Blind Spot Area Research Articles

Efficient radio resource allocation scheme for 5G networks with device-to-device communication

<p>A vital technology in the next-generation cellular network is device-to-device (D2D) communication. Cellular user enabled with D2D communication provides high spectral efficiency and further increases the coverage area of the cell, especially for the end-cell users and blind spot areas. However, the implementation of D2D communication increases interference among the cellular and D2D users. In this paper, we proposed a radio resource allocation (RRA) algorithm to manage the interference using fractional frequency reuse (FFR) scheme and Hungarian algorithm. The proposed algorithm is divided into three parts. First, the FFR scheme allocates different frequency bands among the cell (inner and outer region) for both the cellular and the D2D users to reduce the interference. Second, the Hungarian weighted bipartite matching algorithm is used to allocate the resources to D2D users with the minimum total system interference, while maintaining the total system sum rate. The cellular users share the resources with more than one D2D pair. Lastly, the local search technique of swapping is used for further allocation to minimize the interference. We implemented two types of assignments, fair multiple assignment, and restricted multiple assignment. We compared our results with existing algorithms which verified that our proposed algorithm provides outstanding results in aspects like interference reduction and system sum rate. For restricted multiple assignment, 60-70% of the D2D users are allocated in average cases.</p>

An integrated framework for knowledge based obstacle information system with image processing techniques

One of the most important reasons of car accidents are collisions with vehicles that are not visible to a driver. This is why driver warning systems are developed. The main threat for a driver on the highway comes from the surrounding vehicles especially when the driver is not aware of the close presence generally known as driver’s blind spot. An area in and around the vehicle that cannot be directly observed by the driver are known as blind spots. Image processing plays a vital role in this scenario to safe guard drivers from sudden obstacles and blind spots. The pre-processed sequences of images acquired using front and rear camera of a vehicle are considered to train the case base reasoning (CBR) model, which detects the presence of dangerous objects in the blind spot area. To distinguish near and far obstacles, the same CBR model is used with a specified threshold in the vehicle blind spot area. The proposed knowledge based obstacle information system obtains promising results with standard blind spot camera which can improve safety of the driver and has the potential to be applied in vehicular applications for the detection of obstacles and blind spot area.

Parametric Study of a Blind Spot Zone: Case Studies of Perodua Viva, Perodua Myvi and Proton Suprima


 
 
 The initial works on Blind Spot Zone (BSZ) identification presents the importance of the blind spot towards the everyday drive. The alarming collision rate in the blind spot zone especially when changing lanes has triggered the necessity of BSZ detection and warning system, focusing on a daily-used and affordable car segment. Such technologies are recently available at top variant cars. Therefore, a low cost yet facilitative BSZ detection and warning system is required. This paper presents the continuity experimental result of identification of the BSZ of three different car segments, i.e. Perodua Viva in a compact A-segment car, Perodua Myvi in a compact B-segment and Proton Suprima in a C- segment hatchback. Instead of using volunteered persons, this paper presents a dedicated platform with adjustable heights and camera to replace the human. The results show that the blind spot area and angle is affected by types of car model and driver heights. Also, the relationship between the blind spot area and angle with driver height can be shown in a straight line.
 
 

Optimization Shortest One-Way Path for Energy Saving Auto Robot Collecting Floating Garbage using Fast Approximate Nearest Neighbor Search

Floating garbage is a global problem that needs to be cleared and disposed of from the sea and rivers or canals. Mostly the floating plastic garbage directly affects the ecology and the aquatic animals. This paper proposes to develop a low-cost auto robot from waste materials in the household, school, or agriculture, then combines with the Internet of Things technology and focusing on the power energy saving with the highest accuracy in operation of floating garbage collection. ESP32 development kit board is the primary controller with a camera module and ultrasonic sensor for detecting the floating garbage while moving on the water’s surface in the boundary of the specified area, including the small canal, rectangle area, and no rectangle area. The mixed mode is a new method to define the shortest one-way path using a fast approximate nearest neighbor search for the auto robot to detect and collect the floating garbage on the water surface. It was combined between the stationary and the cover model. The mixed mode can eliminate the blind spot area that occurs when using the stationary mode and cover mode. As a result, the auto robot system’s accuracy in mixed mode is 98.3%. It was found that the development of the shortest one-way path for the auto robot collecting floating garbage by mixed mode provides the highest efficiency in detecting floating garbage and also helps to save overall costs, and the resource consumption is nearly half of cover mode consumption.

