Read Books Online and Download eBooks, EPub, PDF, Mobi, Kindle, Text Full Free.
Embedded Deep Learning Multi Scale Object Detection Model For Advanced Driving Assistance System Using Real Time Distant Region Locating Technique
Download Embedded Deep Learning Multi Scale Object Detection Model For Advanced Driving Assistance System Using Real Time Distant Region Locating Technique full books in PDF, epub, and Kindle. Read online Embedded Deep Learning Multi Scale Object Detection Model For Advanced Driving Assistance System Using Real Time Distant Region Locating Technique ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
Book Synopsis Embedded Deep Learning Multi-Scale Object Detection Model for Advanced Driving Assistance System Using Real-Time Distant Region Locating Technique by :
Download or read book Embedded Deep Learning Multi-Scale Object Detection Model for Advanced Driving Assistance System Using Real-Time Distant Region Locating Technique written by and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Machine Learning and Embedded Computing in Advanced Driver Assistance Systems (ADAS) by : John Ball
Download or read book Machine Learning and Embedded Computing in Advanced Driver Assistance Systems (ADAS) written by John Ball and published by MDPI. This book was released on 2019-10-01 with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains the latest research on machine learning and embedded computing in advanced driver assistance systems (ADAS). It encompasses research in detection, tracking, LiDAR and camera processing, ethics, and communications. Several new datasets are also provided for future research work. Researchers and others interested in these topics will find important advances contained in this book.
Book Synopsis Object Detection for Autonomous Systems Operating Under Challenging Conditions by : Mazin Hnewa
Download or read book Object Detection for Autonomous Systems Operating Under Challenging Conditions written by Mazin Hnewa and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advanced Driver-Assistance Systems (ADAS) and autonomous systems, in general, such as emerging autonomous vehicles rely heavily on visual data and state-of-the-art deep learning approaches to classify and localize objects such as pedestrians, traffic signs and lights, and other nearby cars, to assist the corresponding vehicles maneuver safely in their environments. However, due to the well-known domain shift problem, the performance of object detection methods could degrade rather significantly under challenging scenarios such as low light and adverse weather conditions. The domain shift problem arises due to the difference between the distributions of source data used for training in comparison with target data used during realistic testing scenarios. The area of domain adaptation has been instrumental in addressing the domain shift problem encountered by many applications. In fact, domain adaptation frameworks for object detection methods have been providing powerful tools for handling a variety of underlying changes in probability distribution between training and testing data. In this dissertation, we first propose a novel integrated Generative-model based unsupervised training and Domain Adaptation (GDA) framework that improves the performance of a region-proposal based object detector under challenging scenarios. In particular, we exploit unsupervised image-to-image translation to generate annotated visuals that are representatives of a target challenging domain. Then, we use these generated annotated visuals in addition to unlabeled target domain data to train a domain adaptive region-proposal based object detector. We show that using this integrated approach outperforms both methods, unsupervised image translation, and domain adaptation, when they are used separately.℗ Despite the popularity of region-proposal based object detectors, such as Faster R-CNN and many of its variants, these detectors suffer from a long inference time. Therefore, such approaches are not the optimal choice for time-critical, real-time applications such as autonomous driving. As a result, in the second part of this dissertation, we propose a novel MultiScale Domain Adaptive YOLO (MS-DAYOLO) framework for the popular state-of-the-art real time object detector YOLO. MS-DAYOLO employs multiple domain adaptation paths and corresponding domain classifiers at different scales of the recently introduced YOLOv4 object detector. Building on our baseline MS-DAYOLO architecture, we introduce three novel deep learning architectures for a Domain Adaptation Network (DAN) that generates domain-invariant features. In particular, we propose a Progressive Feature Reduction, a Unified Domain Classifier, and an Integrated architecture.While RGB cameras represent the most popular imaging sensors used by ADAS systems and autonomous vehicles due to cost and related practical reasons, employing other modalities such as thermal and gated imaging sensors can significantly improve the detection performance under challenging conditions. However, these other types of sensors are expensive, and incorporating them into ADAS and autonomous vehicle platforms may cause design and manufacturing challenges. As a result, in the third part of this dissertation, we propose a new framework that utilizes Cross Modality Knowledge Distillation (CMKD) to improve the performance of RGB-only pedestrian detection in low light and adverse weather conditions without increasing computational complexity during inference. Specifically, we develop two CMKD methods that rely on feature-based knowledge distillation and adversarial training to transfer knowledge from a detector (teacher) that is trained using multiple modalities to a single modality detector (student) that is trained using RGB images only.℗ To validate the proposed approaches, we train and test them using popular datasets captured by vehicles driving under different conditions including challenging scenarios. Our experiments with the proposed approaches show significant improvements in object detection performance in comparison with state-of-the-art methods.
