Relevant bibliographies by topics / Road cameras

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Road cameras'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 February 2022

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Journal articles on the topic "Road cameras"

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Lee, Si-Ho, Bong-Ju Kim, and Seon-Bong Lee. "Study on Image Correction and Optimization of Mounting Positions of Dual Cameras for Vehicle Test." Energies 14, no. 16 (August 9, 2021): 4857. http://dx.doi.org/10.3390/en14164857.

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Among surrounding information-gathering devices, cameras are the most accessible and widely used in autonomous vehicles. In particular, stereo cameras are employed in academic as well as practical applications. In this study, commonly used webcams are mounted on a vehicle in a dual-camera configuration and used to perform lane detection based on image correction. The height, baseline, and angle were considered as variables for optimizing the mounting positions of the cameras. Then, a theoretical equation was proposed for the measurement of the distance to the object, and it was validated via vehicle tests. The optimal height, baseline, and angle of the mounting position of the dual camera configuration were identified to be 40 cm, 30 cm, and 12°, respectively. These values were utilized to compare the performances of vehicles in stationary and driving states on straight and curved roads, as obtained by vehicle tests and theoretical calculations. The comparison revealed the maximum error rates in the stationary and driving states on a straight road to be 3.54% and 5.35%, respectively, and those on a curved road to be 9.13% and 9.40%, respectively. It was determined that the proposed method is reliable because the error rates were less than 10%.
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Grabowski, Dariusz, and Andrzej Czyżewski. "System for monitoring road slippery based on CCTV cameras and convolutional neural networks." Journal of Intelligent Information Systems 55, no. 3 (September 9, 2020): 521–34. http://dx.doi.org/10.1007/s10844-020-00618-5.

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Abstract The slipperiness of the surface is essential for road safety. The growing number of CCTV cameras opens the possibility of using them to automatically detect the slippery surface and inform road users about it. This paper presents a system of developed intelligent road signs, including a detector based on convolutional neural networks (CNNs) and the transfer-learning method employed to the processing of images acquired with video cameras. Based on photos taken in different light conditions by CCTV cameras located at the roadsides in Poland, four network topologies have been trained and tested: Resnet50 v2, Resnet152 v2, Vgg19, and Densenet201. The last-mentioned network has proved to give the best result with 98.34% accuracy of classification dry, wet, and snowy roads.
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Ali, S. Y., O. Al-Saleh, and P. A. Koushki. "Effectiveness of Automated Speed-Monitoring Cameras in Kuwait." Transportation Research Record: Journal of the Transportation Research Board 1595, no. 1 (January 1997): 20–26. http://dx.doi.org/10.3141/1595-04.

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In 1994 the General Traffic Department installed automatic radar cameras to monitor traffic speed at a number of strategic roadway locations in Kuwait. The aim was to lower the number of high-speed violations and thus reduce road accidents. Recent traffic safety records point to an increase in both the number of violations and the occurrence of road accidents. It is argued in this paper that without live enforcement support and active follow-up of camera-recorded violations, the effectiveness of these cameras in improving road safety is insignificant at best, particularly in the undisciplined driving environment of the oil-rich nations in the Middle East. The speed of traffic was simultaneously measured via radar instruments both at the automatic camera site and at sections approximately 1 km before or after or before and after the cameras at eight camera locations. Measurements were recorded for six 1/2-hr periods at each site for a total of 72 hr over a period of 3 months, so that morning, afternoon, and after-dark hours, as well as different days of the week and roadway types, were covered. Analysis of the speed data showed that for the three daily periods and various roadway types, traffic speeds were consistently higher in sections before or after or before and after the automatic camera at the camera site. Statistical tests indicated that the difference in speed measured at and away from the cameras was at the 99 percent level. The findings demonstrate that in a traffic environment characterized by poor driving behavior, inconsistent and piecemeal driver education programs, and insufficient presence of law enforcement officials, reliance on automatic cameras alone to reduce traffic violations is doomed to fail.
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Kuehnle, Andreas, and Wilco Burghout. "Winter Road Condition Recognition Using Video Image Classification." Transportation Research Record: Journal of the Transportation Research Board 1627, no. 1 (January 1998): 29–33. http://dx.doi.org/10.3141/1627-05.

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Sweden spends 1.7 billion Crowns on winter road maintenance annually. A large part of this money goes into plowing, salting, and sanding of the roads. The decision about what maintenance to perform is made, in part, based on data received from road weather information stations, some of which are also equipped with video cameras. These video cameras form an additional unexploited sensor for determining the road condition during winter. Images taken from a handheld roadside video camera are investigated here to see if it is possible to determine the road state (dry, wet, snowy, icy, snowy with tracks) from the video images alone. The system is intended to supplement the other weather station measurements, such as temperature and wind speed, and make better maintenance decisions and quality control of maintenance possible. The results indicate that it is possible to distinguish between all road states except for ice/wet and ice/tracks. Typical class separations are a Mahanalobis distance between 0 and 2. Neural networks with three or four input features, three to five hidden neurons, and a sigmoid-sigmoid-linear architecture are used to classify the road state. Rates of correct classification are typically 40 to 50 percent with these networks. There are useful feature combinations, including purely monochrome features, which do not depend on the network architecture.
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Zhang, Haojie, David Hernandez, Zhibao Su, and Bo Su. "A Low Cost Vision-Based Road-Following System for Mobile Robots." Applied Sciences 8, no. 9 (September 13, 2018): 1635. http://dx.doi.org/10.3390/app8091635.

