reduces the cognitive reaction time of drivers in risk situations on the road, similar to the findings in previous studies. However, in terms of attitude change, ADAS-FCW has the adverse long-term effect of increasing violations in maintaining safety distance in the case of nighttime-drivers under 60 years old. As can be seen from these results, new technologies in the road safety arena can have a short-term effect of improving safety with behavioral control but may have a negative impact in the long term. The results of this study are expected to provide a theoretical basis for reference in the safety evaluation of ADAS and traffic safety facilities.

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Zhou, Jinwei, and Luigi del Re. "Reduced Complexity Safety Testing for ADAS & ADF." IFAC-PapersOnLine 50, no. 1 (July 2017): 5985–90. http://dx.doi.org/10.1016/j.ifacol.2017.08.1261.

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Lindgren, Thomas, Vaike Fors, Sarah Pink, and Katalin Osz. "Anticipatory experience in everyday autonomous driving." Personal and Ubiquitous Computing 24, no. 6 (May 6, 2020): 747–62. http://dx.doi.org/10.1007/s00779-020-01410-6.

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AbstractIn this paper, we discuss how people’s user experience (UX) of autonomous driving (AD) cars can be understood as a shifting anticipatory experience, as people experience degrees of AD through evolving advanced driver assistance systems (ADAS) in their everyday context. We draw on our ethnographic studies of five families, who had access to AD research cars with evolving ADAS features in their everyday lives for a duration of 1½ years. Our analysis shows that people gradually adopt AD cars, through a process that involves anticipating if they can trust them, what the ADAS features will do and what the longer-term technological possibilities will be. It also showed that this anticipatory UX occurs within specific socio-technical and environmental circumstances, which could not be captured easily in experimental settings. The implication is that studying anticipation offers us new insights into how people adopt AD in their everyday commute driving.

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Orlovska, Julia, Fjollë Novakazi, Bligård Lars-Ola, MariAnne Karlsson, Casper Wickman, and Rikard Söderberg. "Effects of the driving context on the usage of Automated Driver Assistance Systems (ADAS) -Naturalistic Driving Study for ADAS evaluation." Transportation Research Interdisciplinary Perspectives 4 (March 2020): 100093. http://dx.doi.org/10.1016/j.trip.2020.100093.

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Voinea, Gheorghe-Daniel, Cristian Cezar Postelnicu, Mihai Duguleana, Gheorghe-Leonte Mogan, and Radu Socianu. "Driving Performance and Technology Acceptance Evaluation in Real Traffic of a Smartphone-Based Driver Assistance System." International Journal of Environmental Research and Public Health 17, no. 19 (September 28, 2020): 7098. http://dx.doi.org/10.3390/ijerph17197098.

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Technological advances are changing every aspect of our lives, from the way we work, to how we learn and communicate. Advanced driver assistance systems (ADAS) have seen an increased interest due to the potential of ensuring a safer environment for all road users. This study investigates the use of a smartphone-based ADAS in terms of driving performance and driver acceptance, with the aim of improving road safety. The mobile application uses both cameras of a smartphone to monitor the traffic scene and the driver’s head orientation, and offers an intuitive user interface that can display information in a standard mode or in augmented reality (AR). A real traffic experiment consisting of two driving conditions (a baseline scenario and an ADAS scenario), was conducted in Brasov, Romania. Objective and subjective data were recorded from twenty-four participants with a valid driver’s license. Results showed that the use of the ADAS influences the driving performance, as most of them adopted an increased time headway and lower mean speeds. The technology acceptance model (TAM) questionnaire was used to assess the users’ acceptance of the proposed driver assistance system. The results showed significant interrelations between acceptance factors, while the hierarchical regression analysis indicates that the variance of behavioral intention (BI) can be predicted by attitude toward behavior.

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Park, Jungme, and Wenchang Yu. "A Sensor Fused Rear Cross Traffic Detection System Using Transfer Learning." Sensors 21, no. 18 (September 9, 2021): 6055. http://dx.doi.org/10.3390/s21186055.

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Recent emerging automotive sensors and innovative technologies in Advanced Driver Assistance Systems (ADAS) increase the safety of driving a vehicle on the road. ADAS enhance road safety by providing early warning signals for drivers and controlling a vehicle accordingly to mitigate a collision. A Rear Cross Traffic (RCT) detection system is an important application of ADAS. Rear-end crashes are a frequently occurring type of collision, and approximately 29.7% of all crashes are rear-ended collisions. The RCT detection system detects obstacles at the rear while the car is backing up. In this paper, a robust sensor fused RCT detection system is proposed. By combining the information from two radars and a wide-angle camera, the locations of the target objects are identified using the proposed sensor fused algorithm. Then, the transferred Convolution Neural Network (CNN) model is used to classify the object type. The experiments show that the proposed sensor fused RCT detection system reduced the processing time 15.34 times faster than the camera-only system. The proposed system has achieved 96.42% accuracy. The experimental results demonstrate that the proposed sensor fused system has robust object detection accuracy and fast processing time, which is vital for deploying the ADAS system.

