Zigbee-Based Collision Avoidance System in Blind Spot and Heavy Traffic using Ultrasonic Sensor

Blind spot detection is an important feature of Advanced Driver Assistance Systems (ADAS). In this paper, we provide a camera-based deep learning method that accurately detects other vehicles in the blind spot, replacing the traditional higher cost solution using radars. The recent breakthrough of deep learning algorithms shows extraordinary performance when applied to many computer vision tasks. Many new convolutional neural network (CNN) structures have been proposed and most of the networks are very deep in order to achieve the state-of-art performance when evaluated with benchmarks. However, blind spot detection, as a real-time embedded system application, requires high speed processing and low computational complexity. Hereby, we propose a novel method that transfers blind spot detection to an image classification task. Subsequently, a series of experiments are conducted to design an efficient neural network by comparing some of the latest deep learning models. Furthermore, we create a dataset with more than 10,000 labeled images using the blind spot view camera mounted on a test vehicle. Finally, we train the proposed deep learning model and evaluate its performance on the dataset.

The government of India aims at “Swachh Bharat Mission” and for that we need smart city with smart streets enabled with smart garbage monitoring system. We see garbage bins around us placed at public places overflowing with due to increase in waste and results into unhygienic conditions for people and leads to deadly diseases. Thus to improve this situation, we proposed this project. In our proposed system, there are multiple smart garbage trash bins on a microcontroller board platform (Arduino Board) located throughout any city or the campus or hospital. The Arduino Board is interfaced with GSM modem and ultrasonic sensor. Once the level of threshold is being crossed, then ultrasonic sensors will trigger the GSM module which in turn continuously alert the authorised person by sending SMS reminder after until the dustbin is cleaned. Beside this, we will also create the central system that will keep showing us the current status of garbage on mobile web browser with html page by wi-fi. With the help of this, we will create shortest path for garbage collection vehicles using Dijkstra Algorithm. This is real time waste management by using smart trash bins that can be accessed anytime anywhere by the concerned person. Keywords: IoT; Smart city; Smart Garbage Bins; Arduino; Ultrasonic Sensors; GSM; Real time Analytics; Route Optimization

Sensor selection is an essential aspect of blind spot detection (BSD) systems. Indeed, we must choose the sensors appropriately to accomplish high accuracy and performance in any driving condition. Each sensor has exclusive properties which are suitable for a few specific circumstances. Therefore, a comprehensive study is warranted to determine the optimum number of sensors and the type of sensors for BSD. Although sensors have some deficiencies which can deteriorate the whole system's performance, a combination of multiple types of sensors together with data fusion methods in most cases can substantially compensate for sensors imperfection. In this paper, we have concentrated on multi-sensor fusion in BSD system and its advantages which cannot be achieved in a single-sensor BSD system. A sensing structure for the BSD system is proposed considering the indispensable factors in BSD coupled with sensors constraints, features, and specifications.

Radar is a key technology in the safety system of a modern vehicle. Automotive radars are the critical sensors in advanced driver-assistance systems, which are used in adaptive cruise control, collision avoidance, blind spot detection, lane change assistance, and parking assistance. The book covers all the modern radars used in automotive technology. A long-range radar mounted in the front of the vehicle is usually for adaptive cruise control. The medium range radars mounted in the front and rear provide wider coverage than the long-range radars and they can be used for cross traffic alert and lane change assistance. The corner mounted short range radars support parking aid, obstacle/pedestrian detection and blind spot monitoring. In real applications, these radars usually work together to provide more robust detection results. In this book, we also recognize that the future of automotive radars should not only address conventional exterior applications, but also play important roles for interior applications, such as gesture sensing for human-vehicle interaction and driver/passenger vital signs and presence monitoring. The book is aimed at those radar engineers who are working on automotive applications.

Roughly 500,000 traffic accidents involving Heavy Duty Vehicles (HDVs) occur yearly in the United States. The primary cause for these accidents stems from the presence of substantial blind spots, regions around the HDV not visible to the driver. Existing solutions to blind spots are the use of camera monitors, sensors, or additional mirrors, but these options have limited success when working in varying weather conditions or different road types. This article proposes a blind spot safety protection system targeting heavy duty vehicle blind spots based on multi-sensor information fusion. The system evaluates the location of pedestrians and other vehicles near the heavy duty vehicle through ultrasonic, touch, and vision sensors. The driver is alerted of potential dangers, such as a nearby pedestrian, through the monitor and a wristband. In the scenario of a crash, pedestrians, external vehicles, and the heavy duty vehicle itself are protected by external airbags. This paper investigates optimal camera threshold settings under different light conditions, and evaluates the field of vision of the camera monitor subsystem. The experimental results indicate that the designed system can protect a blind spot area of approximately 80.3m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . The use of multiple sensors can effectively improve the robustness of safety detection. The designed system has extraordinary significance for protecting pedestrian, vehicle, and HDV safety.

