Driver Safety Research Articles

Plasmochemical air cleaning system in vehicle cabin

Problem. Currently, the problem of energy independence is one of the most urgent. Consequently, there is a significant need for alternative sources of power. Along with this, throughout the history of human development, the issues of roads and their quality have been very topical and relevant. It is especially true for Ukraine. With the development of science and technology, these issues are expanded by functionality of roads, their safety both for drivers and for other road users and the ability to perform several functions simultaneously, for example, to provide transport traffic and generate electricity. Goal. The goal is development of the method of road marking and automatic control of the road traffic using multifunctional road panels. Methodology. The analytical methods of research on the development and application of methods and devices for transforming the energy of the sun into electricity were used. Methods of mathematical modulation were used when considering the principle of multi-functional road panels, combined in one system. Results. The functional features of multi-functional road panels of pavement are revealed and their scheme on the roadway is presented. The principle of operation of the road as a unified system of multifunctional road surface panels has been researched. The scheme of implementation of road marking and automatic control of road traffic is presented. Originality. It is suggested to use special plates – multifunctional road panels – instead of the top layer of the road cover. Such an upper layer of road clothing will allow the road, as an integral complex system, to perform several functions simultaneously: to be a part of the road for motor transport, to generate electricity and to draw a road marking independently, and if necessary – to change it. Practical value. The use of the proposed method of road markings and automatic control of road traffic will make possible quality performance and rapid change of road markings at any time of the year and day, depending on the current natural and traffic conditions. Also, the proposed road pavement will generate electricity as an alternative renewable source of energy.

Investigation of Unexpected Crossing-Lane Activity on Curving Road Using Digital Human Modelling Analysis

This study investigates the truck driving posture, vision safety, and perception time safety for truck drivers adjusting their velocity at the curving road due to crossing lane activity. The investigation method starts defining the road contour of the target using Geospatial Information System software (QBase). After the road elevation projection was made, the line was drawn to the surface in CAD software. After that, Digital Human Modelling (DHM) was created on the Japanese population with Indonesian anthropometry properties of 50%ile. The environment scenario was assembled based on the 3D model road surface and 3D vehicle models, and DHM in series. The objective scenario used vision analysis and driver perception response analysis. The result shows that even if the truck and SUV blockage has not appeared, the truck driver will not see the motorcycle on the corner. After that, pillar-A also contributes to blocking the truck driver's vision. Since it contributes to construction safety, the trade-off cannot be negotiated. A speed trap shall be applied before the curving zone to increase safety. The speed trap could instantly reduce the truck and other vehicles' speed in both lanes.

Cyber Security Issues and Solution in Vehicular Networks

Vehicle Ad-hoc Networks (VANETs), which share traffic-related information between cars and infrastructure and aid to enhance driver safety and road safety, are capable of and viable for implementing Intelligent Transportation Systems (ITS). efficient traffic flow. Nonetheless, the confidentiality and privacy of such information is crucial for VANET due to the open wireless access channel. Attackers can compromise VANET security by capturing, intercepting, altering, replaying, or deleting traffic-related information. Thus, the hottest study field right now is assuring the security and privacy of traffic-related information in VANETs. Vehicle communication has been the subject of extensive effort in this situation. Yet, in terms of security needs, security assaults, and performance effectiveness, these duties fall short of adequately addressing security challenges.This white paper organises various authentication and privacy systems into categories and analyses their workings, advantages and disadvantages, security needs, threats, and performance metrics. Lastly, we chose the research inquiries that would be posted in the VANET security area.

Autonomous Braking System Using Linear Actuator

The most frequent cause of vehicle accidents (car, bike, truck, etc.) is the unexpected existence of barriers while driving. An automated braking system will assist and minimize such collisions and save the driver and other people’s lives and have a substantial influence on driver safety and comfort. An autonomous braking system is a complicated mechatronic system that incorporates a front-mounted ultrasonic wave emitter capable of creating and transmitting ultrasonic waves. In addition, a front-mounted ultrasonic receiver is attached to gather ultrasonic wave signals that are reflected. The distance between the impediment and the vehicle is determined by the reflected wave. Then, a microprocessor is utilized to control the vehicle’s speed depending on the detected pulse information, which pushes the brake pedal and applies the vehicle’s brakes extremely hard for safety. For work-energy at surprise condition for velocity 20 km/hr, the braking distance is 17.69 m, and for velocity 50 km/hr, the braking distance is 73.14.