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Wang, Yugang, and Nengchao Lyu. "A Car-Following Model Based on Safety Margin considering ADAS and Driving Experience." Advances in Civil Engineering 2021 (February 19, 2021): 1–10. http://dx.doi.org/10.1155/2021/6619137.

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Existing studies had shown that advanced driver assistance systems (ADAS) and driver individual characteristics can significantly affect driving behavior. Therefore, it is necessary to consider these factors when building the car-following model. In this study, we established a car-following model based on risk homeostasis theory, which uses safety margin (SM) as the risk level quantization parameter. Firstly, three-way Analysis of Variance (ANOVA) was used to analyze the influencing factors of car-following behavior. The results showed that ADAS and driving experience have a significant effect on the drivers’ car-following behavior. Then, according to these two significant factors, the car-following model was established. The statistical method was used to calibrate the parameter reaction response τ. Other four parameters (SMDL, SMDH, α1, and α2) were calibrated using a classical genetic algorithm, and the effects of ADAS and driving experience in these four parameters were analyzed using T-test. Finally, the proposed model was compared with the GHR model, and the result showed that the proposed model has a smaller Root Mean Square Error (RMSE) than the GHR model. The proposed model is a method of simulating different driving behaviors that are affected by ADAS and individual characteristics. Considering more driver individual characteristics, such as driving style, is the future research goal.

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Ziyatdinov, R. R., and R. A. Biktimirov. "Application of neural networks in object recognition tasks for ADAS systems." IOP Conference Series: Materials Science and Engineering 570 (August 15, 2019): 012107. http://dx.doi.org/10.1088/1757-899x/570/1/012107.

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Iacomussi, Paola. "Metrology Impact of Advanced Driver Assistance Systems." Electronic Imaging 2020, no. 16 (January 26, 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 road from assigned points of view, with given spectral sensitivity). But considering the present and future technological trends and knowledge on visual performances, characterizations based on only photometric quantities in reference conditions as described in the current standards would be not fully suitable, even for human driver visual needs. Nowadays research on components and systems for advanced driver assistance are evolving, following different paths toward different solutions: it is not possible, nor useful to define strict constraints as it has been done previously for road applications measurements. The paper presents the current situation of metrological characterization of road environment and components, on laboratory and on site using mobile high efficiency laboratories, and suggests to use ADAS (Advanced Driver Assistance System) for diffuse mapping of road characteristics for a better understanding of the road environment and maintenance. The suggestion has the additional advantage of minimizing measurement costs, but for its full applicability, the reliability and metrological performances of installed devices and of the measurements performed by ADAS are a priority.

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Mulyanto, Agus, Wisnu Jatmiko, Petrus Mursanto, Purwono Prasetyawan, and Rohmat Indra Borman. "A New Indonesian Traffic Obstacle Dataset and Performance Evaluation of YOLOv4 for ADAS." Journal of ICT Research and Applications 14, no. 3 (March 31, 2021): 286–98. http://dx.doi.org/10.5614/itbj.ict.res.appl.2021.14.3.6.

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Intelligent transport systems (ITS) are a promising area of studies. One implementation of ITS are advanced driver assistance systems (ADAS), involving the problem of obstacle detection in traffic. This study evaluated the YOLOv4 model as a state-of-the-art CNN-based one-stage detector to recognize traffic obstacles. A new dataset is proposed containing traffic obstacles on Indonesian roads for ADAS to detect traffic obstacles that are unique to Indonesia, such as pedicabs, street vendors, and bus shelters, and are not included in existing datasets. This study established a traffic obstacle dataset containing eleven object classes: cars, buses, trucks, bicycles, motorcycles, pedestrians, pedicabs, trees, bus shelters, traffic signs, and street vendors, with 26,016 labeled instances in 7,789 images. A performance analysis of traffic obstacle detection on Indonesian roads using the dataset created in this study was conducted using the YOLOv4 method.

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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 (September 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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Aung, Nyothiri, Weidong Zhang, Sahraoui Dhelim, and Yibo Ai. "Accident Prediction System Based on Hidden Markov Model for Vehicular Ad-Hoc Network in Urban Environments." Information 9, no. 12 (December 7, 2018): 311. http://dx.doi.org/10.3390/info9120311.

