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Relevant bibliographies by topics / Advanced Driver Assistance Systems (ADAS)

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Academic literature on the topic 'Advanced Driver Assistance Systems (ADAS)'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Advanced Driver Assistance Systems (ADAS)"

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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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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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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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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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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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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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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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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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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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Paiva, Sara, Xabiel García Pañeda, Victor Corcoba, Roberto García, Próspero Morán, Laura Pozueco, Marina Valdés, and Covadonga del Camino. "User Preferences in the Design of Advanced Driver Assistance Systems." Sustainability 13, no. 7 (April 2, 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 developed and present a working prototype for an advanced driver assistance system (ADAS), its architecture and rules systems that allowed us to create and test some scenarios in a real environment.

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Dissertations / Theses on the topic "Advanced Driver Assistance Systems (ADAS)"

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Aziz, Tabinda. "Empirical Analyses of Human-Machine Interactions focusing on Driver and Advanced Driver Assistance Systems." 京都大学 (Kyoto University), 2015. http://hdl.handle.net/2433/195975.

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Mattsson, David. "ADAS : A simulation study comparing two safety improving Advanced Driver Assistance Systems." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-85151.

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Driving is a high-risk adventure which many enjoy on a daily basis. The driving task is highly complex, ever-changing, and one which requires continuous attention and rapid decision making. It is a task that is not without risk, where the cost to reach the desired destination can be too great – your life could be at stake. Driving is not without incidents. Rear-end collision is a common problem in the Swedish traffic environment, with over 100 police-reported individual incidents per year. The amount of rear-end collisions can be hypothetically reduced by introducing new technology in the driver’s vehicle, technology which attempts to improve the driver’s safety driving. This technology is called Advanced Driver Assistance Systems – ADAS. In this study two ADAS were evaluated in a driving simulator study: An Adaptive Cruise Control (ACC) which operates on both hazardous and non-hazardous events, and a Collision Warning System (CWS) which operates solely on non-hazardous events. Both of these ADAS function to guard against risky driving and are based on the assumption that drivers will not act in such a manner that they would willingly reduce the effectiveness of the system. A within-subjects simulation study was conducted where participants drove under three conditions: 1) with an adaptive cruise controller, 2) a frontal rear-end collision warning system ADAS, and 3) unaided, in order to investigate differences between the three driving conditions. Particular focus was on whether the two ADAS improved driving safety. The study results indicate that driving enhanced by the two ADAS made the participating drivers drive less safely.

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Andersson, Naesseth Christian. "Vision and Radar Sensor Fusion for Advanced Driver Assistance Systems." Thesis, Linköpings universitet, Reglerteknik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-94222.

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The World Health Organization predicts that by the year 2030, road traffic injuries will be one of the top five leading causes of death. Many of these deaths and injuries can be prevented by driving cars properly equipped with state-of-the-art safety and driver assistance systems. Some examples are auto-brake and auto-collision avoidance which are becoming more and more popular on the market today. A recent study by a Swedish insurance company has shown that on roadswith speeds up to 50 km/h an auto-brake system can reduce personal injuries by up to 64 percent. In fact in an estimated 40 percent of crashes, the auto-brake reduced the effects to the degree that no personal injury was sustained. It is imperative that these so called Advanced Driver Assistance Systems, to be really effective, have good situational awareness. It is important that they have adequate information of the vehicle’s immediate surroundings. Where are other cars, pedestrians or motorcycles relative to our own vehicle? How fast are they driving and in which lane? How is our own vehicle driving? Are there objects in the way of our own vehicle’s intended path? These and many more questions can be answered by a properly designed system for situational awareness. In this thesis we design and evaluate, both quantitatively and qualitatively, sensor fusion algorithms for multi-target tracking. We use a combination of camera and radar information to perform fusion and find relevant objects in a cluttered environment. The combination of these two sensors is very interesting because of their complementary attributes. The radar system has high range resolution but poor bearing resolution. The camera system on the other hand has a very high bearing resolution. This is very promising, with the potential to substantially increase the accuracy of the tracking system compared to just using one of the two. We have also designed algorithms for path prediction and a first threat awareness logic which are both qualitively evaluated.