OPTIMALISASI JARINGAN NIRKABEL DENGAN METODE RSSI DI AIKOM TERNATE

Penelitian ini bertujuan untuk optimalisasi jaringan nirkabel dari titik buta atau blind spot di lingkungan kampus AIKOM Ternate. Area-area titik buta ini membuat pengguna jaringan nirkabel (dosen, staf, dan mahasiswa) tidak nyaman dikarenakan harus mendekat ke sumber jaringan (access point (AP)) terdekat agar dapat mengakses jaringan internet ataupun intranet. Dengan optimalisasi jaringan nirkabel ini, area jangkauan jaringan nirkabel disesuikan dengan kebutuhan agar tidak ada lagi titik buta. Penggunaan aplikasi Wireless Monitoring (Wirelessmon) untuk mendeteksi area jangkauan dari setiap AP yang dipasang, yang diukur adalah receive signal strength indicator (RSSI), termasuk penentuan penggunaan kanal dari setiap AP agar tidak tumpang tindih atau overlapping dalam penggunaan kanal pada setiap AP dan data rate yang mengalami peningkatan dan penurunan menyesuaikan dengan kualitas sinyal yang diterima. Hasil penelitian ini menunjukkan setelah dilakukannya optimalisasi dengan memasang AP pada lima titik. Optimalisasi jaringan nirkabel pada kampus AIKOM Ternate berhasil dengan RSSI -61dBm sampai dengan -44dBm, RSSI termasuk dalam kategori sangat bagus (very good). Disarankan pengukuran RSSI menggunakan software lebih dari satu sehingga dilakukan perbandingan, melakukan perbandingan RSSI yang diterima dengan throughput yang dihasilkan pada perangkat yang berbeda, dan pengelola jaringan dapat menggunakan hasil penelitian ini akan tetapi penggunaan perangkat dengan spesifikasi yang berbeda dengan yang digunakan dalam penelitian ini maka disarankan untuk melakukan pengambilan data ulang agar hasilnya maksimal.

Imaging simulation of a dark field imperfection evaluation arrangement for spherical optical surface based on reverse ray tracing

Imaging simulation of a dark field imperfection evaluation arrangement for spherical optical surface is proposed on the basis of reverse ray tracing. A typical evaluation arrangement for optical element surface imperfections seen in reflection under dark field condition is introduced. As for thin lens, it is a common case that light spots naturally emerge in the field of view (FOV) and dark field condition is no longer satisfied because multiple optical surfaces reflect rays into the camera lenses. A virtual arrangement model composed of a camera model, sample and light geometries, optical properties is established to further discuss the imaging of various lenses and imperfection geometries. The finite aperture camera model is adopted to generate abundant reverse rays for dark field scattering imaging of imperfections such as scratches and digs. But only a small portion of emitted rays whose paths can be traced to lights during propagation will contribute to the final image. Image simulation is performed by solving illuminance integral equation with Monte Carlo techniques. Simulated images of various lens geometries show the bright spots with respect to radius of curvature and centre thickness, which are numerically evaluated by our own defined spot coefficient. To eliminate the blind spot area caused by multiple reflections, lights are switched in turn and an image sequence is captured. A result image with reduced blind area is obtained by image fusion based on the accurate prediction of spot size and location for each frame in sequence. Imaging experiments of 4 thin lenses show that the distributions of spots correspond well with our simulation and proposed fusion method can reduce the blind area for spherical surface inspection while remains high sensitive for imperfections.

Visual-Based Motorcycle Detection using You Only Look Once (YOLO) Deep Network

In Malaysia and various Asian countries, use of motorcycle is growing in numbers, and the motorcycle is the dominating transport mode. In fact, the number of motorcycles per thousand people averaged over several major Asian cities is significantly higher than the average of the rest of the world. With growing use of motorcycle, blind spots have become one of major cause to road injuries and fatalities of motorcyclists in Malaysia. In this work, a visual-based detector is proposed to reduce the risk of blind spots in causing road injuries to motorcyclists. The objective of this detector is to provide alerts to the drivers of cars and other vehicles when there are motorcyclists in proximity to the vehicles especially around blind spot areas. In developing this solution, a variant of deep network called You Only Look Once (YOLO) is chosen as visual-based detector. This YOLO deep network is trained and tested using the 5811 collected images of motorcyclists. A benchmark with regards to its accuracy and speed is conducted by comparing this visual detector against several methods such as Aggregate Channel Features (ACF) and Faster Region Convolutional Neural Network (FRCNN). Results showed that YOLO detector is the most superior detector since it has the best average precision out of all detectors, and its inference time at 22.55 ms (44 fps) is able to provide real-time implementation. Besides, YOLO inference on machines without GPU still manage to achieve a commanding performance, which is on average, at 17 fps.