Human respiration detection based on Wi-Fi signals does not require users to carry any device, hence it has drawn a lot of attention due to better user acceptance and great potential for real-world deployment. However, recent studies show that respiration sensing performance varies in different locations due to the nature of Wi-Fi radio wave propagation in indoor environments, i.e., respiration detection may experience poor performance at certain locations which we call "blind spots". In this paper, we aim to address the blind spot problem to ensure full coverage of respiration detection. Basically, the amplitude and phase of Wi-Fi channel state information (CSI) are orthogonal and complementary to each other, so they can be combined to eliminate the blind spots. However, accurate CSI phase cannot be obtained from commodity Wi-Fi due to the clock-unsynchronized transceivers. Thus, we apply conjugate multiplication (CM) of CSI between two antennas to remove the phase offset and construct two orthogonal signals--new "amplitude and phase" which are still complementary to each other. In this way, we can ensure full human respiration detection. Based on these ideas, We design and implement a real-time respiration detection system with commodity Wi-Fi devices. We conduct extensive experiments to validate our model and design. The results show that, with only one transceiver pair and without leveraging multiple sub-carriers, our system enables full location coverage with no blind spot, showing great potential for real deployment.

This paper focuses on preventing collisions between cyclists and heavy goods vehicles (HGVs). A collision avoidance system, which is designed to avoid side-to-side collisions between HGVs and cyclists, is proposed. The cyclist's motion relative to the HGV is measured with an array of ultrasonic sensors. The detected distances from ultrasonic sensors are processed in real time to construct a smooth trajectory for the cyclist. The controller assumes constant acceleration and constant yaw rate for both the HGV and the cyclist and extrapolates the relative motion forward in time. The HGV's brakes are engaged if a collision is predicted. A prototype system was built and fitted onto a test truck. The proposed collision avoidance system was tested in real time and proved to be effective within certain speed ranges.

Mobile robots have been extensively used in manufacturing plants for inter-logistic transportation in recent years. This paper covers a phased array antenna design for a millimeter wave radar system to improve lidar-based navigation systems' safety and environmental consciousness. The K-band phased array antenna, when integrated with 24 GHz Frequency-Modulated-Continuous-Wave (FMCW) radar, not only enhances the accuracy of the 2-D Area Scanning lidar system but also helps with the safe operation of the vehicle. The safety improvement is made by covering blind spots to mitigate collision risks during the rotations. The paper first reviews the system-level details of the 2D lidar sensor and shows the blind spots when integrated into a Mobile Robot prototype. Then continues with the inclusion of an FMCW Low-Speed Ramp radar system and discusses the design details of the proposed K-band antenna array, which will be integrated with a radar sensor.

In this paper, we propose a real-time detection algorithm using a MCT AdaBoost classifier which detects two-wheeler in a blind spot. The proposed algorithm uses a cascade classifier generated by AdaBoost learning based on the MCT feature vector. The MCT AdaBoost classifier is composed of weak classifiers as many as the number of pixels of the detection window, and each pixel becomes a weak classifier. The smaller the detection window, the faster the processing speed, and the larger the detection window, the greater the accuracy. The proposed algorithm uses two classifiers with different detection window sizes. The first classifier generates candidates quickly with a small detection window. The second classifier verifies the generated candidates with a large detection window. Accordingly, the proposed algorithm supports fast and reliable two-wheeler detection. Also, the proposed algorithm uses a wheel classifier in order to detect an adjacent two-wheeler in the blind spot which is well not detected by two-wheeler classifiers. Experimental results show that the proposed algorithm has faster processing speed and higher detection rate than a single classifier without generating candidates.

In every day our daily needs we are depending on transport system. Transport mode may be two wheeler, four wheelers etc. In metro there is a huge traffic of vehicles, there may be chances of accidents. The most of the accidents are due to inability of the driver to control the vehicle. In order to improve the safety of the driver, need to add some auto control for the vehicle. The driver assistance is one of the auto control system. In driver assistance there are many subsystems. Adaptive cruise control is one of the major driver assistance sub systems. The Adaptive cruise control was implemented by using Real time operating system with preemptive priority based scheduler. It takes more memory and time. Time triggered cooperative scheduler is one of the most efficient and predictable scheduler. It is designed for controlling adaptive cruise control tasks. It is implemented using ARM7 based Microcontroller, ultrasonic sensor, Throttle valve sensor and speed sensor. The whole system is placed in host vehicle, vary the speed of the host vehicle so that to keep minimum distance between host and target vehicle. The TTC scheduler for adaptive cruise control is simulated using keil IDE tool. The application tasks are controlled by using efficient Time triggered cooperative scheduler. The adaptive cruise control is controlled by TTC scheduler is written in embedded `c'. The simulated results are compared with existing cooperative schedulers. It is proved from the results that the TTC scheduler takes less time and less memory.