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Navigation is necessary for autonomous mobile robots that need to track the roads in outdoor environments. These functions could be achieved by fusing data from costly sensors, such as GPS/IMU, lasers and cameras. In this paper, we propose a novel method for road detection and road following without prior knowledge, which is more suitable with small single lane roads. The proposed system consists of a road detection system and road tracking system. A color-based road detector and a texture line detector are designed separately and fused to track the target in the road detection system. The top middle area of the road detection result is regarded as the road-following target and is delivered to the road tracking system for the robot. The road tracking system maps the tracking position in camera coordinates to position in world coordinates, which is used to calculate the control commands by the traditional tracking controllers. The robustness of the system is enhanced with the development of an Unscented Kalman Filter (UKF). The UKF estimates the best road borders from the measurement and presents a smooth road transition between frame to frame, especially in situations such as occlusion or discontinuous roads. The system is tested to achieve a recognition rate of about 98.7% under regular illumination conditions and with minimal road-following error within a variety of environments under various lighting conditions.
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Zhu, Zijian, Xu Li, Jianhua Xu, Jianhua Yuan, and Ju Tao. "Unstructured Road Segmentation Based on Road Boundary Enhancement Point-Cylinder Network Using LiDAR Sensor." Remote Sensing 13, no. 3 (January 30, 2021): 495. http://dx.doi.org/10.3390/rs13030495.

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The segmentation of unstructured roads, a key technology in self-driving technology, remains a challenging problem. At present, most unstructured road segmentation algorithms are based on cameras or use LiDAR for projection, which has considerable limitations that the camera will fail at night, and the projection method will lose one-dimensional information. Therefore, this paper proposes a road boundary enhancement Point-Cylinder Network, called BE-PCFCN, which uses Point-Cylinder in order to extract point cloud features directly and integrates the road enhancement module to achieve accurate unstructured road segmentation. Firstly, we use the improved RANSAC-Boundary algorithm to calculate the rough road boundary point set, training in the same parameters with the original point cloud as a submodule. The whole network adopts the encoder and decoder structure, using Point-Cylinder as the basic module, while considering the data locality and the algorithm complexity. Subsequently, we made an unstructured road data set for training and compared it with existing LiDAR(Light Detection And Ranging) semantic segmentation algorithms. Finally, the experiment verified the robustness of BE-PCFCN. The road intersection-over-union (IoU) was increased by 4% when compared with the best existing algorithm, reaching 95.6%. Even on unstructured roads with an extremely irregular shape, BE-PCFCN also currently has the best segmentation results.
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Rhanizar, Asmae, and Zineb El Akkaoui. "A Predictive Framework of Speed Camera Locations for Road Safety." Computer and Information Science 12, no. 3 (July 30, 2019): 92. http://dx.doi.org/10.5539/cis.v12n3p92.

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Road traffic crashes are a public health issue due to their terrible impact on individuals, communities, and countries. Studies affirmed that vehicle speed is a major contributor to crash likelihood and severity. At the same time, they identified Automated Speed Enforcement (ASE) systems, namely speed cameras, as a highly effective measure to reduce excessive and inappropriate speed, and thus improving road safety. However, identifying optimum sites for fixed speed camera placement stays an open issue in the literature, although it is a key factor that guarantees the efficiency of such ASE systems. This paper describes a predictive framework of speed camera locations using a classification algorithm that can predict, for each section of a given road network, its pertinence as a speed camera location. First, we identify a set of features as predictors of the classification algorithm, that we have argued their goodness through correlation tests. Second, for training our algorithm, data from road controlled sections, corresponding to existing speed cameras, is exploited. Each section class reflects the contribution level of the ASE system (good, neutral, or bad) to road safety. Third, as a proofof-concept, the framework has been implemented and deployed on the Moroccan road network. The results showed that Random Forest classifier is the best performing model attaining an accuracy of 95% and a precision of 88%. Further, a tool was developed to visualize updated classification results on a Moroccan road network map to support authorities in their decision making process.
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Liu, Chun Feng, Shan Shan Kong, and Hai Ming Wu. "Research on a Single Camera Location Model and its Application." Applied Mechanics and Materials 50-51 (February 2011): 468–72. http://dx.doi.org/10.4028/www.scientific.net/amm.50-51.468.

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Digital cameras have been widely used in the areas of road transportation, railway transportation as well as security system. To address the position of digital camera in these fields this paper proposed a geometry calibration method based on feature point extraction of arbitrary target. Under the meaning of the questions, this paper first defines four kinds of coordinate system, that is the world coordinate system. The camera's optical center of the coordinate system is the camera coordinate system, using the same point in different coordinate system of the coordinate transformation to determine the relationship between world coordinate system and camera coordinate. And thus determine the camera's internal parameters and external parameters, available transformation matrix and translation vector indicated by the camera's internal parameters of the external parameters and the establishment of a single camera location model. According to the model, using the camera's external parameters to be on the target circle center point in the image plane coordinates.
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Coronell, Garis, Julián Arellana, and Víctor Cantillo. "LOCATION OF SPEED CONTROL CAMERAS ON HIGHWAYS: A GEOSPATIAL ANALYSIS." Transport 36, no. 3 (August 17, 2021): 199–212. http://dx.doi.org/10.3846/transport.2021.15117.