A Design and Development of a Wireless Sensor Network for Potential Monitoring and Localization

This paper presents an analysis of the received signal strength indicator (RSSI) from the radio frequency signals for human identification in an indoor wireless sensor network (WSN). Instead of using closed-circuit television as the existing security platform, this indoor safety system was improved with a convenient, cheap, and low-power solution. The system was developed using 20 of ESP8266-12F Wi-Fi modules transmitters and another 2 of ESP8266-12F as the access points located in 3 m × 3 m area of interest. With a suitable coordinate of sensor nodes, a WSN telemetry could be established to minimize the blind spot area and limit the movement of the intruder with a minimum area of 0.2 m2. The RSSI measurement was repeatedly conducted for three different conditions, in an empty room, with the presence of a single intruder and the presence of multiple intruders. Based on the RSSI values, we found that there are distinctive features of data that can be utilized as flags for classifying the three above conditions. Besides that, to justify the efficiency of system performance, we also examined the sensitivity of RSSI values towards the variation of temperature. Our results show that the RSSI average values for both morning and night were practically the same. However, during the afternoon, the RSSI signal strength fluctuated by − 1.0 dBm. These results motivate the development of an alarm system that only uses the RSSI statistics to detect human presence.

Signal Sensing on Heavy Goods Vehicle (Hgv) Blind Spot Area for Construction Logistics

This paper will explore the issues and the causes of accidents contributed by heavy goods vehicles (HGV) specifically instigated by blind spot. A blind spot is an area or zone that cannot be directly observe by the truck driver on their side mirror. Unfortunately, due to this, high number of accidents had happened because of HGV drivers were not alert or aware of another road user or pedestrian hidden in the blind spot area. This often occurred to heavy goods vehicle drivers. The aim of this paper is to understand the issue of HGV blind spot and identify the potential use signal sensing as an alert mechanism for the blind spot of a heavy goods vehicles. This is to detect other road users who are in the dangerous distance of the HGV. This study has revealed the holistic understanding of HGV blind spot issues and problem by finding the answer for research question initially outlined.

The Awareness of Blind Spot Area in Heavy Goods Vehicle (HGV)

This paper is to demonstrate the awareness of blind spot area in heavy goods vehicles (HGV) towards the road user. A blind spot is an area or zone that cannot be directly observe by the driver. Usually, the weaknesses in the blind spot area resulted in accident between the vehicle as many drivers did not alert or have awareness on the blind spot area especially in heavy goods vehicle. The objective of this paper is to identify the awareness on the blind spot area in heavy goods vehicles to the road users. To achieve the objective, a questionnaire survey has been conducted to the road user. About 100 drivers randomly pick to answer the survey and they are consisting of non-truck drivers that drive a car, van and motorcycle. The results reveal that even the respondents know about the blind spot in heavy good vehicle, but the awareness in the of blind spot area still less where they mistake on the exact location of blind spot. Typically, the only zone that they know as the blind spot for heavy goods vehicle is at the back area. Therefore, it is suggested that the government to improve the awareness of blind spot areas within the drivers through restructure the system inside driving school by give more attention on the blind spot for heavy goods vehicle, make a campaign on the awareness of blind spot in heavy goods vehicle to remind again the road users about the area of blind spots, and establish a standard direct vision in heavy goods vehicle.

AI Enabled Blind Spot Detection using Rcnn Based Image Processing

Due to the rapid increase in the rate of road accidents and traffic density, modern automobiles are equipped with intelligent systems like Adaptive cruise control and Lane Departure Warning System. Therear-view mirror can be effective to observe a limited range only and there are zones that cannot be viewed. This region is referred to as the blind spot. Therefore, we present a method to detect the vehicles from the side and the rear for Blind Spot Detection with vision system incorporating RCNN. Blind spot detection is a key technology among driver aids that provides 360 degrees of electronic coverage around the car during motion.The methodology presented in this paper uses two stereo cameras as input devices which constantly capture the images at the blind spot area and the information is passed to the main controlling unit. Potholes are also detected and the alert is sent to the nearby vehicle. The incorporation of Artificial Intelligence would help in enhancing the picture quality and blur or cancel the background images probable of misreading the target image. RCNN is used for the vehicle detection and for evaluating the relative distance between the vehicles.This technology allows us to provide a realistic environment for commercial vehicle drivers as they can’t monitor the side and rear-view mirrors all the time, making the whole driving experience more comfortable

Understanding the requirements for a blind-spot monitoring system on tractors from the operator’s perspective