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Gerónimo, Gómez David. "A Global Approach to Vision-Based Pedestrian Detection for Advanced Driver Assistance Systems." Doctoral thesis, Universitat Autònoma de Barcelona, 2010. http://hdl.handle.net/10803/5795.

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A començaments del segle XXI, els accidents de tràfic han esdevingut un greu problema no només pels països desenvolupats sino també pels emergents. Com en altres àrees científiques on la Intel·ligència Artificial s'ha transformat en un actor principal, els sistemes avançats d'assistència al conductor, i concretament els sistemes de protecció de vianants basats en Visió per Computador, han esdevingut una important línia d'investigació adressada a millorar la seguretat dels vianants. Tanmateix, el repte és d'una complexitat considerable donada la variabilitat dels humans (p.e., roba, mida, relació d'aspecte, forma, etc.), la naturalesa dinàmica dels sistemes d'abord i els entorns no estructurats en moviment que representen els escenaris urbans. A més, els requeriments de rendiment son rigorosos en termes de cost computacional i d'indexos de detecció. En aquesta tesi, en comptes de centrar-nos en millorar tasques específiques com sol ser freqüent a la literatura, presentem una aproximació global al problema. Aquesta visió global comença per la proposta d'una arquitectura genèrica pensada per a ser utilitzada com a marc tant per a la revisió de la literatura com per a organitzar les tècniques estudiades al llarg de la tesi. A continuació enfoquem la recerca en tasques com la segmentació dels objectes en primer pla, la classificació d'objectes i el refinament tot seguint una visió general i explorant aspectes que normalment no son analitzats. A l'hora de fer els experiments, també presentem una nova base de dades que consisteix en tres subconjunts, cadascun adressat a l'evaluació de les diferents tasques del sistema. Els resultats presentats en aquesta tesi no només finalitzen amb la proposta d'un sistema de detecció de vianants sino que van un pas més enllà indicant noves idees, formalitzant algoritmes proposats i ja existents, introduïnt noves tècniques i evaluant el seu rendiment, el qual esperem que aporti nous fonaments per a la futura investigació en aquesta àrea.
At the beginning of the 21th century, traffic accidents have become a major problem not only for developed countries but also for emerging ones. As in other scientific areas in which Artificial Intelligence is becoming a key actor, advanced driver assistance systems, and concretely pedestrian protection systems based on Computer Vision, are becoming a strong topic of research aimed at improving the safety of pedestrians. However, the challenge is of considerable complexity due to the varying appearance of humans (e.g., clothes, size, aspect ratio, shape, etc.), the dynamic nature of on-board systems and the unstructured moving environments that urban scenarios represent. In addition, the required performance is demanding both in terms of computational time and detection rates. In this thesis, instead of focusing on improving specific tasks as it is frequent in the literature, we present a global approach to the problem. Such a global overview starts by the proposal of a generic architecture to be used as a framework both to review the literature and to organize the studied techniques along the thesis. We then focus the research on tasks such as foreground segmentation, object classification and refinement following a general viewpoint and exploring aspects that are not usually analyzed. In order to perform the experiments, we also present a novel pedestrian dataset that consists of three subsets, each one addressed to the evaluation of a different specific task in the system. The results presented in this thesis not only end with a proposal of a pedestrian detection system but also go one step beyond by pointing out new insights, formalizing existing and proposed algorithms, introducing new techniques and evaluating their performance, which we hope will provide new foundations for future research in the area.

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Wilkerson, Jaxon. "Handoff of Advanced Driver Assistance Systems (ADAS) using a Driver-in-the-Loop Simulator and Model Predictive Control (MPC)." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1595262540712316.

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Daniel, Jérémie. "Trajectory generation and data fusion for control-oriented advanced driver assistance systems." Phd thesis, Université de Haute Alsace - Mulhouse, 2010. http://tel.archives-ouvertes.fr/tel-00608549.