Book Synopsis Object Detection with Deep Learning Models by : S Poonkuntran
Download or read book Object Detection with Deep Learning Models written by S Poonkuntran and published by CRC Press. This book was released on 2022-11-01 with total page 345 pages. Available in PDF, EPUB and Kindle. Book excerpt: Object Detection with Deep Learning Models discusses recent advances in object detection and recognition using deep learning methods, which have achieved great success in the field of computer vision and image processing. It provides a systematic and methodical overview of the latest developments in deep learning theory and its applications to computer vision, illustrating them using key topics, including object detection, face analysis, 3D object recognition, and image retrieval. The book offers a rich blend of theory and practice. It is suitable for students, researchers and practitioners interested in deep learning, computer vision and beyond and can also be used as a reference book. The comprehensive comparison of various deep-learning applications helps readers with a basic understanding of machine learning and calculus grasp the theories and inspires applications in other computer vision tasks. Features: A structured overview of deep learning in object detection A diversified collection of applications of object detection using deep neural networks Emphasize agriculture and remote sensing domains Exclusive discussion on moving object detection
Book Synopsis Embedded Advanced Driving Assistance System Exploiting Multi-task Deep-learning-based Semantic Segmentation and Multi-object Detection by :
Download or read book Embedded Advanced Driving Assistance System Exploiting Multi-task Deep-learning-based Semantic Segmentation and Multi-object Detection written by and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Advanced Driver Intention Inference by : Yang Xing
Download or read book Advanced Driver Intention Inference written by Yang Xing and published by Elsevier. This book was released on 2020-03-15 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advanced Driver Intention Inference: Theory and Design describes one of the most important function for future ADAS, namely, the driver intention inference. The book contains the state-of-art knowledge on the construction of driver intention inference system, providing a better understanding on how the human driver intention mechanism will contribute to a more naturalistic on-board decision system for automated vehicles. Features examples of using machine learning/deep learning to build industry products Depicts future trends for driver behavior detection and driver intention inference Discuss traffic context perception techniques that predict driver intentions such as Lidar and GPS
Book Synopsis Embedded Platform Realization of Multiple Object Detection Based on Deep Learning Technique for Advanced Driver Assistance System by :
Download or read book Embedded Platform Realization of Multiple Object Detection Based on Deep Learning Technique for Advanced Driver Assistance System written by and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Region Based Convolutional Neural Networks for Object Detection and Recognition in ADAS Application by : Sachit Kaul
Download or read book Region Based Convolutional Neural Networks for Object Detection and Recognition in ADAS Application written by Sachit Kaul and published by . This book was released on 2017 with total page 51 pages. Available in PDF, EPUB and Kindle. Book excerpt: Object Detection and Recognition using Computer Vision has been a very interesting and a challenging field of study from past three decades. Recent advancements in Deep Learning and as well as increase in computational power has reignited the interest of researchers in this field in last decade. Implementing Machine Learning and Computer Vision techniques in scene classification and object localization particularly for automated driving purpose has been a topic of discussion in last half decade and we have seen some brilliant advancements in recent times as self-driving cars are becoming a reality. In this thesis we focus on Region based Convolutional Neural Networks (R-CNN) for object recognition and localizing for enabling Automated Driving Assistance Systems (ADAS). R-CNN combines two ideas: (1) one can apply high-capacity Convolutional Networks (CNN) to bottom-up region proposals in order to localize and segment objects and (2) when labelling data is scarce, supervised pre-training for an auxiliary task, followed by domain-specific-finetuning, boosts performance significantly. In this thesis, inspired by the RCNN framework we describe an object detection and segmentation system that uses a multilayer convolutional network which computes highly discriminative, yet invariant features to classify image regions and outputs those regions as detected bounding boxes for specifically a driving scenario to detect objects which are generally on road such as traffic signs, cars, pedestrians etc. We also discuss different types of region based convolutional networks such as RCNN, Fast RCNN and Faster RCNN, describe their architecture and perform a time study to determine which of them leads to real-time object detection for a driving scenario when implemented on a regular PC architecture. Further we discuss how we can use such R-CNN for determining the distance of objects on road such as Cars, Traffic Signs, Pedestrians from a sensor (camera) mounted on the vehicle which shows how Computer Vision and Machine Learning techniques are useful in automated braking systems (ABS) and in perception algorithms such as Simultaneous Localization and Mapping (SLAM).