Unintentional run-overs occur because the operator of a tractor is unable to physically see all around the machine. Therefore, there is a need to devise an effective blind-spot monitoring system for tractors to prevent unintentional run-overs. The purpose of the study was to identify the locations of blind spots around two types of tractors (i.e., with and without a front-end loader), with the ultimate goal of conceptualizing a blind-spot monitoring system capable of eliminating all existing blind spots. Grids were constructed around all four sides of the tractors to determine the presence of blind spots for drivers of varying sitting height (i.e., 5th, 50th and 95th percentile male for erect and slumped postures) at four horizontal planes representing people of varying stature who might be in the vicinity of the tractor (i.e., standing male, standing female, standing child, kneeling adult). Generally, the proportion of markers not visible decreased as the sitting height increased. Differences between erect and slumped sitting postures were not statistically different suggesting this variable could be ignored in the assessment of blind spots around tractors. The proportion of the markers not visible to the operator varied from 0 to 34%, with higher values observed for the tractor with the front-end loader installed. Values were as high as 42% of the markers not visible for the condition where a passenger was present in the passenger/trainer seat. Use of the existing rear-view mirrors eliminated only a small fraction of the blind spot area around the tractors. Through trial and error, it was determined that five and eight cameras would be required to fully detect the entire blind spot area around the two tractors selected for this study. A blind-spot monitoring system composed of five or eight cameras would create substantial additional monitoring burden for the tractor operator and, therefore, is not a feasible solution. A hybrid blind-spot monitoring system consisting of cameras and proximity sensors warrants further investigation.

Blind Spot Obstacle Detection from Monocular Camera Images with Depth Cues Extracted by CNN

The images from a monocular camera can be processed to detect depth information regarding obstacles in the blind spot area captured by the side-view camera of a vehicle. The depth information is given as a classification result “near” or “far” when two blocks in the image are compared with respect to their distances and the depth information can be used for the purpose of blind spot area detection. In this paper, the proposed depth information is inferred from a combination of blur cues and texture cues. The depth information is estimated by comparing the features of two image blocks selected within a single image. A preliminary experiment demonstrates that a convolutional neural network (CNN) model trained by deep learning with a set of relatively ideal images achieves good accuracy. The same CNN model is applied to distinguish near and far obstacles according to a specified threshold in the vehicle blind spot area, and the promising results are obtained. The proposed method uses a standard blind spot camera and can improve safety without other additional sensing devices. Thus, the proposed approach has the potential to be applied in vehicular applications for the detection of objects in the driver’s blind spot.

Blind spot monitoring at night-time using rear-view camera

The blind spot monitoring (BSM) is one of the key functions of advanced driver assistance system (ADAS). In this paper, we propose vision-based blind spot detection and tracking algorithm which is applicable at night-time. The feature of highly bright blobs due to the headlights of behind vehicle is employed for the detection of vehicles in blind spot area, because the appearance-based approach that can be used at daytime is not any more appropriate. We calculate the motion vectors of detected blobs to find an approaching vehicle and make headlight grouping to estimate its position by use of the projection map. We can successfully generate an alarm signal by detection and tracking the overtaking vehicle in blind spot area at night-time.

Blind spot monitoring at night-time using rear-view camera

The blind spot monitoring (BSM) is one of the key functions of advanced driver assistance system (ADAS). In this paper, we propose vision-based blind spot detection and tracking algorithm which is applicable at night-time. The feature of highly bright blobs due to the headlights of behind vehicle is employed for the detection of vehicles in blind spot area, because the appearance-based approach that can be used at daytime is not any more appropriate. We calculate the motion vectors of detected blobs to find an approaching vehicle and make headlight grouping to estimate its position by use of the projection map. We can successfully generate an alarm signal by detection and tracking the overtaking vehicle in blind spot area at night-time.

Reduction of blind spots in heavy transport vehicles through the optimisation of driver seat design

Statistics show that around 60% of road accidents occur due to poor visibility of driver and fatigue. Visibility of the driver while driving depends on the factors like blind spot, glare, weather and conditions of the head lamps of both own and the vehicle from opposite side. Among these, blind spot is the prime factor for accidents irrespective of time of travel. Reduction of blind spot area (Figure 1) improves the area of visibility of the driver which reduces the causes of the accidents. The driver seat design plays vital role in the driver's visibility while he is in the driver seat. This aim of this paper is to optimise the blind spot area by considering the design parameters used in the design of driver seat. To achieve this, a hybrid multi criteria decision-making (MCDM) approach is proposed by integrating computer aided engineering and technology (COPRAS) technique and fuzzy analytical hierarchy process (FAHP). The developed model is tested by a case study conducted in a public transport corporation located in the southern part of India.