Sensing and Detection of Traffic Signs Using CNNs: An Assessment on Their Performance.

Traffic sign detection systems constitute a key component in trending real-world applications such as autonomous driving and driver safety and assistance. In recent years, many learning systems have been used to help detect traffic signs more accurately, such as ResNet, Vgg, Squeeznet, and DenseNet, but which of these systems can perform better than the others is debatable. They must be examined carefully and under the same conditions. To check the system under the same conditions, you must first have the same database structure. Moreover, the practice of training under the same number of epochs should be the same. Other points to consider are the language in which the coding operation was performed as well as the method of calling the training system, which should be the same. As a result, under these conditions, it can be said that the comparison between different education systems has been done under equal conditions, and the result of this analogy will be valid. In this article, traffic sign detection was done using AlexNet and XresNet 50 training methods, which had not been used until now. Then, with the implementation of ResNet 18, 34, and 50, DenseNet 121, 169, and 201, Vgg 16_bn and Vgg19_bn, AlexNet, SqueezeNet1_0, and SqueezeNet1_1 training methods under completely the same conditions. The results are compared with each other, and finally, the best ones for use in detecting traffic signs are introduced. The experimental results showed that, considering parameters train loss, valid loss, accuracy, error rate and Time, three types of CNN learning models Vgg 16_bn, Vgg19_bn and, AlexNet performed better for the intended purpose. As a result, these three types of learning models can be considered for further studies.

Control Interface for Next Generation Vehicles: What Is the Best Way to Drive Four-Wheel Independent Steering Vehicles?

Four-wheel independent steering (4WIS) vehicles refer to vehicles designed to be able to move in multi-orientation by eliminating the mechanical linkage among the wheels. This study aims to evaluate and compare the usability of previously proposed 4WIS vehicle controllers. A usability test with 33 drivers was conducted to compare the five control interfaces (steering wheel buttons, touchscreen buttons, center console buttons, joystick, and paddle shifters). In order to identify what factors contributes to the usability of 4WIS control interfaces, we utilized a mixed-method approach that collects and analyzes both quantitative and qualitative data within the study. The quantitative results indicate that button-type interfaces shorten task completion time and reduce workload than joystick or paddle shifters. Qualitative analysis implies that touchscreen buttons reduce the perceived safety of drivers due to the lack of tactile feedback. Design recommendations for 4WIS control interfaces were provided from the usability testing results.

Secured data transmission for VANETS using CNN based trust aware clustering

Intelligent transport system is a greatly emerging technology in recent years. The stability and reliability of these systems is very important. In vehicular ad-hoc networks (VANET), the data transmission process can be improved by employing clustering process. The nodes can be clustered in order to utilize the maximum bandwidth of the network and improving network stability. In VANETs it is to introduce road safety and driver safety. In addition, security is a major concern and the malicious nodes need to be accurately detected. Several kinds of attacks can present in the VANETs. Hence an efficient authentication method and trust aware method is essentially required. In this work, stability assured CNN based trust aware clustering and authenticated transmission is introduced. For data authentication quantum cryptography technique is employed. In clustering process, trust degree of nodes is computed, vehicle speed is observed, direction of vehicle and distance among nodes are taken. In addition, for ensuring more safety, the critical data transmission is given higher priority. Therefore in clustering, data criticality parameter is also considered. For cluster formation, convolution neural network is employed. After the clustering process, the quantum cryptography based authentication is implemented for vehicle units and road side units. Data among these units are transmitted with quantum channel encryption key. Then simulation results are observed for validating the proposed protocol.

Exploring the Knowledge and Attitude of the Taxi Drivers in the Field of Traffic Rules and Regulations.

In all countries, the knowledge of driving guidelines is the most important and critical mechanism to ensure the safety of drivers. Naturally, it is expected that more knowledge and attitude towards driving laws would result in fewer driving violations. The aim of this research is to investigate both the knowledge and attitude of taxi drivers in the field of traffic rules and regulations in Shiraz city, Fars province, in 2019. This research is a cross-sectional study, based on which about 1077 taxi drivers were randomly chosen in Shiraz city, Iran. The data collection tool is a demographic information questionnaire and a questionnaire on driving guidance. After collecting these questionnaires, the obtained data were entered into SPSS version 20 and then analyzed by descriptive analyses (mean, standard deviation, and percentage). Afterward, the independent t-test, one-way ANOVA, and Pearson correlation coefficient are implemented and investigated. The mean and standard deviation of knowledge score in drivers were about 26.8 ± 55.2 while the mean and standard deviation of drivers' attitude were about 98.16 ± 59.3. The results showed that there was a significant relationship between the variables studied (R = -0.07; P=0.02) and drivers' attitudes toward driving (P=0.01). The results of this research show that the mean scores of knowledge and attitude of taxi drivers in Shiraz city are moderate in terms of driving rules and regulations; moreover, among taxi drivers of increasing age, the average score of knowledge was lower.