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This research proposes a methodology to identify critical sections of highways where the location of speeding control may be beneficial. The method relies on a spatial and statistical analysis of infrastructure risks, along with traffic accident frequency and severity. A relevant feature of this methodology is related to its potential to be used in areas where there are no detailed historical records about traffic crashes, which is common in Global South countries. We applied the methodology to a rural road network in Colombia, where a recent law established that technical criteria should support the location of speed cameras. The case study uses accident information from six years, and risk data from a road safety audit carried out in the area under study. Even though historical records of accidents in the area were not fully available, the methodology allowed prioritising speed camera installations in the zone and identifying the relevant variables to define camera location. The relevant variables were the geometric characteristics of the road, traffic flows, risk factors, and proximity to populated centres. The use of speed controls should be part of a road safety management system, which allows defining camera location according to robust technical criteria.
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Koukoumidis, Emmanouil, Margaret Martonosi, and Li-Shiuan Peh. "Leveraging Smartphone Cameras for Collaborative Road Advisories." IEEE Transactions on Mobile Computing 11, no. 5 (May 2012): 707–23. http://dx.doi.org/10.1109/tmc.2011.275.

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Dissertations / Theses on the topic "Road cameras"

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Egbert, Joseph M. "Low-Altitude Road Following, Using Strap-Down Cameras on Miniature Aerial Vehicles." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd2170.pdf.

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Boscoe-Wallace, Agnes. "Optimisation of speed camera locations using genetic algorithm and pattern search." Thesis, Loughborough University, 2017. https://dspace.lboro.ac.uk/2134/25179.

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Road traffic accidents continue to be a public health problem and are a global issue due to the huge financial burden they place on families and society as a whole. Speed has been identified as a major contributor to the severity of traffic accidents and there is the need for better speed management if road traffic accidents are to be reduced. Over the years various measures have been implemented to manage vehicle speeds. The use of speed cameras and vehicle activated signs in recent times has contributed to the reduction of vehicle speeds to various extents. Speed cameras use punitive measures whereas vehicle activated signs do not so their use depends on various factors. Engineers, planners and decision makers responsible for determining the best place to mount a speed camera or vehicle activated sign along a road have based their decision on experience, site characteristics and available guidelines (Department for Transport, 2007; Department for Transport, 2006; Department for Transport, 2003). These decisions can be subjective and indications are that a more formal and directed approach aimed at bringing these available guidelines together in a model will be beneficial in making the right decision as to where to place a speed camera or vehicle activated sign is to be made. The use of optimisation techniques have been applied in other areas of research but this has been clearly absent in the Transport Safety sector. This research aims to contribute to speed reduction by developing a model to help decision makers determine the optimum location for a speed control device. In order to achieve this, the first study involved the development of an Empirical Bayes Negative Binomial regression accident prediction model to predict the number of fatal and serious accidents combined and the number of slight accidents. The accident prediction model that was used explored the effect of certain geometric and traffic characteristics on the effect of the severity of road traffic accident numbers on selected A-roads within the Nottinghamshire and Leicestershire regions of United Kingdom. On A-roads some model variables (n=10) were found to be statistically significant for slight accidents and (n=6) for fatal and serious accidents. The next study used the accident prediction model developed in two optimisation techniques to help predict the optimal location for speed cameras or vehicle activated signs. Pattern Search and Genetic Algorithms were the two main types of optimisation techniques utilised in this thesis. The results show that the two methods did produce similar results in some instances but different in others. Optimised results were compared to some existing sites with speed cameras some of the results obtained from the optimisation techniques used were within proximity of about 160m. A validation method was applied to the genetic algorithm and pattern search optimisation methods. The pattern search method was found to be more consistent than the genetic algorithm method. Genetic algorithm results produced slightly different results at validation in comparison with the initial results. T-test results show a significant difference in the function values for the validated genetic algorithm (M= 607649.34, SD= 1055520.75) and the validated pattern search function values (M= 2.06, SD= 1.17) under the condition t (79) = 5.15, p=0.000. There is a role that optimisation techniques can play in helping to determine the optimum location for a speed camera or vehicle activated sign based on a set of objectives and specified constraints. The research findings as a whole show that speed cameras and vehicle activated signs are an effective speed management tool. Their deployment however needs to be carefully considered by engineers, planners and decision makers so as to achieve the required level of effectiveness. The use of optimisation techniques which has been generally absent in the Transport Safety sector has been shown in this thesis to have the potential to contribute to improve speed management. There is however no doubt that this research will stimulate interest in this rather new but high potential area of Transport Safety.
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Snyder, Sara Ann. "Examining the impacts of State Route 101 on wildlife using road kill surveys and remote cameras." DigitalCommons@CalPoly, 2014. https://digitalcommons.calpoly.edu/theses/1296.