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Since the origin of the automotive at the end of the 19th century, the traffic flow is subject to a constant increase and, unfortunately, involves a constant augmentation of road accidents. Research studies such as the one performed by the World Health Organization, show alarming results about the number of injuries and fatalities due to these accidents. To reduce these figures, a solution lies in the development of Advanced Driver Assistance Systems (ADAS) which purpose is to help the Driver in his driving task. This research topic has been shown to be very dynamic and productive during the last decades. Indeed, several systems such as Anti-lock Braking System (ABS), Electronic Stability Program (ESP), Adaptive Cruise Control (ACC), Parking Manoeuvre Assistant (PMA), Dynamic Bending Light (DBL), etc. are yet market available and their benefits are now recognized by most of the drivers. This first generation of ADAS are usually designed to perform a specific task in the Controller/Vehicle/Environment framework and thus requires only microscopic information, so requires sensors which are only giving local information about an element of the Vehicle or of its Environment. On the opposite, the next ADAS generation will have to consider more aspects, i.e. information and constraints about of the Vehicle and its Environment. Indeed, as they are designed to perform more complex tasks, they need a global view about the road context and the Vehicle configuration. For example, longitudinal control requires information about the road configuration (straight line, bend, etc.) and about the eventual presence of other road users (vehicles, trucks, etc.) to determine the best reference speed. [...]

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Tang, Zongzhi. "A Novel Road Marking Detection and Recognition Technique Using a Camera-based Advanced Driver Assistance System." Thesis, Université d'Ottawa / University of Ottawa, 2017. http://hdl.handle.net/10393/35729.

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Advanced Driver Assistance System (ADAS) was widely learned nowadays. As crucial parts of ADAS, lane markings detection, as well as other objects detection, have become more popular than before. However, most methods implemented in such areas cannot perfectly balance the performance of accuracy versus efficiency, and the mainstream methods (e.g. Machine Learning) suffer from several limitations which can hardly break the wall between partial autonomous and fully autonomous driving. This thesis proposed a real-time lane marking detection framework for ADAS, which included 4-extreme points set descriptor and a rule-based cascade classifier. By analyzing the behavior of lane markings on the road surface, a characteristic of markings was discovered, i.e., standard markings can sustain their shape in the perpendicular plane of the driving direction. By employing this feature, a 4-extreme points set descriptor was applied to describe the shape of each marking first. Specifically, after processing Maximally Stable Extremal Region (MSER) and Hough transforms on a 2-D image, several contours of interest are obtained. A bounding box, with borders parallel to the image coordinate, intersected with each contour at 4 points in the edge, which was named 4-extreme points set. Afterward, to verify consistency of each contour and standard marking, some rules abstracted from construction manual are employed such as Area Filter, Colour Filter, Relative Location Filter, Convex Filter, etc. To reduce the errors caused by changes in driving direction, an enhanced module was then introduced. By tracking the vanishing point as well as other key points of the road net, a method for 3-D reconstruction, with respect to the optical axis between vanishing point and camera center, is possible. The principle of such algorithm was exhibited, and a description about how to obtain the depth information from this model was also provided. Among all of these processes, a key-point based classification method is the main contribution of this paper because of its function in eliminating the deformation of the object caused by inverse perspective mapping. Several experiments were conducted in highway and urban roads in Ottawa. The detection rate of the markings by the proposed algorithm reached an average accuracy rate of 96.77% while F1 Score (harmonic mean of precision and recall) also attained a rate of 90.57%. In summary, the proposed method exhibited a state-of-the-art performance and represents a significant advancement of understanding.

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Bareiss, Max. "Effectiveness of Intersection Advanced Driver Assistance Systems in Preventing Crashes and Injuries in Left Turn Across Path / Opposite Direction Crashes in the United States." Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/96570.