Book Synopsis Moving Objects Detection Using Machine Learning by : Navneet Ghedia
Download or read book Moving Objects Detection Using Machine Learning written by Navneet Ghedia and published by Springer Nature. This book was released on 2022-01-01 with total page 91 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book shows how machine learning can detect moving objects in a digital video stream. The authors present different background subtraction approaches, foreground segmentation, and object tracking approaches to accomplish this. They also propose an algorithm that considers a multimodal background subtraction approach that can handle a dynamic background and different constraints. The authors show how the proposed algorithm is able to detect and track 2D & 3D objects in monocular sequences for both indoor and outdoor surveillance environments and at the same time, also able to work satisfactorily in a dynamic background and with challenging constraints. In addition, the shows how the proposed algorithm makes use of parameter optimization and adaptive threshold techniques as intrinsic improvements of the Gaussian Mixture Model. The presented system in the book is also able to handle partial occlusion during object detection and tracking. All the presented work and evaluations were carried out in offline processing with the computation done by a single laptop computer with MATLAB serving as software environment.
Book Synopsis Towards Real-time 3D Vehicle Detection from Monocular Images Using Deep Learning by : Nils Gählert
Download or read book Towards Real-time 3D Vehicle Detection from Monocular Images Using Deep Learning written by Nils Gählert and published by . This book was released on 2021* with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: One key task of the environment perception pipeline for autonomous driving is object detection using monocular RGB images. This task is usually limited to 2D object detection. The question arises whether 3D object detection is also possible using only monocular RGB images. In this dissertation, we evaluate this question specifically for 3D vehicle detection in monocular RGB images in the scope of driver assistance systems and autonomous driving. We use modern deep learning techniques without utilizing temporal information and a so-called 2D-3D lifting. In particular, this includes the estimation of 3D location, orientation, and the size of the object. In addition to a reliable and high-quality detection performance, autonomous driving systems require a short runtime. Therefore, we opt for the best possible trade-off between detection performance and runtime. Since the basis of any deep learning approach is high-quality data, we introduce a new dataset, Cityscapes 3D. This dataset is characterized in particular by its annotations with 9 degrees of freedom, as well as novel and improved evaluation metrics. We published a publicly available benchmark that allows research groups to assess and compare their methods for 3D object detection to those of other researchers. We develop improvements for 2D object detection and prove their effectiveness. Firstly, we increase the 2D detection performance by more than 5% using an adapted error function during training. Secondly, we develop vg-NMS that particularly supports 2D amodal object detection. With MB-Net, BS3D, and 3D-GCK, we develop three different approaches based on the 2D-3D lifting scheme. All developed approaches stand out for their comparably good detection performances and their short runtime. In direct comparison to MB-Net and BS3D, 3D-GCK does not require any post-processing. It estimates all 9 degrees of freedom of a vehicle in 3D space and also requires no prior knowledge about possible vehicle extents.
Book Synopsis Deep Learning based Vehicle Detection in Aerial Imagery by : Sommer, Lars Wilko
Download or read book Deep Learning based Vehicle Detection in Aerial Imagery written by Sommer, Lars Wilko and published by KIT Scientific Publishing. This book was released on 2022-02-09 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book proposes a novel deep learning based detection method, focusing on vehicle detection in aerial imagery recorded in top view. The base detection framework is extended by two novel components to improve the detection accuracy by enhancing the contextual and semantical content of the employed feature representation. To reduce the inference time, a lightweight CNN architecture is proposed as base architecture and a novel module that restricts the search area is introduced.