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Roads can negatively impact the survival of wildlife populations through additional mortality from road kill and population fragmentation caused by road avoidance behaviors. The 11.9 mile section of State Route 101 between the towns of San Luis Obispo and Atascadero, CA, USA, cross a mountain lion movement corridor and an area important to maintaining ecological connectivity between protected lands in the Los Padres National Forest to the north and south. I examined the spatial patterns and landscape and roadway factors associated with road kill occurrence for six taxa; large mammals, mesocarnivores, squirrels, rabbits, birds and raptors. Between 1 May 2009 and 30 June 2010 road kills were documented using vehicle-based surveys. Small mammals were the most common road kill (58.3%), followed by mesocarnivores (10.9%), birds (10.6%), rabbits (5.1%), large mammals (3.3%) and raptors (3.2%). Twenty-nine large mammal road kills were observed during the survey period; eighteen mule deer, six black bears and five feral pigs. Road kill was highest in the middle of the survey area between the top of Cuesta Grade and the southern edge of Atascadero and lowest along the Cuesta Grade. I modeled road kill occurrence using logistic regression to determine which landscape and roadway characteristics were associated with road kill locations. Large mammal and mesocarnivore road kills were more likely to occur near riparian corridors. Mesocarnivore and squirrel road kills were associated with locations with greater roadside tree cover. Squirrel and rabbit road kills were more likely to occur along sections of the road with large grassy center medians. I documented animal activity patterns around the roadway during three survey periods (summer 2009, fall 2009 and spring 2010) using remote cameras placed on game trails and underpasses along the roadway. Mule deer displayed crepuscular activity patterns with peaks in activity in the morning between 05:00h and 07:00h and in the evening between 16:00h and 18:00h. Mesocarnivores generally displayed a nocturnal activity patterns with the majority of activity occurring between 18:00h and 06:00h. I used logistic regression to determine if there was a relationship between animal activity patterns and traffic patterns while controlling for time of day, day of the week, and season. Mule deer and mesocarnivore activity patterns varied significantly by time of day and mule deer activity also varied significantly by season; however only mesocarnivore activity varied significantly in relation to traffic volume suggesting that mesocarnivores are less activity when traffic volume is high. Using traffic volume and animal activity patterns I calculated a collision potential value for both mule deer and mesocarnivores. Collision potential for mule deer was high in the morning, between 06:00h and 08:00h, and in the evening, between 16:00h and 18:00h in all three seasons. Collision potential for mesocarnivores was high in the evening in fall 2009 (18:00h and 21:00) and spring 2010 (17:00h), and high in the morning in summer 2009 (09:00h).
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Novoa, Pardo Ana María. "Effectiveness of road safety interventions in Spain." Doctoral thesis, Universitat Pompeu Fabra, 2010. http://hdl.handle.net/10803/22689.

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Las lesiones por tráfico son un importante problema de salud pública en España. En 2004, el gobierno español estableció la seguridad vial como una prioridad política, y creó el Plan Estratégico de Seguridad Vial 2005-2008, que propone una serie de medidas dirigidas a disminuir el impacto de las lesiones por tráfico en España. Los objetivos de la tesis son revisar las intervenciones de seguridad vial que se han demostrado efectivas en reducir las lesiones y mortalidad por tráfico y evaluar el impacto en morbilidad y mortalidad por tráfico del conjunto de medidas implementadas en España a partir del año 2004 y de algunas de las medidas implementadas, concretamente el permiso por puntos, la reforma del Código Penal y los radares. El diseño de todos los estudios de evaluación consistió en estudios de series temporales interrumpidas. Las poblaciones de estudio fueron el número de colisiones, conductores involucrados en colisiones con lesionados y personas lesionadas por tráfico en España entre los años 2000 y 2008. Las fuentes de información fueron bases de datos de policía y hospitalarias. Se ajustaron modelos de regresión Quasi-Poisson, controlando la tendencia temporal y la estacionalidad. Los estudios incluidos en la tesis sugieren que la priorización de la seguridad vial en el año 2004 supuso un cambio en la tendencia de las lesiones por tráfico en España, y fue especialmente efectiva en reducir el número de lesionados graves. Entre las intervenciones incluidas en el Plan Estratégico de Seguridad Vial 2005-2008, se evaluó la efectividad de los radares, el permiso por puntos y la criminalización de una serie de comportamientos de tráfico – mediante la reforma del Código Penal –, medidas que redujeron el impacto de las lesiones por tráfico en España. Sin embargo, la revisión de la literatura incluida en la tesis identificó diversas medidas efectivas de seguridad vial, como el permiso de conducir gradual, que todavía no han sido implementadas y que podrían reducir todavía más el número de personas lesionadas en las carreteras españolas. Será necesario realizar esfuerzos importantes y adjudicar suficientes recursos para mantener el nivel de seguridad vial alcanzado. Además, se deberán implementar más medidas efectivas de seguridad vial para reducir el todavía inaceptablemente elevado número de personas lesionadas o muertas en las carreteras españolas cada día.
Road traffic injuries are an important public health problem in Spain. In 2004, the Spanish government established road safety as a political priority, and created the Road Safety Strategic Programme 2005-2008, which proposes a series of actions aimed to reduce the burden of traffic injuries in Spain. The objectives of the present thesis are to review the road safety interventions which have proven to be effective in reducing road traffic deaths and injuries, and to assess the impact on traffic morbidity and mortality of overall road safety interventions implemented in Spain from the year 2004 on and of specific road safety interventions implemented, specifically the penalty points system, the reform of the Penal Code and speed cameras. The design of all the intervention evaluation studies consisted in interrupted time-series studies. The number of injury crashes, drivers involved in injury collisions, and people injured in traffic collisions in Spain between the years 2000-2008 were the study populations. Police and hospital registries were used as sources of information. Quasi-Poisson regression models were adjusted, controlling for time trend and seasonal patterns. The studies included in the present thesis suggest that the prioritisation of road safety in the year 2004 changed the trend of road traffic injuries in Spain, being especially effective in reducing the number of seriously injured people. Among the interventions included in the Road Safety Strategic Programme 2005-2008, speed cameras, the penalty points system, and the criminalisation of a set of road behaviours - by means of reforming the Penal Code – were assessed for effectiveness and were observed to reduce the burden of traffic injuries in Spain. Nevertheless, the literature review included in the thesis identified several effective road safety interventions, such as the graduated licensing system, that have not been implemented as yet, and which could further reduce the number of people injured on the Spanish roads. Important efforts and enough resources will be needed to maintain the level of road safety achieved. Furthermore, additional effective road safety measures should be implemented to reduce the still unacceptably high number of people injured or killed on the Spanish roads every day.
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Jensen, Alex J. "Crossing Corridors: Wildlife Use of Jumpouts and Undercrossings Along a Highway With Wildlife Exclusion Fencing." DigitalCommons@CalPoly, 2018. https://digitalcommons.calpoly.edu/theses/1939.