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Intersection crashes represent one-fifth of all police reported traffic crashes and one-sixth of all fatal crashes in the United States each year. Active safety systems have the potential to reduce crashes and injuries across all crash modes by partially or fully controlling the vehicle in the event that a crash is imminent. The objective of this thesis was to evaluate crash and injury reduction in a future United States fleet equipped with intersection advanced driver assistance systems (I-ADAS). In order to evaluate this, injury risk modeling was performed. The dataset used to evaluate injury risk was the National Automotive Sampling System / Crashworthiness Data System (NASS/CDS). An injured occupant was defined as vehicle occupant who experienced an injury of maximum Abbreviated Injury Scale (AIS) of 2 or greater, or who were fatally injured. This was referred to as MAIS2+F injury. Cases were selected in which front-row occupants of late-model vehicles were exposed to a frontal, near-, or far-side crash. Logistic regression was used to develop an injury model with occupant, vehicle, and crash parameters as predictor variables. For the frontal and near-side impact models, New Car Assessment Program (NCAP) test results were used as a predictor variable. This work quantitatively described the injury risk for a wide variety of crash modes, informing effectiveness estimates. This work reconstructed 501 vehicle-to-vehicle left turn across path / opposite direction (LTAP/OD) crashes in the United States which had been originally investigated in NMVCCS. The performance of thirty different I-ADAS system variations was evaluated for each crash. These variations were the combinations of five Time to Collision (TTC) activation thresholds, three latency times, and two different intervention types (automated braking and driver warning). In addition, two sightline assumptions were modeled for each crash: one where the turning vehicle was visible long before the intersection, and one where the turning vehicle was only visible after entering the intersection. For resimulated crashes which were not avoided by I-ADAS, a new crash delta-v was computed for each vehicle. The probability of MAIS2+F injury to each front row occupant was computed. Depending on the system design, sightline assumption, I-ADAS variation, and fleet penetration, an I-ADAS system that automatically applies emergency braking could avoid 18%-84% of all LTAP/OD crashes. An I-ADAS system which applies emergency braking could prevent 44%-94% of front row occupants from receiving MAIS2+F injuries. I-ADAS crash and injured person reduction effectiveness was higher when both vehicles were equipped with I-ADAS. This study presented the simulated effectiveness of a hypothetical intersection active safety system on real crashes which occurred in the United States, showing strong potential for these systems to reduce crashes and injuries. However, this crash and injury reduction effectiveness made the idealized assumption of full installation in all vehicles of a future fleet. In order to evaluate I-ADAS effectiveness in the United States fleet the proportion of new vehicles with I-ADAS was modeled using Highway Loss Data Institute (HLDI) fleet penetration predictions. The number of potential LTAP/OD conflicts was modeled as increasing year over year due to a predicted increase in Vehicle Miles Traveled (VMT). Finally, the combined effect of these changes was used to predict the number of LTAP/OD crashes each year from 2019 to 2060. In 2060, we predicted 70,439 NMVCCS-type LTAP/OD crashes would occur. The predicted number of MAIS2+F injured front row occupants in 2060 was 3,836. This analysis shows that even in the long-term fleet penetration of Intersection Active Safety Systems, many injuries will continue to occur. This underscores the importance of maintaining passive safety performance in future vehicles.
M.S.
Future vehicles will have electronic systems that can avoid crashes in some cases where a human driver is unable, unaware, or reacts insufficiently to avoid the crash without assistance. The objective of this work was to determine, on a national scale, how many crashes and injuries could be avoided due to Intersection Advanced Driver Assistance Systems (I-ADAS), a hypothetical version of one of these up-and-coming systems. This work focused on crashes where one car is turning left at an intersection and the other car is driving through the intersection and not turning. The I-ADAS system has sensors which continuously search for other vehicles. When the I-ADAS system determines that a crash may happen, it applies the brakes or otherwise alerts the driver to apply the brakes. Rather than conduct actual crash tests, this was simulated on a computer for a large number of variations of the I-ADAS system. The basis for the simulations was real crashes that happened from 2005 to 2007 across the United States. The variations that were simulated changed the time at which the I-ADAS system triggered the brakes (or alert) and the simulated amount of computer time required for the I-ADAS system to make a choice. In some turning crashes, the car cannot see the other vehicle because of obstructions, such as a line of people waiting to turn left across the road. Because of this, simulations were conducted both with and without the visual obstruction. For comparison, we performed a simulation of the original crash as it happened in real life. Finally, since there are two cars in each crash, there are simulations when either car has the I-ADAS system or when both cars have the I-ADAS system. Each simulation either ends in a crash or not, and these are tallied up for each system variation. The number of crashes avoided compared to the number of simulations run is crash effectiveness. Crash effectiveness ranged from 1% to 84% based on the system variation. For each crash that occurred, there is another simulation of the time immediately after impact to determine how severe the impact was. This is used to determine how many injuries are avoided, because often the crashes which still happened were made less severe by the I-ADAS system. In order to determine how many injuries can be avoided by making the crash less severe, the first chapter focuses on injury modeling. This analysis was based on crashes from 2008 to 2015 which were severe enough that one of the vehicles was towed. This was then filtered down by only looking at crashes where the front or sides were damaged. Then, we compared the outcome (injury as reported by the hospital) to the circumstances (crash severity, age, gender, seat belt use, and others) to develop an estimate for how each of these crash circumstances affected the injury experienced by each driver and front row passenger. A second goal for this chapter was to evaluate whether federal government crash ratings, commonly referred to as “star ratings”, are related to whether the driver and passengers are injured or not. In frontal crashes (where a vehicle hits something going forwards), the star rating does not seem to be related to the injury outcome. In near-side crashes (the side next to the occupant is hit), a higher star rating is better. For frontal crashes, the government test is more extreme than all but a few crashes observed in real life, and this might be why the injury outcomes measured in this study are not related to frontal star rating. Finally, these crash and injury effectiveness values will only ever be achieved if every car has an I-ADAS system. The objective of the third chapter was to evaluate how the crash and injury effectiveness numbers change each year as new cars are purchased and older cars are scrapped. Early on, few cars will have I-ADAS and crashes and injuries will likely still occur at roughly the rate they would without the system. This means that crashes and injuries will continue to increase each year because the United States drives more miles each year. Eventually, as consumers buy new cars and replace older ones, left turn intersection crashes and injuries are predicted to be reduced. Long into the future (around 2050), the increase in crashes caused by miles driven each year outpaces the gains due to new cars with the I-ADAS system, since almost all of the old cars without I-ADAS have been removed from the fleet. In 2025, there will be 173,075 crashes and 15,949 injured persons that could be affected by the I-ADAS system. By 2060, many vehicles will have I-ADAS and there will be 70,439 crashes and 3,836 injuries remaining. Real cars will not have a system identical to the hypothetical I-ADAS system studied here, but systems like it have the potential to significantly reduce crashes and injuries.