Optimization of ecological node layout and stability analysis of ecological network in desert oasis：a typical case study of ecological fragile zone located at Deng Kou County(Inner Mongolia)

Optimization of coverage control strategy for spatial ecological nodes and the correct evaluation of the stability of spatial ecological network structure are the basis for understanding and optimizing the ecological network structure. The excellent ecological network structure, especially in arid and semi-arid regions in northwest China, is the basis of sustainable development of regional ecological environment. Based on this, Deng Kou County, the typical ecological vulnerable zone, was chosen as the study area. Minimum cumulative resistance model improved by emergy theory was used to extract the ecological network, and a new spatial layout strategy based on Tyson blind zone was constructed to optimize the spatial layout of ecological nodes. In the aspect of ecological network stability, the connectivity robustness and recovery robustness index were used to analyze structural robustness of un-optimized and optimized ecological network. The results showed that the spatial distribution of ecological source nodes at level 3, 4, and 5 with higher emergy value formed the desertification protection pattern. 1058 ecological nodes and 47,466 ecological corridors were extracted at the county level, which formed the ecological network in Deng Kou County. After optimization, the coverage area of ecological nodes reached 1870.03km2, and the area of Tyson blind spot was reduced to 1179.27km2. Compared with the un-optimized ecological nodes, the spatial distribution of the optimized ecological nodes was more homogeneous, the coverage index (CR) of the optimized ecological nodes reached 87.79%, and the distribution uniformity (U) of optimized ecological nodes was reduced to 0.3978. Based on the above, the robustness of the un-optimized and optimized ecological network structure was analyzed. The initial connectivity robustness of the un-optimized ecological network was 0.73, but the optimized ecological network was 1. The optimized network was more stable than the un-optimized network. For these two kinds of networks, the malicious attack was more destructive than the random attack. The node recovery robustness and edge recovery robustness under random attack were superior to those of the malicious attack. In summary, the spatial layout optimization of the ecological nodes improved the stability of the ecological network in Deng Kou County. This analysis provided insights that will support planning to adjust the ecological spatial structure and future sustainable development of ecological environment.

The causal effect of wrong-hand drive vehicles on road safety

Left-hand drive (LHD) vehicles share higher road accident risks under left-hand traffic because of blind spot areas. Due to low import prices, the number of wrong-hand drive vehicles skyrockets in emerging countries like Georgia, Kyrgyzstan and Russia. I identify the causal effect of wrong-hand drive vehicles on road safety employing a new “backward version” of the synthetic control method. Sweden switched from left-hand to right-hand traffic in 1967. Before 1967, however, almost all Swedish vehicles were LHD for reasons of international trade and Swedish customer demand. I match on accident figures in the period after 1967, when both Sweden and other European countries drove on the right and used LHD vehicles. Results show that right-hand traffic decreased road fatality, injury and accident risk in Sweden by approximately 30%. An earlier switch would have saved more than 4000 lives between 1953 and 1966.

A blind spot detection and warning system based on millimeter wave radar for driver assistance

This paper proposed a blind spot detection and warning system named BSDW for driver assistance under daytime and nighttime conditions. The proposed BSDW system includes system architecture platform, radar hardware system structure, intermediate frequency signal processing, radar target detection algorithms, blind spot area calibration method, system control strategy and system integration. Line frequency modulated continuous wave millimeter-wave radar was used to detect moving targets which come into the rear blind spot alarm area, including left, right and behind area of the subject vehicle. Based on Rayleigh clutter distribution model, a cell greatest, smallest and averaging constant false-alarm rate target detection algorithm named CGSA-CFAR was proposed to maintain higher detection rate and lower false-alarm rate by adjusting power detection threshold in time based on the noise power level, which was estimated according to the proposed target detection algorithm. The proposed system was implemented on embedded hardware platform and verified on the Chery Arrizo7 vehicle. Under urban daytime and nighttime conditions, the early warning rates of the proposed system were up to 98.20% and 98.21% respectively. The results show that the proposed system can detect the targets which come into the rear warning area of the subject vehicle and give early alarm to driver effectively in various urban road environments.

Hybrid optimisation model for blind spot reduction in heavy vehicles

The road accidents are common nowadays and the visibility of the driver is the prime factor in such accidents. Many lives as well as money have been lost during accidents. Reduction of blind spot area improves the area of visibility of the driver which leads towards the reduction of the accidents. This paper aims to optimise the blind spot area by considering the design parameters used in the design of rear view mirror in heavy vehicles. This is achieved by using a hybrid multi-criteria decision making (MCDM) approach by integrating complex proportional assessment of alternatives (COPRAS) technique and entropy measurement. The effectiveness of the developed model is proved by a case study conducted in a public transport corporation located in the southern part of India.