Book Synopsis Adaptive Embedded Systems for Autonomous Vehicles by : Maryam Hemmati
Download or read book Adaptive Embedded Systems for Autonomous Vehicles written by Maryam Hemmati and published by . This book was released on 2019 with total page 137 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modern cars are being equipped with powerful computational resources for autonomous driving systems (ADS) as one of their major parts to provide safer travels on roads. High accuracy and hard real-time requirements of ADS are addressed by HW/SW co-design methodology, which helps in offloading the computationally intensive tasks to the hardware part. This work presents a hardware accelerator for multi-scale pedestrian detection using the histogram of oriented gradients (HOG) feature extraction and support vector machine (SVM) classification. The hardware accelerator features parallel and pipelined architecture, as well as proposing a special memory hierarchy and access pattern. The implementation is based on an algorithmic modification to the conventional multi-scale HOG feature extraction to improve the real-time performance. It includes the scaling module which downscales the HOG features instead of resizing the original image. The repetition of the computationally intensive task of histogram generation is eliminated, and consequently higher throughput of 60 fps (frames per second) is achieved for HDTV (1920x1080) frame while maintaining reasonably low resource utilization. Hardware accelerator for vehicle detection, which is the other main task in ADS, is developed. The vehicle detection task is divided into three different scenarios of day, dusk, and dark with high, medium, and low environmental light, respectively. Detection in day and dusk is done by using HOG feature extraction, followed by SVM classification. Two different classifiers are trained for day and dusk scenarios to increase the detection accuracy during the dusk with limited environmental light. A novel deep learning method is presented for the detection of vehicles in the dark condition where the road light is very limited or unavailable. Hardware accelerators iii of vehicle detector are implemented featuring parallel and pipelined architecture. The accelerators satisfy real-time requirements of detection by processing the HDTV frame at the rate of 50fps. In complicated systems such as ADS, the limited hardware resources could become a limiting factor. This work presents a dynamically reconfigurable system for ADS which is capable of the real-time vehicle and pedestrian detection. To achieve higher reconfiguration throughput and minimize any overhead of partial reconfiguration (PR), a PR controller is presented for Zynq SoC which accelerates the reconfiguration process up to 97.5% of its theoretical maximum. By maintaining the resource requirements low enough, the existence of other functionalities of ADS on hardware will be possible.
Book Synopsis Visual Object Tracking with Deep Neural Networks by : Pier Luigi Mazzeo
Download or read book Visual Object Tracking with Deep Neural Networks written by Pier Luigi Mazzeo and published by BoD – Books on Demand. This book was released on 2019-12-18 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt: Visual object tracking (VOT) and face recognition (FR) are essential tasks in computer vision with various real-world applications including human-computer interaction, autonomous vehicles, robotics, motion-based recognition, video indexing, surveillance and security. This book presents the state-of-the-art and new algorithms, methods, and systems of these research fields by using deep learning. It is organized into nine chapters across three sections. Section I discusses object detection and tracking ideas and algorithms; Section II examines applications based on re-identification challenges; and Section III presents applications based on FR research.
Book Synopsis Visual Object Tracking from Correlation Filter to Deep Learning by : Weiwei Xing
Download or read book Visual Object Tracking from Correlation Filter to Deep Learning written by Weiwei Xing and published by Springer Nature. This book was released on 2021-11-18 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book focuses on visual object tracking systems and approaches based on correlation filter and deep learning. Both foundations and implementations have been addressed. The algorithm, system design and performance evaluation have been explored for three kinds of tracking methods including correlation filter based methods, correlation filter with deep feature based methods, and deep learning based methods. Firstly, context aware and multi-scale strategy are presented in correlation filter based trackers; then, long-short term correlation filter, context aware correlation filter and auxiliary relocation in SiamFC framework are proposed for combining correlation filter and deep learning in visual object tracking; finally, improvements in deep learning based trackers including Siamese network, GAN and reinforcement learning are designed. The goal of this book is to bring, in a timely fashion, the latest advances and developments in visual object tracking, especially correlation filter and deep learning based methods, which is particularly suited for readers who are interested in the research and technology innovation in visual object tracking and related fields.
Book Synopsis Applications of Deep Learning in Large-scale Object Detection and Semantic Segmentation by : Wei Xiang (Ph.D.)
Download or read book Applications of Deep Learning in Large-scale Object Detection and Semantic Segmentation written by Wei Xiang (Ph.D.) and published by . This book was released on 2019 with total page 128 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the massive storage of multimedia data and increasing computational power of mobile devices, developing scalable computer vision applications has become the primary motivation for both research and industrial community. Among these applications, object detection and semantic segmentation are two of the most popular topics which, in addition, serve as the fundamental features for many computer vision systems under platforms like mobile, healthcare, autonomous driving, etc. Inspired by the current and foreseeable trend, this thesis focuses on developing both effective and efficient object detection and semantic segmentation models, with the large-scale,publicly available data sets sourced for various applications.In the last several years, object detection and semantic segmentation have received large attention in the literature, and have been significantly advanced with the emergence of deep learning methods. Particularly, by applying Convolutional Neural Networks (CNNs), researchers have leveraged unsupervised features in modeling which greatly simplified the tasks of classification and regression, compared to using merely hand-crafted features in those traditional approaches. In object detection, however, there still exist many open research problems like integrating contextual information to the existing models, the missing relationship between proposal scales and receptive field sizes for different CNNs, etc. In this thesis,we study extensively such relationship, and further demonstrate that our statistical results can be used as a guideline to design both heuristically and efficiently new detection models, with an improvement of detection accuracy particularly for small objects.In semantic segmentation, we investigate many of the state-of-the-art methods and figure out that current research have largely focused on using complicated backbones together with some popular meta-architectures and designs which, in turn,leads to the problem of overtting and incapability for real-time tasks. To overcome this issue, we propose Turbo Unified Network (ThunderNet), which builds on a minimum backbone followed by a pyramid pooling module and a customized, two-level lightweight decoder. Our experimental results show that ThunderNet remains one of the fastest models that are currently available, while achieving comparable accuracy to a majority of methods in the literature. We also test ThunderNet with a GPU-powered embedded platform{NVIDIA Jetson TX2, whose results indicate that ThunderNet performs sufficiently fast and accurate, thus meeting the demands for embedded system. Finally, this thesis also surveys on the joint calibration methods for RGB-D sensor. We summarize the related work and present our quantitative evaluation results thereafter.