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With the emergence of autonomous vehicles and internet of vehicles (IoV), future roads of smart cities will have a combination of autonomous and automated vehicles with regular vehicles that require human operators. To ensure the safety of the road commuters in such a network, it is imperative to enhance the performance of Advanced Driver Assistance Systems (ADAS). Real-time driving risk prediction is a fundamental part of an ADAS. Many driving risk prediction systems have been proposed. However, most of them are based only on vehicle’s velocity. But in most of the accident scenarios, other factors are also involved, such as weather conditions or driver fatigue. In this paper, we proposed an accident prediction system for Vehicular ad hoc networks (VANETs) in urban environments, in which we considered the crash risk as a latent variable that can be observed using multi-observation such as velocity, weather condition, risk location, nearby vehicles density and driver fatigue. A Hidden Markov Model (HMM) was used to model the correlation between these observations and the latent variable. Simulation results showed that the proposed system has a better performance in terms of sensitivity and precision compared to state of the art single factor schemes.

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Bella, Francesco, and Manuel Silvestri. "VEHICLE–PEDESTRIAN INTERACTIONS INTO AND OUTSIDE OF CROSSWALKS: EFFECTS OF DRIVER ASSISTANCE SYSTEMS." Transport 36, no. 2 (April 30, 2021): 98–109. http://dx.doi.org/10.3846/transport.2021.14739.

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This study aimed to analyse the driver’s behaviour during the interaction with a pedestrian crossing into and outside the zebra crossing, and evaluate the effectiveness of two kinds of Advanced Driver Assistance System (ADAS) that provided to the driver an auditory alert, and a visual alert to detect the pedestrian. 42 participants joined the experiment conducted using the fixed-base driving simulator of the Department of Engineering (Roma Tre University). They experienced different crossing conditions (legal and illegal) and ADAS conditions (no ADAS, visual warning and auditory warning) in an urban scenario. The parameters Time-To-Arrive (TTA) and Speed Reduction Time (SRT) were obtained from the drivers’ speed profiles in the last 150 m in advance of the conflict point with the pedestrian. Results clearly showed the criticality of illegal crossings. When the pedestrian crossed outside of the crosswalk, the highest number of collision occurred and the ANalysis Of VAriance (ANOVA) returned significant effects on both the dependent variables TTA and SRT, highlighting the higher criticality of the vehicle–pedestrian interaction and the more abrupt yielding manoeuvre. Positive effects (the vehicle–pedestrian interaction was less critical and the yielding manoeuvre was smoother) emerged for both the driver assistance systems, although not statistically significant. Besides, both the driver assistance systems positively affected the behaviour of the average cautious drivers. No significant effects of the warning systems were recorded on the aggressive drivers, which because of their behavioural characteristics ignored the warning alarm. In addition, no significant effects of the warning systems were recorded for the very cautious drivers, which adjusted their behaviour even before the alarm trigger. Finally, the outcomes of the questionnaire submitted to the participants highlighted the clear preference for the auditory warning, probably because of the different physical stimuli that are solicited by the warning signal. The results confirm that adequate pedestrian paths should be planned to avoid jaywalker conditions, which induce the driver to assume critical driving behaviour and provide useful findings of the effectiveness of driver assistance systems for pedestrian detection.

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Lee, Kyu-haeng. "ADAS: Adaptive Switching between Full-Duplex and MU-MIMO for Wi-Fi Networks." Wireless Communications and Mobile Computing 2019 (January 9, 2019): 1–10. http://dx.doi.org/10.1155/2019/1825234.

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Full-duplex (FD) and multiuser multi-input multioutput (MU-MIMO) approaches have been attracting much attention as core technologies of next-generation Wi-Fi systems, since they have vast potential to improve communication performance. In particular, the system throughput can be significantly increased if both technologies are used in harmony, based on a solid understanding of the characteristics of each technology. To realise this, it is essential and important for a node using both technologies to acquire a priori channel and queue information about the receiving nodes. Unfortunately, it is very challenging to obtain this information in Wi-Fi networks in which there are no separate channels or proper protocols. In this paper, a new MAC protocol for Wi-Fi networks is proposed, called ADAS, which selects the transmission strategy that best matches the given network environment. By fully utilising the conventional CSI acquisition protocol, an ADAS access point (AP) effectively obtains the necessary information and exploits it appropriately, in order to yield higher throughput gains. Through extensive Matlab simulations, the author proves that ADAS maintains high system throughputs for dynamic network changes.

