Development of Stationary and Mobile Tailgating Detection Solutions for Ground Vehicles

Improving the safety of North American roadways is a top priority for government agencies and transportation organizations alike. Regulations on appropriate driving behavior have been developed to minimize the likelihood of crashes occurring, but law enforcement tools remain to be fully developed and applied in the field. One prominent example of this is tailgating – the dangerous act of one ground vehicle following another too closely. This activity is responsible for thousands of crashes every year, but police officers currently have few tools to accurately detect and document tailgating events. Though tailgating often occurs in a wide variety of vehicle scenarios, the most hazardous class of tailgating is that which occurs when a semitrailer, more commonly called an 18-wheeler, follows a passenger vehicle too closely. The difference in mass between a semitrailer and a passenger vehicle results in a stopping distance nearly twice as long for the former. In addition, truck drivers may be fatigued and unable to react as quickly to emergency situations, further increasing the risk of a deadly crash. Therefore, a tool is necessary that enables officers to determine when tailgating occurs, and allows them to document the event for use by prosecutors in a court of law

Design and implement a smart traffic light controlled by internet of things

The rise of the population produces an increase in the number of vehicles on the road, which creates heavy traffic in the roads and that causes many issues for the citizens and traffic cops an extra two emergency instances so it’s necessary with developing technology to solve this problem. in this research, we used the Arduino UNO microcontroller board to build a new smart traffic light controller (STLC). Signal lights produce traffic congestion, and the system makes every attempt to alleviate it. In this paper, we designed a smart traffic control system by using Arduino to solve the problem of congestion at the intersection of the Dor al Moalemen in Wasit city , working to prevent traffic jam and reduce time, Using Arduino mega, ultrasonic sensor, and a camera esp32, the suggested technique analyses and manages everyday traffic at a three-line intersection. Furthermore, the suggested system achieves three-line intersection sync and implements a balance between the number of vehicles on each side and the green light. when Traffic violation the camera will capture the car number and send it to the database by using telegram

Intelligent traffic monitoring and control system

Thesis (M.S.) University of Alaska Fairbanks, 2019This thesis presents an intelligent system for monitoring and controlling traffic by sensing vehicles' attributes and using communication between vehicles and roadside infrastructures. The goal of this system is to improve the safety of the commuters and help the drivers in making better decisions by providing them with additional information about the traffic conditions. A prototype system consisting of a roadside unit (RSU) and an on-board unit (OBU) was developed to test the functionalities of the proposed system. The RSU consists of sensors for detecting vehicles and estimating their attributes and a radio for communicating with the OBU. The OBU also has a radio for communication purpose. Afterward, a vehicle was used to test the functionalities of the system and the communication between OBU and RSU was evaluated by emulating the presence of a vehicle. A protocol for exchanging messages between the RSU and the OBU was developed to support effective communication. The efficiency of the communication process was further improved by varying the transmission range of different messages. A format for the message was proposed to convey all the necessary information efficiently. The process of collecting vehicle data, processing them and extracting useful information from the data was discussed here along with some limitations of the proposed system

A Survey and Comparison of Low-Cost Sensing Technologies for Road Traffic Monitoring

Abstract This paper reviews low-cost vehicle and pedestrian detection methods and compares their accuracy. The main goal of this survey is to summarize the progress achieved to date and to help identify the sensing technologies that provide high detection accuracy and meet requirements related to cost and ease of installation. Special attention is paid to wireless battery-powered detectors of small dimensions that can be quickly and effortlessly installed alongside traffic lanes (on the side of a road or on a curb) without any additional supporting structures. The comparison of detection methods presented in this paper is based on results of experiments that were conducted with a variety of sensors in a wide range of configurations. During experiments various sensor sets were analyzed. It was shown that the detection accuracy can be significantly improved by fusing data from appropriately selected set of sensors. The experimental results reveal that accurate vehicle detection can be achieved by using sets of passive sensors. Application of active sensors was necessary to obtain satisfactory results in case of pedestrian detection

Development and evaluation of low cost 2-d lidar based traffic data collection methods