An Improved Deep Learning-Based Technique for Driver Detection and Driver Assistance in Electric Vehicles with Better Performance

Technology for electric vehicles (EVs) is a developing subject that offers numerous advantages, such as reduced operating costs. Since the goal of EVs has always been to have long-lasting batteries, any new hardware might drastically diminish battery life. Errors are common among human beings. Because of that, accidents and fatalities may occur due to drivers’ different behaviors such as sports style and moderation. To advance driver safety, security, and comfort, Advanced Driver Assistance Systems (ADAS) must be personalized. Modern cars have ADAS that relieves the driver of some of the tasks they perform while driving. As a part of this research, a driver identification system based on a deep driver classification model (deep neural network as DNN) with feature reduction techniques (random forest as RF and principal component analysis as PCA) is implemented to help automate and aid in crucial jobs such as the brake system in an efficient manner. Using task models, we simulate a low-cost driver assisted scheme in real time, where various scenarios are explored and the schedulability of tasks is established before implementing them in EV. The new driver assistance scheme has several advantages over the existing options. It lowers the risk of an accident and ensures driver safety. The proposed model (RF-DNN) achieved 97.05% of accuracy and the PCA-DNN model achieved 95.55% of accuracy, whereas the artificial neural network as ANN with PCA and RF achieved nearly 92% of accuracy.

A secure cross-layer architecture for reactive routing in vehicle to vehicle (V2V) communications

Vehicular communication is one of the essential technologies for increasing road safety, traffic efficiency, and comfort for pedestrians and drivers. In this context, the internet of vehicles is an emerging paradigm. However, with advances in vehicular communication, security threats have also emerged. Several vulnerabilities exist in vehicular communications, including Denial of Service (DoS), black hole attacks, and fabrication attacks. A malicious attack alters the packet information in a fabrication attack, causing congestion and high delays in the vehicular network. We propose two algorithms to protect the routing protocols in a vehicle-to-vehicle scenario against several attacks that target confidentiality, authentication, privacy, and integrity. The first algorithm detects the malicious behavior of each vehicle by calculating the percentage of modified destination addresses. If it exceeds a predetermined threshold, this vehicle is classified as malicious. Otherwise, it is a normal vehicle. The second algorithm detects malicious modifications based on the Signal to Interference Ratio (SIR) by monitoring the SIR value, adjusting the distance, altering the power received, and changing the transmitted power value. We performed simulations using the SUMO 0.22 simulator and Network Simulator (NS). The results obtained show an improvement in End-to-End (E2E) delay, Packet Delivery Ratio (PDR), and reduced overhead.

Distributed Motion Planning for Safe Autonomous Vehicle Overtaking via Artificial Potential Field

Autonomous driving of multi-lane vehicle platoons have attracted significant attention in recent years due to their potential to enhance the traffic-carrying capacity of the roads and produce better safety for drivers and passengers. This paper proposes a distributed motion planning algorithm to ensure safe overtaking of autonomous vehicles in a dynamic environment using the Artificial Potential Field method. Unlike the conventional overtaking techniques, autonomous driving strategies can be used to implement safe overtaking via formation control of unmanned vehicles in a complex vehicle platoon in the presence of human-operated vehicles. Firstly, we formulate the overtaking problem of a group of autonomous vehicles into a multi-target tracking problem, where the targets are dynamic. To model a multi-vehicle system consisting of both autonomous and human-operated vehicles, we introduce the notion of velocity difference potential field and acceleration difference potential field. We then analyze the stability of the multi-lane vehicle platoon and propose an optimization-based algorithm for solving the overtaking problem by placing a dynamic target in the traditional artificial potential field. A simulation case study has been performed to verify the feasibility and effectiveness of the proposed distributed motion control strategy for safe overtaking in a multi-lane vehicle platoon.