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Yan, Xinping, Rui Zhang, Jie Ma, and Yulin Ma. "Considering Variable Road Geometry in Adaptive Vehicle Speed Control." Mathematical Problems in Engineering 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/617879.

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Adaptive vehicle speed control is critical for developing Advanced Driver Assistance Systems (ADAS). Vehicle speed control considering variable road geometry has become a hotspot in ADAS research. In this paper, first, an exploration of intrinsic relationship between vehicle operation and road geometry is made. Secondly, a collaborative vehicle coupling model, a road geometry model, and an AVSC, which can respond to variable road geometry in advance, are developed. Then, based onH∞control method and the minimum energy principle, a performance index is specified by a cost function for the proposed AVSC, which can explicitly consider variable road geometry in its optimization process. The proposed AVSC is designed by the Hamilton-Jacobi Inequality (HJI). Finally, simulations are carried out by combining the vehicle model with the road geometry model, in an aim of minimizing the performance index of the AVSC. Analyses of the simulation results indicate that the proposed AVSC can automatically and effectively regulate speed according to variable road geometry. It is believed that the proposed AVSC can be used to improve the economy, comfort, and safety effects of current ADAS.

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Farag, Wael. "A lightweight vehicle detection and tracking technique for advanced driving assistance systems." Journal of Intelligent & Fuzzy Systems 39, no. 3 (October 7, 2020): 2693–710. http://dx.doi.org/10.3233/jifs-190634.

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In this paper, an advanced-and-reliable vehicle detection-and-tracking technique is proposed and implemented. The Real-Time Vehicle Detection-and-Tracking (RT_VDT) technique is well suited for Advanced Driving Assistance Systems (ADAS) applications or Self-Driving Cars (SDC). The RT_VDT is mainly a pipeline of reliable computer vision and machine learning algorithms that augment each other and take in raw RGB images to produce the required boundary boxes of the vehicles that appear in the front driving space of the car. The main contribution of this paper is the careful fusion of the employed algorithms where some of them work in parallel to strengthen each other in order to produce a precise and sophisticated real-time output. In addition, the RT_VDT provides fast enough computation to be embedded in CPUs that are currently employed by ADAS systems. The particulars of the employed algorithms together with their implementation are described in detail. Additionally, these algorithms and their various integration combinations are tested and their performance is evaluated using actual road images, and videos captured by the front-mounted camera of the car as well as on the KITTI benchmark with 87% average precision achieved. The evaluation of the RT_VDT shows that it reliably detects and tracks vehicle boundaries under various conditions.

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Geerts, Hugo, and Athan Spiros. "Simulating the Effects of Common Comedications and Genotypes on Alzheimer’s Cognitive Trajectory Using a Quantitative Systems Pharmacology Approach." Journal of Alzheimer's Disease 78, no. 1 (October 27, 2020): 413–24. http://dx.doi.org/10.3233/jad-200688.

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Background: Many Alzheimer’s disease patients in clinical practice are on polypharmacy for treatment of comorbidities. Objective: While pharmacokinetic interactions between drugs have been relatively well established with corresponding treatment guidelines, many medications and common genotype variants also affect central brain circuits involved in cognitive trajectory, leading to complex pharmacodynamic interactions and a large variability in clinical trials. Methods: We applied a mechanism-based and ADAS-Cog calibrated Quantitative Systems Pharmacology biophysical model of neuronal circuits relevant for cognition in Alzheimer’s disease, to standard-of-care cholinergic therapy with COMTVal158Met, 5-HTTLPR rs25531, and APOE genotypes and with benzodiazepines, antidepressants, and antipsychotics, all together 9,585 combinations. Results: The model predicts a variability of up to 14 points on ADAS-Cog at baseline (COMTVV 5-HTTLPRss APOE 4/4 combination is worst) and a four-fold range for the rate of progression. The progression rate is inversely proportional to baseline ADAS-Cog. Antidepressants, benzodiazepines, first-generation more than second generation, and most antipsychotics with the exception of aripiprazole worsen the outcome when added to standard-of-care in mild cases. Low dose second-generation benzodiazepines revert the negative effects of risperidone and olanzapine, but only in mild stages. Non APOE4 carriers with a COMTMM and 5HTTLPRLL are predicted to have the best cognitive performance at baseline but deteriorate somewhat faster over time. However, this effect is significantly modulated by comedications. Conclusion: Once these simulations are validated, the platform can in principle provide optimal treatment guidance in clinical practice at an individual patient level, identify negative pharmacodynamic interactions with novel targets and address protocol amendments in clinical trials.