Traffic data collection is one of the essential components of a transportation planning exercise. Granular traffic data such as volume count, vehicle classification, speed measurement, and occupancy, allows managing transportation systems more effectively. For effective traffic operation and management, authorities require deploying many sensors across the network. Moreover, the ascending efforts to achieve smart transportation aspects put immense pressure on planning authorities to deploy more sensors to cover an extensive network. This research focuses on the development and evaluation of inexpensive data collection methodology by using two-dimensional (2-D) Light Detection and Ranging (LiDAR) technology. LiDAR is adopted since it is economical and easily accessible technology. Moreover, its 360-degree visibility and accurate distance information make it more reliable. To collect traffic count data, the proposed method integrates a Continuous Wavelet Transform (CWT), and Support Vector Machine (SVM) into a single framework. Proof-of-Concept (POC) test is conducted in three different places in Newark, New Jersey to examine the performance of the proposed method. The POC test results demonstrate that the proposed method achieves acceptable performances, resulting in 83% ~ 94% accuracy. It is discovered that the proposed method\u27s accuracy is affected by the color of the exterior surface of a vehicle since some colored surfaces do not produce enough reflective rays. It is noticed that the blue and black colors are less reflective, while white-colored surfaces produce high reflective rays. A methodology is proposed that comprises K-means clustering, inverse sensor model, and Kalman filter to obtain trajectories of the vehicles at the intersections. The primary purpose of vehicle detection and tracking is to obtain the turning movement counts at an intersection. A K-means clustering is an unsupervised machine learning technique that clusters the data into different groups by analyzing the smallest mean of a data point from the centroid. The ultimate objective of applying K-mean clustering is to identify the difference between pedestrians and vehicles. An inverse sensor model is a state model of occupancy grid mapping that localizes the detected vehicles on the grid map. A constant velocity model based Kalman filter is defined to track the trajectory of the vehicles. The data are collected from two intersections located in Newark, New Jersey, to study the accuracy of the proposed method. The results show that the proposed method has an average accuracy of 83.75%. Furthermore, the obtained R-squared value for localization of the vehicles on the grid map is ranging between 0.87 to 0.89. Furthermore, a primary cost comparison is made to study the cost efficiency of the developed methodology. The cost comparison shows that the proposed methodology based on 2-D LiDAR technology can achieve acceptable accuracy at a low price and be considered a smart city concept to conduct extensive scale data collection

Error Prevention in Sensors and Sensor Systems

Achievements in all fields of engineering and fabrication methods have led towards optimization and integration of multiple sensing devices into a concise system. These advances have caused significant innovation in various commercial, industrial, and research efforts. Integrations of subsystems have important applications for sensor systems in particular. The need for reporting and real time awareness of a device’s condition and surroundings have led to sensor systems being implemented in a wide variety of fields. From environmental sensors for agriculture, to object characterization and biomedical sensing, the application for sensor systems has impacted all modern facets of innovation. With these innovations, however, additional sources of errors can occur, that can cause new but exciting challenges for such integrated devices. Such challenges range from error correction and accuracy to power optimization. Researchers have invested significant time and effort to improve the applicability and accuracy of sensors and sensor systems. Efforts to reduce inherent and external noise of sensors can range from hardware to software solutions, focusing on signal processing and exploiting the integration of multiple signals and/or sensor types. My research work throughout my career has been focused on deployable and integrated sensor systems. Their integration not only in hardware and components but also in software, machine learning, pattern recognition, and overall signal processing algorithms to aid in error correction and noise tailoring in all their hardware and software components

Estimation of a risk profile to operatives and the public from motorway hard-shoulder incursions

This project focuses on the risk to the operatives and the public arising from hard-shoulder incursions on motorways, which are defined as the temporary violation of this lane by a vehicle travelling on the nearside lane. Even though interest has been raised around safety when stopping on the hard-shoulder, there is no significant research conducted to investigate and quantify this risk. In this EngD project, motorway hard-shoulder accidents were investigated individually from the main traffic lanes to explore the factors affecting their severity and likelihood and identify potential differences using discrete choice and time-series modelling techniques. Based on the safety triangle theory, it was assumed that eliminating the contributory factors for injury accidents would also minimise the risk of hard-shoulder incursions, which were used as a risk indicator. An observation-based survey was conducted to gain initial knowledge on the frequency of incursions within a motorway stretch and also basic conditions that may affect the severity as well. Further to the survey, in order to collect hard-shoulder incursion data automatically, potential vehicle detection solutions were investigated. A radar sensor-based system was identified as the most suitable for this purpose and was adapted to suit the project s requirements. The sensor was installed on a motorway site, following a series of requirements to ensure safe and effective deployment. The data collected from the radar sensor were processed to minimise the errors and then corresponded to the traffic related and environmental data available for the same period of time. Using the Generalised Linear Autoregressive Moving Average model, the final models developed provided the factors that mostly affect the occurrence of hard-shoulder incursions. The main factors are temperature, humidity, traffic composition and average speed on the main carriageway. Using these models it is possible to quantify the risk and forecast when this will be minimised at a particular motorway section at any time. The risk is estimated according to the explanatory variables proposed, by inputting the predictions of these conditions in the model. This model is a tool that may then allow the operatives to be deployed on the network in the safest manner, according to the levels of tolerable risk

A smart traffic light using a microcontroller based on the fuzzy logic

Traffic jam that is resulted from the buildup of vehicles on the road has become an important problem, which leads to an interference with drivers. The impacts it has on cost and time effectiveness may take the form of increased fuel consumption, traffic emissions, and noise. This paper offers a solution by creating a smart traffic light using a fuzzy-logic-based microcontroller for a greater adaptability of the traffic light to the dynamics of the vehicles that are to cross the intersection. The ATMega2560 microcontroller-based smart traffic light is designed to create a breakthrough in the breakdown of congestions at road junctions, thereby optimizing the real-time happenings in the road. Ultrasonic, infrared, and light sensors are used in this smart traffic light, resulting in the smart traffic light’s effectiveness in parsing jams. The four sets of sensors that are placed in four sections determine the traffic light timing process. When the length of vehicle queue reaches the sensor, a signal is sent as the microcontroller’s digital input. Ultrasonic and infrared sensors can reduce congestions at traffic lights by giving a green light time when one or all of the sensors are active so that the vehicle congestions can be relieved