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Roads pose two central problems for wildlife: wildlife-vehicle collisions (WVCs) and habitat fragmentation. Wildlife exclusion fencing can reduce WVCs but can exacerbate fragmentation. In Chapter 1, I summarize the relevant studies addressing these two problems, with a focus on large mammals in North America. Chapters 2 and 3 summarize field assessments of technologies to reduce WVCs and maintain connectivity, specifically jumpout ramps and underpasses, along Highway 101 near San Luis Obispo, CA. In a fenced highway, some animals inevitably breach the fence and become trapped, which increases the risk of a wildlife-vehicle collision. Earthen escape ramps, or “jumpouts”, can allow the trapped animal to escape the highway corridor. Few studies have quantified wildlife use of jumpouts, and none for >2 years. We used wildlife cameras to quantify wildlife use of 4 jumpouts from 2012-2017. Mule deer were 88% percent of our detections and jumped out 20% of the time. After accounting for pseudoreplication, 33% of the events were independent events, and 2 groups of deer accounted for 41% of all detections at the top of the jumpout. Female deer were 86% of the detections and were much more likely than males to return to the jumpout multiple times. This is the first study to document use of jumpouts for more than 3 years, the first to account for pseudoreplication, and the first to quantify differences in jumpout use between male and female mule deer. We recommend a jumpout height between 1.75m-2m for mule deer to increase the jumpout success rate. Chapter 3 addresses factors that may affect the use of undercrossings by mule deer and other wildlife. Wildlife crossings combined with wildlife exclusion fencing have been shown to be the most effective method to reduce wildlife-vehicle collisions while maintaining ecological connectivity. Although several studies have quantified wildlife use of undercrossings, very few have exceeded 24 months, and the factors affecting carnivores use of the undercrossings remain unclear. We quantified mule deer, black bear, mountain lion, and bobcat use of 11 undercrossings along Highway 101 near San Luis Obispo, California from 2012-2017. We constructed zero-inflated Poisson general linear models on the monthly activity of our focal species using underpass dimensionality, distance to cover, substrate, human activity, and location relative to the wildlife exclusion fence as predictor variables. We accounted for temporal variation, as well as spatial variation by quantifying the landscape resistance near each undercrossing. We found that deer almost exclusively used the larger underpasses whereas the carnivores were considerably less selective. Bears used undercrossings more that were within the wildlife exclusion fence, whereas mountain lion activity was higher outside the wildlife exclusion fence. Bobcat activity was highest and most widespread, and was negatively associated with distance to cover. Regional connectivity is most important for bear and mountain lion, and the surrounding habitat may be the most important predictor for their use of undercrossings. We recommend placing GPS collars on our focal species to more clearly document fine-scale habitat selection near the highway.
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Paula, Maurício Braga de. "Visão computacional para veículos inteligentes usando câmeras embarcadas." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2015. http://hdl.handle.net/10183/122511.