Book Synopsis A Novel Road Marking Detection and Recognition Technique Using a Camera-based Advanced Driver Assistance System by : Zongzhi Tang
Download or read book A Novel Road Marking Detection and Recognition Technique Using a Camera-based Advanced Driver Assistance System written by Zongzhi Tang and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Machine and Deep Learning Techniques for Real-time In-vehicle Fog Detection and Speed Behavior Investigation Utilizing the SHRP2 Naturalistic Driving Study Data by : Md Nasim Khan
Download or read book Machine and Deep Learning Techniques for Real-time In-vehicle Fog Detection and Speed Behavior Investigation Utilizing the SHRP2 Naturalistic Driving Study Data written by Md Nasim Khan and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The negative impact of reduced visibility on driver performance has been recognized as one of the major causes of motor vehicle crashes. Proper assessment of real-time visibility condition is therefore crucial for safe driving, especially during adverse weather including fog. Although many studies have investigated various visibility detection methods, most of them had several limitations and did not provide reliable real-time prediction capabilities. This study describes some unique and advanced data mining techniques for detecting real-time fog and visibility conditions utilizing video data from the Second Strategic Highway Research Program (SHRP2) Naturalistic Driving Study (NDS) dataset. In this study, Gray Level Co-occurrence Matrix (GLCM) features were extracted and significant texture features including Contrast, Correlation, Energy, and Homogeneity were selected as classification parameters for Support Vector Machine (SVM) and K-Nearest Neighbors (K-NN) classifiers. In addition, Convolutional Neural Network (CNN) Deep Learning technique was also examined for fog detection. Although the analysis was done initially on a dataset consisted of binary weather conditions including clear and fog, it has been successfully extended to include different levels of fog, i.e., near fog and distant fog. The classifications were conducted to leverage the SHRP2 NDS data by adding additional trajectory-level weather and visibility variables to the original data in an automated fashion. While the prediction accuracy of the first analysis was approximately 92% and 91% for the SVM and K-NN classifier, respectively, the CNN Deep Learning technique produced a far better classification results with an accuracy close to 99%. As expected, the prediction accuracy of the second analysis with more refined weather categories was relatively less compared to the first analysis where the SVM and the K-NN classifier produced an accuracy of about 89% and 88% respectively, and the CNN provided an accuracy of about 97%. The methods developed in this study are based on a single in-vehicle camera and can be used to detect daytime fog in real-time. This thesis also utilized the data from the SHRP2 NDS database to understand driver behavior in general and speed selection in particular during clear and foggy weather conditions. In this study, a preliminary analysis, and an ordered logit model were developed to evaluate driver speed behavior in fog and clear weather conditions. The preliminary analysis showed a Weibull speed distribution in heavy fog under free-flow conditions while the speeds were normally distributed in clear weather for the matching dataset (i.e., same driver, vehicle, route, and traffic state). Descriptive analysis indicated about 10% reduction in speed during near fog and about 3% reduction in speed during distant fog. The calibrated speed selection model found weather-related factors including fog, visibility, and surface conditions to have a significant impact on driver speed selection. For instance, results showed that drivers were more likely to select significantly lower speeds during foggy weather conditions. More specifically, the odds of drivers reducing their speeds from the posted speed limit were 1.31 and 1.28 times higher for drivers traveling in near fog and distant fog, respectively, compared to drivers who were driving in clear weather conditions. The results from this study will unlock new horizons and potentials in conducting adverse weather-related research utilizing the SHRP2 NDS data. The advanced Machine and Deep Learning techniques introduced in this study could be extended to other weather and surface conditions. Moreover, the findings from this study can also be incorporated into Advanced Driving Assistance Systems (ADAS) and Connected Variable Speed Limit (VSL) algorithms to improve their reliability and accuracy.