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Balasubramanian, ArunKumar. "Benchmarking of Vision-Based Prototyping and Testing Tools." Master's thesis, Universitätsbibliothek Chemnitz, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-229999.

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The demand for Advanced Driver Assistance System (ADAS) applications is increasing day by day and their development requires efficient prototyping and real time testing. ADTF (Automotive Data and Time Triggered Framework) is a software tool from Elektrobit which is used for Development, Validation and Visualization of Vision based applications, mainly for ADAS and Autonomous driving. With the help of ADTF tool, Image or Video data can be recorded and visualized and also the testing of data can be processed both on-line and off-line. The development of ADAS applications needs image and video processing and the algorithm has to be highly efficient and must satisfy Real-time requirements. The main objective of this research would be to integrate OpenCV library with ADTF cross platform. OpenCV libraries provide efficient image processing algorithms which can be used with ADTF for quick benchmarking and testing. An ADTF filter framework has been developed where the OpenCV algorithms can be directly used and the testing of the framework is carried out with .DAT and image files with a modular approach. CMake is also explained in this thesis to build the system with ease of use. The ADTF filters are developed in Microsoft Visual Studio 2010 in C++ and OpenMP API are used for Parallel programming approach.

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Meijer, Max Jan. "Exploring Augmented Reality for enhancing ADAS and Remote Driving through 5G : Study of applying augmented reality to improve safety in ADAS and remote driving use cases." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-277857.