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O uso de sistemas de assistência ao motorista (DAS) baseados em visão tem contribuído consideravelmente na redução de acidentes e consequentemente no auxílio de uma melhor condução. Estes sistemas utilizam basicamente uma câmera de vídeo embarcada (normalmente fixada no para-brisa) com o propósito de extrair informações acerca da rodovia e ajudar o condutor num melhor processo de dirigibilidade. Pequenas distrações ou a perda de concentração podem ser suficientes para que um acidente ocorra. Este trabalho apresenta uma proposta para o desenvolvimento de algoritmos para extrair informações sobre a sinalização em rodovias. Mais precisamente, serão abordados algoritmos de calibração de câmera explorando a geometria da pista, de extração da marcação de pintura (sinalização horizontal) e detecção e identificação de placas de trânsito (sinalização vertical). Os resultados experimentais indicam que o método de calibração de câmera alcançou bons resultados na obtenção dos parâmetros extrínsecos com erros inferiores a 0:5 . O erro médio encontrado nos experimentos com relação a estimativa da altura da câmera foi em torno de 12 cm (erro relativo aproximado de 10%), permitindo explorar o uso da realidade aumentada como uma possível aplicação. A acurácia global para a detecção e reconhecimento da sinalização horizontal (marcas seccionadas, contínuas e mistas) foi acima de 96% perante uma diversidade de situações apresentadas, tais como: sombras, variação de iluminação, degradação do asfalto e pintura. O uso da câmera calibrada para a detecção da sinalização vertical contribui para delimitar o espaço de varredura da janela deslizante do detector, bem como realizar a procura por placas em uma única escala para cada região de busca, caracterizada pela distância ao veículo. Os resultados apresentados reportam uma taxa global de classificação de aproximadamente 99% para o sinal de proibido ultrapassar, considerando-se uma base de dados limitada a 962 amostras.
The use of driver assistance systems (DAS) based on computer vision has helped considerably in reducing accidents and consequently aid in better driving. These systems primarily use an embedded video camera (usually fixed on the windshield) for the purpose of extracting information about the highway and assisting the driver in a better handling process. Small distractions or loss of concentration may be sufficient for an accident to occur. This work presents the development of algorithms to extract information about traffic signs on highways. More specifically, this work will tackle a camera calibration algorithm that exploits the geometry of the road track, algorithms for the extraction of road marking paint (lane markings) and detection and identification of vertical traffic signs. Experimental results indicate that the proposed method for obtaining the extrinsic parameters achieve good results with errors of less than 0:5 . The average error in our experiments, related to the camera height, were around 12 cm (relative error around 10%). Global accuracy for the detection and classification of road lane markings (dashed, solid, dashed-solid, solid-dashed or double solid) were over 96%. Finally, our camera calibration algorithm was used to reduce the search region and to define the scale of a slidingwindow detector for vertical traffic signs. The use of the calibrated camera for the detection of traffic signs contributes to define the scanning area of the sliding window and perform a search for signs on a unique scale for each region of interest, determined by the distance to the vehicle. The results reported a global classification rate of approximately 99% for the no overtaking sign, considering a limited of 962 samples.
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Ekström, Marcus. "Road Surface Preview Estimation Using a Monocular Camera." Thesis, Linköpings universitet, Datorseende, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-151873.

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Recently, sensors such as radars and cameras have been widely used in automotives, especially in Advanced Driver-Assistance Systems (ADAS), to collect information about the vehicle's surroundings. Stereo cameras are very popular as they could be used passively to construct a 3D representation of the scene in front of the car. This allowed the development of several ADAS algorithms that need 3D information to perform their tasks. One interesting application is Road Surface Preview (RSP) where the task is to estimate the road height along the future path of the vehicle. An active suspension control unit can then use this information to regulate the suspension, improving driving comfort, extending the durabilitiy of the vehicle and warning the driver about potential risks on the road surface. Stereo cameras have been successfully used in RSP and have demonstrated very good performance. However, the main disadvantages of stereo cameras are their high production cost and high power consumption. This limits installing several ADAS features in economy-class vehicles. A less expensive alternative are monocular cameras which have a significantly lower cost and power consumption. Therefore, this thesis investigates the possibility of solving the Road Surface Preview task using a monocular camera. We try two different approaches: structure-from-motion and Convolutional Neural Networks.The proposed methods are evaluated against the stereo-based system. Experiments show that both structure-from-motion and CNNs have a good potential for solving the problem, but they are not yet reliable enough to be a complete solution to the RSP task and be used in an active suspension control unit.
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ZENG, HAOMING. "FPGA based smart NIR camera." Thesis, Mittuniversitetet, Institutionen för informationsteknologi och medier, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-17613.

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Road conditions are a critical issue for road users as, if not given sufficient attention, they may threaten users’ lives. The environmental parameters, such as snowy, icy, dry and wet, are important in relation to the condition of roads. This is particularly true in relation to the northern regions and greatest concern should be in relation to snowy and icy situations. In this thesis, a system based on an InGaAs area scan sensor utilizes NIR technology to detect water or ice on the road so as to enable drivers to avoid slippery road conditions. The conditions caused by freezing water on road surface are particularly dangerous and are not easy to observe and it is hope that this project will boost traffic safety. The system is able to assist road maintenance personnel in forecasting and detecting slippery road conditions during winter road maintenance (WRM). The system, which is based on FPGA, has functionalities that display the captured images on an HDMI monitor and send the images to the software on a host PC via the UART protocol. An interface board, which carries the sensor and which connects to the FPGA board, is developed for NIR sensor. VHDL implementation and PC software design are the works included in the project. Besides, this device is exploited utilizing InGaAs image sensor. According to its features, it can be applied in other applications which will also be discussed. Finally, experiments are conducted in order to investigate the system’s operation with the variation of temperature.
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Lee, Jong Ho. "Understanding the Visual Appearance of Road Scenes Using a Monocular Camera." Research Showcase @ CMU, 2017. http://repository.cmu.edu/dissertations/795.

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Over the past several decades, research efforts in the development of self-driving vehicles have drastically improved accompanying technologies. Since the challenges held by Defense Advanced Research Projects Agency, the autonomous driving industry has increased significantly, and almost all the automotive companies have started to develop the technologies to deploy autonomous driving vehicles in the real world. Even though a lot of companies have been making efforts to achieve fully automated vehicles, the current technologies are not mature enough to be deployed in the real world yet, because self-driving vehicles need to respond to uncontrolled environments, such as moving objects, pedestrians, traffic lights, and unexpected work-zones. Among these uncontrolled environments, this thesis focuses on understanding road information and estimating states of traffic lights. Given that all of the traffic control devices are regularized in colors, color is one of the most significant features to be recognized. In order to accomplish such necessary a vision task, self-driving vehicles must incorporate cameras. Despite the fact that traffic control devices have their own regularized color and cameras can see those devices, they are still difficult to detect and recognize by autonomous vehicles. One of the biggest problems is that the color of those devices can be captured differently based on illumination. In this thesis, we investigate the problem of recognizing static objects using a monocular camera to assist self-driving vehicles in perceiving traffic control devices. The perception system, specifically a camera, should recognize the objects robustly regardless of the environment. Throughout this thesis, we exploit different color spaces and apply machine learning to reduce color variance. Also, we develop algorithms which compensate for illumination changes by considering the Sun position, to further improve the road sign recognition. Furthermore, we improve a traffic light state estimation which performs robustly under various illumination conditions. We deploy and demonstrate all of the algorithms in an autonomous vehicle.
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Ågren, Elisabeth. "Lateral Position Detection Using a Vehicle-Mounted Camera." Thesis, Linköping University, Department of Electrical Engineering, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-1984.

