Driver assistance systems that monitor driver intent, warn drivers of lane departures, or assist in vehicle guidance are all being actively research and even put into commercial production. It is therefore important to take a critical... more
Detecting objects within complex environments, such as urban settings, holds significant importance across various applications, including driver assistance systems, traffic monitoring, and obstacle detection systems. Particularly crucial... more
An Intelligent Driver Assistance System (I-DAS) for Vehicle Safety Modelling using Ontology Approach
This paper proposes an ontology modelling approach for assisting vehicle drivers through safety warning messages during time critical situation. Intelligent Driver Assistance System (I-DAS) is a major component of InVANET[12], which... more
Vehicle detection plays an important role in the development of an autonomous driving system. Fast processing and accurate detection are two major aspects of generating the autonomous vehicle detection system. This paper proposes a novel... more
Automated road lane detection is the crucial part of vision-based driver assistance system of intelligent vehicles. This driver assistance system reduces the road accidents, enhances safety and improves the traffic conditions. In this... more
Future mobility has to keep the safety level that is already achieved and shall even improve it. This paper reviews the research about driver behavior and assesses how far the work has proceeded in the field of assistance and... more
Th e paper deals with microscopic modeling of the traffi c fl ow using cellular automaton in the Tunnel Simulator based on PLC. Model with lane changing logic is implemented in the SCADA visualization screen of the tunnel with... more
There is a strong trend for increasingly sophisticated Advanced Driver Assistance Systems (ADAS) such as Autonomous Emergency Braking (AEB) systems, Lane Keeping Aid (LKA) systems, and indeed autonomous driving. This trend generates a... more
The research of safety applications in vehicular networks has been a popular research topic in an effort to reduce the number of road victims. Advances on vehicular communications are facilitating information sharing through real time... more
This article presents a multisensor fusion strategy for a novel road-matching method designed to support real-time navigational features within advanced driver assistance systems. In road navigation, context, integrity, reliability and... more
The knowledge about lanes and the exact position on the road is fundamental for many advanced driver assistance systems. In this paper, a novel iterative histogram based approach with occupancy grids for the detection of multiple lanes is... more
This paper describes a study analyzing the interaction of users with a computer system to show that user identification is possible only by analyzing the user interaction behavior. The identification of the user can be done with a... more
The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and... more
This paper elucidates and discusses some aspects of cultural adaptability which aid usability and universal access. We describe the concept, influence and Use Cases of cultural adaptability in driver information and assistance systems... more
This paper describes pitfalls experienced during intercultural humanmachine interaction (HMI) development projects in Japan and Korea and how they can be explained from a scientific point of view with the goal of deriving recommendations... more
This paper reports preliminary work on an experimental vehicle and simulators and their initial use towards the development of driver adaptive warning and assistance systems that will be triggered by driver inattention monitor in the... more
In conditionally automated driving, drivers do not have to monitor the road, whereas in partially automated driving, drivers have to monitor the road permanently. We evaluated a dynamic allocation of monitoring tasks to human and... more
The research of safety applications in vehicular networks has been a popular research topic in an effort to reduce the number of road victims. Advances on vehicular communications are facilitating information sharing through real time... more
This paper presents the 60-s time-resolution segment from our 50-h total sleep deprivation (TSD) dataset (Aidman et al., 2018) [1] that captures minute-by-minute dynamics of driving performance (lane keeping and speed variability) along... more
Human errors are the cause of most traffic accidents, with drivers' inattention and wrong driving decisions being the two main sources. These errors can be reduced, but not completely eliminated. That is why Advanced Driver Assistance... more
Motion prediction for holonomic objects in unstructured environments is an ambitious task due to their high freedom of movement compared with non-holonomic objects. In this paper, we present a method for inferring the future goal of... more
Sleepiness is a major contributor to motor vehicle crashes and shift workers are particularly vulnerable. There is currently no validated objective field-based measure of sleep-related impairment prior to driving. Ocular parameters are... more
This paper presents a model of stereo vision system for intelligent vehicles to avoid forward car collision. Approached vehicle extraction and distance estimation algorithm based on dense edge map calculation and ROI filter. Primary... more
The safety in the transport sector is an indicator for sustainability. Given its importance for the overall economic development and the malfunctions caused due to a possible drawback in the transport sector development different systems... more
In many highway traffic accidents, the main cause is straight line detection. So as to avoid accidents straight line detection is an important component of intelligent transportation systems. This paper describes different lane marking... more