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This thesis consists of two projects focusing on how 5G can be used to make vehicles safer. The first project focuses on conceptualizing near-future use cases of how Advanced Driver Assistance Systems (ADAS) can be enhanced through 5G technology. Four concepts were developed in collaboration with various industry partners. These concepts were successfully demonstrated in a proof-of-concept at the 5G Automotive Association (5GAA) “The 5G Path of Vehicle-to-Everything Communication: From Local to Global” conference in Turin, Italy. This proof-of-concept was the world’s first demonstration of such a system. The second project focuses on a futuristic use case, namely remote operation of semi-autonomous vehicles (sAVs). As part of this work, it was explored if augmented reality (AR) can be used to warn remote operators of dangerous events. It was explored if such augmentations can be used to compensate during critical events. These events are defined as occurrences in which the network conditions are suboptimal, and information provided to the operator is limited. To evaluate this, a simulator environment was developed that uses eye- tracking technology to study the impact of such scenarios through user studies. The simulator establishes an extendable platform for future work. Through experiments, it was found that AR can be beneficial in spotting danger. However, it can also be used to directly affect the scanning patterns at which the operator views the scene and directly affect their visual scanning behavior.
Denna avhandling består av två projekt med fokus på hur 5G kan användas för att göra fordon säkrare. Det första projektet fokuserar på att konceptualisera användningsfall i närmaste framtid av hur Advanced Driver Assistance Systems (ADAS) kan förbättras genom 5G-teknik. Fyra koncept utvecklades i samarbete med olika branschpartner. Dessa koncept demonstrerade i ett proof-of- concept på 5G Automotive Association (5GAA) “5G Path of Vehicle to to Everything Communication: From Local to Global” -konferensen i Turin, Italien. Detta bevis-of-concept var världens första demonstration av ett sådant system. Det andra projektet fokuserar på ett långt futuristiskt användningsfall, nämligen fjärrstyrning av semi-autonoma fordon (sAVs). Som en del av detta arbete undersöktes det om augmented reality (AR) kan användas för att varna fjärroperatörer om farliga händelser. Det undersöktes om sådana förstärkningar kan användas för att kompensera under kritiska händelser. Dessa händelser definieras som händelser där nätverksförhållandena är suboptimala och information som tillhandahålls till operatören är begränsad. För att utvärdera detta utvecklades en simulatormiljö som använder ögonspårningsteknologi för att studera effekterna av sådana scenarier genom en användarstudie. Simulatorn bildar en utdragbar plattform för framtida arbete. Genom experiment fann man att AR kan vara fördelaktigt när det gäller att upptäcka fara. Men det kan också användas för att direkt påverka skanningsmönstret där operatören tittar på scenen och direkt påverka deras visuella skanningsbeteende.

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Books on the topic "Advanced Driver Assistance Systems (ADAS)"

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International Conference on Advanced Driver Assistance Systems (2001 Birmingham, England). ADAS: International Conference on Advanced Driver Assistance Systems, 17-18 September 2001, venue, Austin Court, Birmingham, UK. London: IEE, 2001.

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Waschl, Harald, Ilya Kolmanovsky, and Frank Willems, eds. Control Strategies for Advanced Driver Assistance Systems and Autonomous Driving Functions. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-91569-2.

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Machine Learning and Embedded Computing in Advanced Driver Assistance Systems (ADAS). MDPI, 2019. http://dx.doi.org/10.3390/books978-3-03921-376-4.

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IMechE (Institution of Mechanical Engineers). Advanced Driver Assistance Systems (ADAS): Vehicle Control for the Future (IMechE Seminar Publications). Wiley, 1999.

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Institution of Highways and Transportati. International Conference on Advanced Driver Assistance Systems Adas 2001 (Conference Publication (Institution of Electrical Engineers)). Institution of Electrical Engineers, 2001.

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Mensch-Maschine-Mobilität 2019. VDI Verlag, 2019. http://dx.doi.org/10.51202/9783181023600.

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Dieser VDI-Bericht ist ausschließlich als PDF-Dokument erschienen! Inhalt Akzeptanz: Erstkontakt mit autonomem Fahren Automation ohne Unsicherheit: Vorstellung des Förderprojekts AutoAkzept zur Erhöhung der Akzeptanz automatisierten Fahrens 1 U. Drewitz, K. Ihme, M. Oehl, Deutsches Zentrum für Luft und Raumfahrt (DLR), Braunschweig; F. Schrödel, R. Voßwinkel, IAV GmbH, Entwicklungszentrum Chemnitz/Stollberg; F. Hartwich, C. Schmidt, Technische Universität Chemnitz; A.-A. Pape, T. Fleischer, S. Cornelsen, TWT Science & Innovation GmbH; A. Lüdtke, D. Gräfing, A. Trende, OFFIS Institut für Informatik, Oldenburg Der Erstkontakt mit vollautomatisiertem Fahren – Ergebnisse aus drei Studien im Wizard of Oz-Fahrzeug 21 L. Gauer, I. Totzke, Audi Electronics Venture GmbH, Gaimersheim; M. Zehetleitner, Psychologie II, Katholische Universität Eichstätt-Ingolstadt ADAS Pick & Mix – A use case based approach to the naming and configuration of advanced driver assistance system...