A collision avoidance system, also known as a pre-crash system, forward collision warning system, or collision mitigation system, is a sophisticated driver-assistance system that aims to avoid or mitigate the severity of a collision. For this research, collision avoidance system will be fabricating to show that this system can detect avoidance range before apply the braking action to prevent collision. The ultrasonic sensor will be used in this system to detect the avoidance range. In this collision avoidance system, there will be uses of Field Programmable Gate Array (FPGA) and Complex Programmable Logic Device (CPLD). This research will observe how to implement FPGA and CPLD in the collision avoidance system using VHSIC Hardware Description Language (VHDL). The VHDL will be done in Quartus II 15.0 Software. In this research, Terasic DE-10 Standard board has been used. It contains FPGA microcontroller model Cyclone V SoC 5CSXFC6D6F31C6N. Max II board is used because it contains CPLD microcontroller model EPM240T100C5.

This paper details the design and implementation of an embedded real-time system to fuse data from two 77 GHz radars, three 24 GHz radars and a video lane detection system fitted to a vehicle to track the movement of other vehicles in the local environment. The system demonstrates excellent tracking of vehicles as they pass through regions covered by single and multiple sensors as well as regions with no sensor coverage where tracking continues despite no new measurements. Such a system facilitates future driver assistance functions such as lane change support and blind spot detection.

Riders of motorcycles must take additional safety measures to safeguard their bodies. The most crucial place to begin is with head protection. In motorcycle accidents, the head and brain are most likely to sustain injuries. When drivers and passengers wear helmets, their chances of survival are significantly greater than when they do not. However, this does not guarantee the safety of its users and passengers because fatal injuries to the lower body may still occur even while wearing a helmet. The main objective of this research is to foster the creation and development of an automated motorcycle helmet with the ability to monitor and keep track of the helmet status in case of accidents occur to reduce the increasingly major public issue regarding motorcycle accidents. Microcontrollers are used to convert the analog signals such as vibrations produced on the helmet into digital signals for further processing. A camera is also placed to the upper body of the helmet to record and provide evidence in the event of a road accident. This project is an automated Helmet with GPS and GSM system to determine the exact location of a motorcycle helmet, Blind Spot to detect nearby vehicles and impact detection to detect whether an accident occurred with a mobile application for notifications and serves as the locator of the helmet's location using Google Maps.

Various collision avoidance systems (CASs) have been developed and employed in human-operated vehicles as well as more recently in autonomous vehicles. Most of the existing CASs perform an override function to actuate automatic emergency braking in a critical situation based on the current traffic information obtained from in-vehicle sensors or short-range vehicular communications. These CASs focus on the critical situation in the vicinity of the subject vehicle, which means they may have negative influences on the subject vehicle and its following vehicles, particularly when the leader vehicle of a platoon with short headway applies harsh braking to mitigate an impending collision risk. This study proposes a risk predictive CAS (RPCAS) which executes predictive deceleration with mild braking in advance to prevent a potential rear-end collision by predicting the collision risk arising from a downstream site. To evaluate the performance of the RPCAS, the proposed system is compared with several existing CASs in various car-following cases based on a microscopic traffic simulation. The simulation results show that the RPCAS can effectively reduce the rear-end collision risk with less harsh braking compared with the existing CASs. Furthermore, the RPCAS enables vehicles arriving from upstream to anticipate a potential crash, which provides them with sufficient time to reduce their current speeds proactively. The research findings suggest that the proposed system can attenuate the negative impacts of the previous CASs in relation to traffic and vehicular safety.

In recent years, the country has gradually paid attention to the intelligent and connected automobile industry, from emergency driving to the current auxiliary driving, the blind spot detection system is also gradually upgraded. For the early warning BSD system, how to have a better alarm signal definition and a higher accuracy is the basis of upgrade direction. In the actual driving process, the change in road conditions and the driver’s negligence make the BSD system has the upgrade space. First of all, according to the BSD test of GB / T 39265-2020, evaluates its rationality and propose an improvement plan. The evaluation scheme of key functions that can be applied to blind spot monitoring in L2 driving assistance is designed to effectively guarantee the further standardization and upgrading of the blind spot detection system.