![1) Y. Wang, E. K. Teoh, and D. Shen introduced lane detection and tracking using b-snake [5]. Here lane detection and trac king is proposed without using any cameras parameters. B-Spline can form any arbitrary shape by a set of control points so the B-Snake based lane model is able to describe a wider range of lane structures. By using the knowledge of the perspective paral el lines the problems of det ecting both sides of lane markings (or boundaries) have been merged here as the problem of detecting the mid-line of the lane. For providing a good initial position for the B-Snake, a robust algorithm called CHI hod by Minimum Mean Square Error (MMS hod is robust against noise, shadows, and illumination variations in the captured road images. It is applicable to the met met EVP is proposed. Also to d dash and the solid paint line roads also to the mar etermine the control points of the B-Snake model, a minimum error E) is proposed by the overall image forces on two sides of lane. This ked and the unmarked roads. Lane model plays an important role in lane detection. Here construction of the 2D lane model is done by assuming that the two sides of the road boundaries are parallel on the ground plane as shown in Fig. | (a). It is assumed that the right side of road is the shifted version of the left side of road at a distance, D = ( X,_ Xj); along the x axis in the ground plane. Here X, and X;1s the x coordinates of the two correspondence points P;(X1, y) and P,(X,, y) in the ground plane. After projection from ground planes the horizontal distance is given by,](https://figures.academia-assets.com/108232125/figure_001.jpg)
![Fig.2. IPM coordinates. Left: the coordinate axes (world, camera, and image frames). Right: definition of pitch a and yaw B angles. In this algorithm there is a work on the red channel, which gives better images for white and yellow lanes than converting it to gray scale. There should be work done further to improve this algorithm. The further work can be done to classify different lane boundaries by using the color information i.e. white solid lines, double yellow lines etc. This can remove the false positives due to curbs which are detected as lanes, as well as confusing writings on the streets. The drawback of this algorithm for is it does not give well accurate results lane detection [3].](https://figures.academia-assets.com/108232125/figure_002.jpg)
This paper presents a design methodology for driving simulator scenarios in which autonomous and controlled surrounding vehicles are combined. The main motives are to achieve both a high realism and high reproducibility. The methodology... more
Accuracy in detecting a moving object is critical to autonomous driving or advanced driver assistance systems (ADAS). By including the object classification from multiple sensor detections, the model of the object or environment can be... more
Timber quality control is undoubtedly a very laborious process in the secondary wood industry. Manual inspections by operators are prone to human error, thereby resulting in poor timber quality inspections and low production volumes. The... more
![Generally, there are two types of research problems when dealing with AVI in the wood industry, namely, defect detection and defect identification. Table 2 lists the previous AVI studies related to the detection and identification of timber defects. The difference between these two approaches depends on their final output, where the detection approach focuses on the process of locating wood defects based on a computer vision technique such as segmentation [31]. In working on the problem of detection, Luo and Sun [18] suggested a local binary threshold segmentation algorithm for the detection of wood image defects by calculating the threshold based on the mean, standard deviation and extreme value of the window. Their research managed to achieve an accuracy of 92.6% for wood defect images with a complex background. In addition, Pahlberg [32] conducted to further investigate the use of vibrothermography for the detection of cracks in parquet lamellae, where an accuracy of 80% was achieved by capturing the texture image using completed local binary pattern histograms and segmenting the cracks with background suppression and thresholding. Likewise, the use of the three-dimensional stress wave imaging method for detecting internal defects in wood on PT-Kriging (particle swarm optimization (PSO) Top-k Kriging) achieved a relative error ranging from 11.57% to 28.74% compared to the use of the TIDW algorithm, which had a relative error (%) between 8.69 and 46.28 [33].](https://figures.academia-assets.com/107026359/table_001.jpg)
![Table 4. Previous studies on wood defect identification using CNN architecture 4. DISCUSSSION This paper reviewed previous works on the automated inspection of timber surface defect: including various kinds of approaches targeting both the detection and identification of timber defects. Whil some of the approaches demonstrated good performance, most of them were still in the experimental phas: The majority of the challenges encountered during the industrial deployment of these approaches in sma and medium-sized businesses were associated with high investment costs, and the AVI integrated wit artificial intelligence appeared to be the most plausible approach owing to its ease of use and minim: operating costs. Although the capabilities of the AVI were limited to the inspection of surface defects, at th very least, they enhanced the inspection process. Nowadays, the trend is moving towards more contemporar machine learning/deep learning approaches, particularly in neural network architectures, for their outstandin performance. A brief comparison of contemporary algorithms and techniques for either the detection « identification of various wood defects was discussed. Machine learning and deep learning are the two type of artificial intelligence models used in the identification of timber defects. Although both learning mode! perform well in tasks involving parameter prediction and pattern identification, the chosen models at dependent on how data is provided to the system. Machine learning algorithms are designed to learn an increase their accuracy by analyzing labelled data, with the objective of producing further outputs with mot sets of data. While the accuracy of machine learning improves with training, human intervention is require when the actual output changes unexpectedly. The deep learning algorithms, however, do not require huma intervention as the nested layers in the networks put data through hierarchies of different concepts, whic eventually learn through their own errors. Besides, the convolutional neural network architecture in dee learning models has proven its capabilities by producing record-breaking results on highly challengin datasets, while leveraging supervised learning [96]. For the identification of wood defects, feature extractio plays an important role in machine learning, where the extraction of characteristic quantities has a direc impact on the rate of image identification. Besides, a variety of feature extraction methods are availabl under digital image processing such as color, shape, and texture features [50]. TRA wn RL tawtuien fLrantyier neteenntingn mnthada avinhkh nn thn KOI MANA navn tN TDD avawrnnenr a en) OR 4. DISCUSSSION for the last few layers to learn the features in the respective domains, as the model had obtained essentia features such as corners in their first few layers. Hence, transfer learning has proven to be a very effective approach for the training of neural networks with a limited dataset. Additionally, implementing both transfer earning and data augmentation techniques in CNN does appear to be a viable approach in tackling limited dataset issues as well as improving CNN classification performance as demonstrated in [90]. A convolutiona neural network (CNN) architecture is made up of three primary neural layers, namely a convolutional layer, pooling (subsampling) layer, and fully-connected layer [91]. With the current success of the implementation of the CNN in the computer vision domain, a number of well-known CNN architectures have been developed throughout the image processing field, especially with regard to the identification of timber defects as shown in Table 4. The AlexNet, VGG, and deep convolutional generative adversarial network (DCGAN) are among the architectures used in CNN to improve the accuracy of identification with their own configurations and contributions [92]. Urbonas [30] recommended the utilization of a faster region-based convolutional neura network to identify wood veneer surface defects, with the greatest average accuracy of 80.6% being achieved using a pre-trained ResNet152 neural network model. Nonetheless, a comparison of the CNN model to the SIFT+k-NN model [27] demonstrated the superiority of deep learning, with the CNN model achieving an accuracy of 88.09% for the identification of knots in contrast to its counterpart.](https://figures.academia-assets.com/107026359/table_003.jpg)
Methods for the evaluation of traffic safety effects of Antilock Braking System (ABS) and Electronic Stability Control (ESC)-a literature review Abstract (background, aim, method, result) max 200 words: In today's vehicles, active safety... more
The research of safety applications in vehicular networks has been a popular research topic in an effort to reduce the number of road victims. Advances on vehicular communications are facilitating information sharing through real time... more
This chapter starts with an introduction illuminating the theoretical background necessary for taking culture into account in Human Computer Interaction (HCI) design. Definitions of concepts used are provided followed by a historical... more
In this paper, a method-mix for intercultural user interface design (IUID) is explained and exemplified by an application example to elucidate why and how cultural aspects play a role in UI design and usability/UX engineering. The readers... more
There are wide and recent research efforts to extract location information of people in certain spaces, with new services using this information in real space being launched in the market. In particular, there are emerging services and... more
Ambon City is the capital of Maluku Province. The rapid growth of transportation facilities in Ambon City has resulted in traffic problems such as traffic accidents. This study aims to model the probability of four-wheeled vehicle... more
Driver gaze mapping is crucial to estimate driver attention and determine which objects the driver is focusing on while driving. We introduce DGAZE, the first large-scale driver gaze mapping dataset. Unlike previous works, our dataset... more
Over the years, many systems have addressed the problem of location sensing. In the past, geographic positioning systems (GPS) have been widely used to track moving objects located outside environments. These systems have several problems... more
Intersections are among the most complex traffic situations that motorists encounter, which is reflected by the fact that in Europe more than 40 percent of accidents resulting in injury occur at intersections. In order to support the... more
The morning commute home is an especially vulnerable time for workers engaged in night shift work due to the heightened risk of experiencing drowsy driving. One strategy to manage this risk is to monitor the driver's state in real... more
In this paper a nonlinear observer is presented in order to estimate the angular velocity of both upper and lower wheels of the PC controlled ABS Laboratory setup, from INTECO Ltd. The system simulates the dynamics of a quarter car model.... more
Action and intention recognition of pedestrians in urban settings are challenging problems for Advanced Driver Assistance Systems as well as future autonomous vehicles to maintain smooth and safe traffic. This work investigates a number... more
This study proposes a vision-based intelligent nighttime driver assistance and surveillance system (VIDASS system) implemented by a set of embedded software components and modules, and integrates these modules to accomplish a... more
ESC (Electronic Stability Control) was introduced on the mass market in 1998. Since then, several studies showing the positive effects of ESC has been presented.
Driving style can characteristically be divided into two categories: "typical" (non-aggressive) and aggressive. Understanding and recognizing driving events that fall into these categories can aid in vehicle safety systems.... more
Numerous measures shown to depict reliably the developing drowsy state include changes in human electroencephalographic (EEG) output, 10,11 electrooculography (EOG) or other oculographic measurements, 7,12,13 heart rate variability, 14-16... more
From a legacy of almost no experience of charging for road use in the UK, except for a small number of tolled estuarial and river crossings and some innovative flirtations with congestion charging trials, such as the Cambridge congestion... more
First of all, the most important content of the ISO/TS 15007-2:2001 standard for performing eye tracking experiments will be described. The following text includes a detailed description of how eye/gaze experiments using the Dikablis eye... more
Future mobility has to keep the safety level that is already achieved and shall even improve it. This paper reviews the research about driver behavior and assesses how far the work has proceeded in the field of assistance and... more
This paper explores the performance of an Advanced Driving Assistance System (ADAS) during navigation in urban traffic and a winter condition. The selected ADAS technology, Mobileye, has been integrated into a hydrogen electric vehicle. A... more