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Waschl, Harald, Ilya Kolmanovsky, and Frank Willems. Control Strategies for Advanced Driver Assistance Systems and Autonomous Driving Functions: Development, Testing and Verification. Springer, 2019.

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Waschl, Harald, Ilya Kolmanovsky, and Frank Willems. Control Strategies for Advanced Driver Assistance Systems and Autonomous Driving Functions: Development, Testing and Verification. Springer, 2018.

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Book chapters on the topic "Advanced Driver Assistance Systems (ADAS)"

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Antony, Maria Merin, and Ruban Whenish. "Advanced Driver Assistance Systems (ADAS)." In Automotive Embedded Systems, 165–81. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-59897-6_9.

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Dimitrakopoulos, George. "Advanced Driver Assistance Systems (ADAS)." In Current Technologies in Vehicular Communication, 63–96. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47244-7_4.

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Riener, Andreas. "Advanced Driver Assistance Systems (ADAS)." In Sensor-Actuator Supported Implicit Interaction in Driver Assistance Systems, 83–85. Wiesbaden: Vieweg+Teubner, 2010. http://dx.doi.org/10.1007/978-3-8348-9777-0_9.

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Krishnarao, Santhosh, Hwang-Cheng Wang, Abhishek Sharma, and Mazher Iqbal. "Enhancement of Advanced Driver Assistance System (Adas) Using Machine Learning." In Proceedings of Fifth International Congress on Information and Communication Technology, 139–46. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5856-6_13.

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Kiebach, Helge, and Gerald-Alexander Beese. "Influence of Advanced Driver Assistance Systems (ADAS) on damages and repair costs." In Fahrerassistenzsysteme 2016, 31–40. Wiesbaden: Springer Fachmedien Wiesbaden, 2018. http://dx.doi.org/10.1007/978-3-658-21444-9_3.

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Gerwinn, Sebastian, Eike Möhlmann, and Anja Sieper. "Statistical Model Checking for Scenario-Based Verification of ADAS." In Control Strategies for Advanced Driver Assistance Systems and Autonomous Driving Functions, 67–87. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91569-2_4.

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Löchner, Joachim, J. Wagner, M. Wolter, and A. Fernandez. "Validating advanced driver assistance systems (ADAS) using comprehensive, loss-free in-vehicle measurements." In Proceedings, 1143–54. Wiesbaden: Springer Fachmedien Wiesbaden, 2016. http://dx.doi.org/10.1007/978-3-658-13255-2_85.

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van Waterschoot, Boris, and Mascha van der Voort. "Implementing Human Factors within the Design Process of Advanced Driver Assistance Systems (ADAS)." In Engineering Psychology and Cognitive Ergonomics, 461–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02728-4_49.

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Waschl, Harald, Roman Schmied, Daniel Reischl, and Michael Stolz. "A Virtual Development and Evaluation Framework for ADAS—Case Study of a P-ACC in a Connected Environment." In Control Strategies for Advanced Driver Assistance Systems and Autonomous Driving Functions, 107–31. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91569-2_6.

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Gehrig, Stefan, and Uwe Franke. "Stereovision for ADAS." In Handbook of Driver Assistance Systems, 495–524. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-12352-3_22.

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Conference papers on the topic "Advanced Driver Assistance Systems (ADAS)"

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Ziebinski, Adam, Rafal Cupek, Damian Grzechca, and Lukas Chruszczyk. "Review of advanced driver assistance systems (ADAS)." In PROCEEDINGS OF THE INTERNATIONAL CONFERENCE OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2017 (ICCMSE-2017). Author(s), 2017. http://dx.doi.org/10.1063/1.5012394.

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Stevens, A. "ADVISORS - a strategic approach to ADAS deployment." In International Conference on Advanced Driver Assistance Systems (ADAS). IEE, 2001. http://dx.doi.org/10.1049/cp:20010488.

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Chalmers, I. J. "User attitudes to automated highway systems." In International Conference on Advanced Driver Assistance Systems (ADAS). IEE, 2001. http://dx.doi.org/10.1049/cp:20010489.