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Мякочкин, Ю., and М. Бирюков. "Миллиметровые радары АО «ПКК Миландр» для применения на автотранспорте и в системах безопасности." ELECTRONICS: SCIENCE, TECHNOLOGY, BUSINESS 189, no. 8 (October 4, 2019): 90–95. http://dx.doi.org/10.22184/1992-4178.2019.189.8.90.95.

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Рассмотрен ряд приборов группы компаний АО «ПКК Миландр» – автомобильный радар, блок управления безопасностью автомобиля, однолучевой радар и СВЧ-приемопередатчики, – предназначенных для реализации функций интеллектуальной системы помощи водителю (Advanced Driver-Assistance Systems, ADAS) и построения систем контроля дорожного трафика, охраны периметра, определения скорости и дальности объектов.

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Rowell, J. Martin. "Applying Map Databases to Advanced Navigation and Driver Assistance Systems." Journal of Navigation 54, no. 3 (September 2001): 355–63. http://dx.doi.org/10.1017/s0373463301001503.

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Navigation map databases have evolved during the last 10 years in coverage, content and accuracy to such a degree that they are being used (and considered) for applications beyond vehicle navigation. For a number of advanced driver assistance systems (ADAS) in Europe and North America, the navigation maps are being enhanced to offer new functions. This will complement dynamic route guidance, with location referencing, descriptions of delays, floating car data and re-routeing, all requiring developments in traffic management, road capacity balancing and routing algorithms.

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Ntasiou, Nikoletta, Giannis Adamos, and Eftihia Nathanail. "Exploring the Effects of Psychological Factors on the Use of Navigation Systems While Driving." Transport and Telecommunication Journal 22, no. 1 (January 29, 2021): 109–15. http://dx.doi.org/10.2478/ttj-2021-0009.

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Abstract The ever-increasing use of private vehicles makes Advanced Driver Assistance Systems (ADAS) more necessary as they improve users’ convenience, safety and travel time. Although these systems offer significant advantages, they call into question the traditional role of users, making the psychology of drivers towards these technologies a necessary factor for their adoption. The purpose of this paper is to investigate the effects of psychological factors on the use of one of the most widely used ADAS, the Global Navigation Satellite Systems (GNSS). Towards this direction, a literature review was conducted to identify the factors that influence drivers’ behavior and the psychology of drivers towards new technologies. Furthermore, a questionnaire survey was organized in Greece, based on the Theory of Planned Behavior, including additional variables, which were identified in the literature, such as technophilia, trust in technology and endorsement. From the data collected, models predicting the behavior of drivers were developed through structural equation modelling, concerning the use of navigation systems in both urban and interurban networks. The findings of the research reveal that the intention to use a navigation system is determined by various factors such as behavioral beliefs about its usage, normative beliefs and technophilia. The actual use of a navigation system depends to some extent on this intention.

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Raj, Thinal, Fazida Hanim Hashim, Aqilah Baseri Huddin, Mohd Faisal Ibrahim, and Aini Hussain. "A Survey on LiDAR Scanning Mechanisms." Electronics 9, no. 5 (April 30, 2020): 741. http://dx.doi.org/10.3390/electronics9050741.

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In recent years, light detection and ranging (LiDAR) technology has gained huge popularity in various applications such as navigation, robotics, remote sensing, and advanced driving assistance systems (ADAS). This popularity is mainly due to the improvements in LiDAR performance in terms of range detection, accuracy, power consumption, as well as physical features such as dimension and weight. Although a number of literatures on LiDAR technology have been published earlier, not many has been reported on the state-of-the-art LiDAR scanning mechanisms. The aim of this article is to review the scanning mechanisms employed in LiDAR technology from past research works to the current commercial products. The review highlights four commonly used mechanisms in LiDAR systems: Opto-mechanical, electromechanical, micro-electromechanical systems (MEMS), and solid-state scanning. The study reveals that electro-mechanical scanning is the most prominent technology in use today. The commercially available 1D time of flight (TOF) LiDAR instrument is currently the most attractive option for conversion from 1D to 3D LiDAR system, provided that low scanning rate is not an issue. As for applications with low size, weight, and power (SWaP) requirements, MEMS scanning is found to be the better alternative. MEMS scanning is by far the more matured technology compared to solid-state scanning and is currently given great emphasis to increase its robustness for fulfilling the requirements of ADAS applications. Finally, solid-state LiDAR systems are expected to fill in the gap in ADAS applications despite the low technology readiness in comparison to MEMS scanners. However, since solid-state scanning is believed to have superior robustness, field of view (FOV), and scanning rate potential, great efforts are given by both academics and industries to further develop this technology.

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Journal articles on the topic 'ADAS systems'

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