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A complete prototype system for measuring vehicle lateral position has been set up during the course of this master’s thesis project. In the development of the software, images acquired from a back-ward looking video camera mounted on the roof of the vehicle were used.

The problem of using computer vision to measure lateral position can be divided into road marking detection and lateral position extraction. Since the strongest characteristic of a road marking image are the edges of the road markings, the road marking detection step is based on edge detection. For the detection of the straight edge lines a Hough based method was chosen. Due to peak spreading in Hough space, the difficulty of detecting the correct peak in Hough space was encountered. A flexible Hough peak detection algorithm was developed based on an adaptive window that takes peak spreading into account. The road marking candidate found by the system is verified before the lateral position data is generated. A good performance of the road marking tracking algorithm was obtained by exploiting temporal correlation to update a search region within the image. A camera calibration made the extraction of real-world lateral position information and yaw angle data possible.

This vision-based method proved to be very accurate. The standard deviation of the error in the position detection is 0.012 m within an operating range of ±2 m from the image centre. During continuous road markings the rate of valid data is on average 96 %, whereas it drops to around 56 % for sections with intermittent road markings. The system performs well during lane change manoeuvres, which is an indication that the system tracks the correct road marking. This prototype system is a robust and automatic measurement system, which will benefit VTI in its many driving behaviour research programs.

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Books on the topic "Road cameras"

1

Victoria. Office of the Auditor-General. Road safety camera program. Melbourne, Vic: Victorian Government Printer, 2011.

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Bomans, Godfried. Merkwaardigheden rond de Camera obscura. Amsterdam: Amber, 1989.

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Ruth, Heidenreich, and Schaden Christoph, eds. Ruta de la Plata: Günter Derleth : Fotografien mit der Camera obscura. Köln: Schaden, 2006.

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Office, New Zealand Audit. Bringing down the road toll: The speed camera programme : report of the Controller and Auditor-General, Tumuaki o te Mana Arotake. [Wellington, N.Z: Audit Office, 2002.

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Bonine, Mindy L. Archaeological and historic archival background research and cultural resource survey for the proposed farm-to-market 511 road expansion project, Brownsville, Cameron County, Texas. Austin, Tex: Texas Dept. of Transportation, 2006.

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Clark, Darryl. Final environmental assessment, freshwater introduction south of LA Highway 82 project (ME-16), Cameron and Vermilion parishes, Louisiana. Lafayette, La: U. S. Fish and Wildlife Service, 2005.

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Stacey, May Humphreys. Uncle Sam's camels: The journal of May Humphreys Stacey supplemented by the report of Edward Fitzgerald Beale (1857-1858). San Marino, Calif: Henry E. Huntington Library and Art Gallery, 2006.

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On The Road With Your Digital Camera. Ilex, 2005.

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On the Road with Your Digital Camera. Course Technology PTR, 2004.

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Angling With Rod, Line & Camera. Book Guild Ltd, 2004.

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Book chapters on the topic "Road cameras"

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Ozcan, Koray, Anuj Sharma, Skylar Knickerbocker, Jennifer Merickel, Neal Hawkins, and Matthew Rizzo. "Road Weather Condition Estimation Using Fixed and Mobile Based Cameras." In Advances in Intelligent Systems and Computing, 192–204. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17795-9_14.

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Wei, Lijun, Bahman Soheilian, and Valérie Gouet-Brunet. "Augmenting Vehicle Localization Accuracy with Cameras and 3D Road Infrastructure Database." In Computer Vision - ECCV 2014 Workshops, 194–208. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16178-5_13.

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Wu, Chien-Chung, Yu-Xuan Lin, Deng-Xiang Hu, Chien-Chuan Ko, and Ji-Han Jiang. "The Warning System for Speed Cameras on the Road by Deep Learning." In Advances in Intelligent Systems and Computing, 768–75. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-15035-8_74.

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Van Crombrugge, Izaak, Ibrahim Ben Azza, Rudi Penne, Gregory Van Barel, and Steve Vanlanduit. "Fast Ground Detection for Range Cameras on Road Surfaces Using a Three-Step Segmentation." In Advanced Concepts for Intelligent Vision Systems, 479–90. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70353-4_41.

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Fang, Yong, Cindy Cappelle, and Yassine Ruichek. "Road Detection Using Fisheye Camera and Laser Range Finder." In Lecture Notes in Computer Science, 495–502. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07998-1_57.

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Asai, Toshihiro, Koichiro Yamaguchi, Yoshiko Kojima, Takashi Naito, and Yoshiki Ninomiya. "3D Line Reconstruction of a Road Environment Using an In-Vehicle Camera." In Advances in Visual Computing, 897–904. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89646-3_89.