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Senior, C. J. D. "Telematics systems from the service perspective." In International Conference on Advanced Driver Assistance Systems (ADAS). IEE, 2001. http://dx.doi.org/10.1049/cp:20010491.

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Kees, M. "Hydraulic actuated brake and electromechanically actuated brake systems." In International Conference on Advanced Driver Assistance Systems (ADAS). IEE, 2001. http://dx.doi.org/10.1049/cp:20010495.

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Richardson, J. H. "Human factors research priorities for ADAS systems: a UK perspective." In International Conference on Advanced Driver Assistance Systems (ADAS). IEE, 2001. http://dx.doi.org/10.1049/cp:20010492.

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Papadopoulos, Y. "Model-based semiautomatic safety analysis of programmable systems in automotive applications." In International Conference on Advanced Driver Assistance Systems (ADAS). IEE, 2001. http://dx.doi.org/10.1049/cp:20010498.

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Stevens, A. "Maximising usability and minimising liability - the RESPONSE project." In International Conference on Advanced Driver Assistance Systems (ADAS). IEE, 2001. http://dx.doi.org/10.1049/cp:20010490.

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Middleton, H. "Human interaction with automotive technologies: specification and measurement of driver abilities." In International Conference on Advanced Driver Assistance Systems (ADAS). IEE, 2001. http://dx.doi.org/10.1049/cp:20010493.

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Wang, F. "A driver assistant system for improvement of passenger ride comfort through modification of driving behaviour." In International Conference on Advanced Driver Assistance Systems (ADAS). IEE, 2001. http://dx.doi.org/10.1049/cp:20010494.

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Reports on the topic "Advanced Driver Assistance Systems (ADAS)"

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Waraniak, John. Unsettled Issues on Sensor Calibration for Automotive Aftermarket Advanced Driver-Assistance Systems. SAE International, March 2021. http://dx.doi.org/10.4271/epr2021008.

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Many automotive industry safety advocates have been pushing for greater market penetration for active safety and advanced driver-assistance systems (ADAS), with the goal of ending deaths due to car crashes. However, there are far-reaching implications for the collision repair, specialty equipment, and performance aftermarket sectors—after a collision or modification, the ADAS system functionality must be preserved to maintain, driver, passenger, and road user safety. To do this, sensor recalibration and ADAS functional safety validation and documentation after repair, modification, or accessorizing are necessary. Unsettled Issues on Sensor Calibration for Automotive Aftermarket ADAS tackles the challenges of accelerating the pace of ADAS implementation; increasing industry understanding of systems, sensors, software, controllers; and minimizing the overwhelming variety of sensor calibration procedures and automaker targets. Additionally, this report addresses the liability concerns that are challenging the industry as it seeks to move forward safely.

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Razdan, Rahul. Unsettled Topics Concerning Human and Autonomous Vehicle Interaction. SAE International, December 2020. http://dx.doi.org/10.4271/epr2020025.

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This report examines the current interaction points between humans and autonomous systems, with a particular focus on advanced driver assistance systems (ADAS), the requirements for human-machine interfaces as imposed by human perception, and finally, the progress being made to close the gap. Autonomous technology has the potential to benefit personal transportation, last-mile delivery, logistics, and many other mobility applications enormously. In many of these applications, the mobility infrastructure is a shared resource in which all the players must cooperate. In fact, the driving task has been described as a “tango” where we—as humans—cooperate naturally to enable a robust transportation system. Can autonomous systems participate in this tango? Does that even make sense? And if so, how do we make it happen?

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Unsettled Issues on HD Mapping Technology for Autonomous Driving and ADAS. SAE International, June 2021. http://dx.doi.org/10.4271/epr2021013.

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Current advanced driver-assistance systems (ADAS) and automated driving systems (ADS) rely on high-definition (HD) maps to enable a range of features and functions. These maps can be viewed as an additional sensor from an ADAS or ADS perspective as they impact overall system confidence, reduce system computational resource needs, help improve comfort and convenience, and ultimately contribute to system safety. However, HD mapping technology presents multiple challenges to the automotive industry. Unsettled Issues on HD Mapping Technology for Autonomous Driving and ADAS identifies the current unsettled issues that need to be addressed to reach the full potential of HD maps for ADAS and ADS technology and suggests some possible solutions for initial map creation, map change detection and updates, and map safety levels.

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