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Panhuber, Christian, Bo Liu, Oliver Scheickl, Rene Wies, and Carsten Isert. "Recognition of Road Surface Condition Through an On-Vehicle Camera Using Multiple Classifiers." In Proceedings of SAE-China Congress 2015: Selected Papers, 267–79. Singapore: Springer Singapore, 2015. http://dx.doi.org/10.1007/978-981-287-978-3_24.

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Bo Bo, Nyan, Maarten Slembrouck, Peter Veelaert, and Wilfried Philips. "Distributed Multi-class Road User Tracking in Multi-camera Network For Smart Traffic Applications." In Advanced Concepts for Intelligent Vision Systems, 517–28. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-40605-9_44.

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Al-Shammari, Hammad, and Chen Ling. "Investigating the Effectiveness of a Traffic Enforcement Camera-System on the Road Safety in Saudi Arabia." In Advances in Intelligent Systems and Computing, 660–70. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93885-1_60.

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Gordeev, Alexey Y., and Vladimir A. Klyachin. "Determination of the Spatial Position of Cars on the Road Using Data from a Camera or DVR." In "Smart Technologies" for Society, State and Economy, 172–80. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59126-7_20.

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Conference papers on the topic "Road cameras"

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Pordel, Dana, and Lars Petersson. "Road Asset Detection Model Using Smartphones." In ICDSC 2017: International Conference on Distributed Smart Cameras. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3131885.3131936.

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Indu, S., Varun Nair, Shashwat Jain, and Santanu Chaudhury. "Video based adaptive road traffic signaling." In 2013 Seventh International Conference on Distributed Smart Cameras (ICDSC). IEEE, 2013. http://dx.doi.org/10.1109/icdsc.2013.6778234.

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Tay, Richard. "Do Speed Cameras Improve Road Safety?" In Second International Conference on Transportation and Traffic Studies (ICTTS ). Reston, VA: American Society of Civil Engineers, 2000. http://dx.doi.org/10.1061/40503(277)7.

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Asci, Guven, and M. Elif Karsligil. "Road Damage Detection via in Car Cameras." In 2020 28th Signal Processing and Communications Applications Conference (SIU). IEEE, 2020. http://dx.doi.org/10.1109/siu49456.2020.9302086.

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Vermeulen, E. "Automatic incident detection (AID) with thermal cameras." In Road Transport Information and Control Conference 2014 (RTIC 2014). Institution of Engineering and Technology, 2014. http://dx.doi.org/10.1049/cp.2014.0795.

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Sciberras, Ricardo, and Frankie Inguanez. "Road traffic flow estimation via public IP cameras." In 2018 IEEE 8th International Conference on Consumer Electronics - Berlin. IEEE, 2018. http://dx.doi.org/10.1109/icce-berlin.2018.8576229.

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Pelaez, G. A., D. Bacara, A. de la Escalera, F. Garcia, and C. Olaverri-Monreal. "Road detection with thermal cameras through 3D information." In 2015 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2015. http://dx.doi.org/10.1109/ivs.2015.7225695.

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Deigmoeller, Joerg, Nils Einecke, Oliver Fuchs, and Herbert Janssen. "Road Surface Scanning using Stereo Cameras for Motorcycles." In International Conference on Computer Vision Theory and Applications. SCITEPRESS - Science and Technology Publications, 2018. http://dx.doi.org/10.5220/0006614805490554.

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Pordel, Dana, and Lars Petersson. "A cost-benefit analysis of an ad-hoc road asset data collection system using fleet-vehicles." In ICDSC '15: International Conference on distributed Smart Cameras. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2789116.2789146.

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Molina-Cabello, Miguel A., Rafael Marcos Luque-Baena, Ezequiel Lopez-Rubio, Lipika Deka, and Karl Thurnhofer-Hemsi. "Road Pollution Estimation Using Static Cameras And Neural Networks." In 2018 International Joint Conference on Neural Networks (IJCNN). IEEE, 2018. http://dx.doi.org/10.1109/ijcnn.2018.8489581.

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Reports on the topic "Road cameras"

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Balali, Vahid, Arash Tavakoli, and Arsalan Heydarian. A Multimodal Approach for Monitoring Driving Behavior and Emotions. Mineta Transportation Institute, July 2020. http://dx.doi.org/10.31979/mti.2020.1928.

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Studies have indicated that emotions can significantly be influenced by environmental factors; these factors can also significantly influence drivers’ emotional state and, accordingly, their driving behavior. Furthermore, as the demand for autonomous vehicles is expected to significantly increase within the next decade, a proper understanding of drivers’/passengers’ emotions, behavior, and preferences will be needed in order to create an acceptable level of trust with humans. This paper proposes a novel semi-automated approach for understanding the effect of environmental factors on drivers’ emotions and behavioral changes through a naturalistic driving study. This setup includes a frontal road and facial camera, a smart watch for tracking physiological measurements, and a Controller Area Network (CAN) serial data logger. The results suggest that the driver’s affect is highly influenced by the type of road and the weather conditions, which have the potential to change driving behaviors. For instance, when the research defines emotional metrics as valence and engagement, results reveal there exist significant differences between human emotion in different weather conditions and road types. Participants’ engagement was higher in rainy and clear weather compared to cloudy weather. More-over, engagement was higher on city streets and highways compared to one-lane roads and two-lane highways.
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