Minnesota Intelligent Transportation Systems Laboratory [ITS Research Lab

The Minnesota Intelligent Transportation Systems Laboratory (MnITSLab) was established in 1994 as part of the Center for Distributed Robotics (CDR) and the Department of Computer Science and Engineering in the College of Science and Engineering ( <xref ref-type="fig" rid="fig1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Figure 1</xref> ). The MnITSLab research, through collaboration with local state agencies, outside companies, and engineering faculty, focuses on applying sensing, computing, and automation that will improve driver, pedestrian, and worker safety as well as human well-being. The Center and its members conduct a broad array of research activities, spanning intelligent transportation systems, distributed robotics, machine learning, and computer vision. Currently, the CDR supports three full-time staff engineers, one research faculty member, two staff support personnel, eight graduate students, and five undergraduates, and it has more than US$2 million in annual funding.

Building a Campaign to Increase Older Driver Safety

With aging, physical and cognitive changes can affect driving safety. Emergency nurses can provide education for seniors that can create awareness of these changes and ways to mitigate the changes, allowing the older driver to remain independent and a safer driver.

Privacy-Preserving Deep Reinforcement Learning in Vehicle Ad Hoc Networks

The increasing number of road vehicles results in more fatalities and accidents. Thus, the manufacturing industry is working on driver safety to secure and safe transportation in Vehicle Ad hoc networks. In addition, the mobile vehicles run in the geographical zone and communicate roadside units over the wireless medium with a certain radius. The Internet of Vehicles has become a new network type where vehicles communicate with the application over public networks. This results in an increase in data exploration and threats related to network security. We propose the deep reinforcement learning method to sensitize the private information for a given vehicle connect over Vehicle Ad hoc networks, maintaining a balance between security and privacy through any sanitization process. Furthermore, we provide a set of recommendations and potential applications for the Vehicle Ad hoc networks as use cases.

Safety for Drivers using OpenCV in Python

Abstract: Majority of accidents happening in the country is mainly due to the usage of cell-phones or drowsiness. Travelling long distance or driving trucks and taxis for a long time every day and night can cause the driver to be sleep deprived and drowsy which can lead to accidents. With this project, we will be building a system using python and OpenCV that will detect the age and gender of the person and detect if there is any mobile phone in the vicinity. It also detects if the person is sleeping or not and will alarm the driver accordingly. With this project, we would like to reduce the number of accidents happening around us.

Evaluasi jenis dan penataan tanaman median jalan Sunset Road, Kuta untuk keselamatan pengendara

This study aims to evaluate the type and arrangement of plants on the median of Sunset Road with a length of 1,561 m which is divided into 7 segments based on road characteristics and driver safety criteria. The survey method with a qualitative descriptive analysis approach and data collection techniques in the form of observation, interviews and literature study were used in this study. The results of the evaluation showed that 12.5% of plant species were categorized as unsuitable and potentially dangerous to motorists, 50% were suitable and 37.5% were very suitable to be planted on the Sunset Road median. Plants in the median of the road should have more than 1 function, based on each function, 93.8% of plants on Sunset Road have a directing function, 62.5% have a function as a vehicle light blocking, 56.5% have a separator function and 18.8% of plants have a safety function. Completeness of plant composition in terms of leaf texture, height and color in each segment ranged from 56-89%. In addition, improper arrangement was also found in several segments, especially segments 2-5. It can be concluded that there is a mismatch of species and plant arrangement in the median of Sunset Road in terms of the driver safety criteria used in this study.

Condition Monitoring of an All-Terrain Vehicle Gear Train Assembly Using Deep Learning Algorithms with Vibration Signals

Condition monitoring of gear train assembly has been carried out with vibration signals acquired from an all-terrain vehicle (ATV) gearbox. The location of the defect in the gear was identified based on finite element analysis results. The vibration signals were acquired using an accelerometer under good and simulated fault conditions of the gear. The raw vibration signatures acquired from all the possible conditions of the gear train assembly were processed using the descriptive statistics tool. A set of descriptive statistical features were extracted from the raw vibrational signals. This study used a deep learning algorithm based on the tree family, which includes the decision tree, random forest, and random tree algorithms, to classify gear train conditions. Among the tree family algorithms, the random forest algorithm produced maximum classification accuracy of 99%. The decision rules were used to design an online monitoring system to display the gear condition. This study will help to implement online gear health monitoring in ATVs, ensuring the safety of drivers.

Severity of Placental Abruption in Restrained Pregnant Vehicle Drivers: Correct Seat Belt Use Confirmed by Finite Element Model Analysis.

Despite wearing a seat belt, pregnant drivers often suffer from negative fetal outcomes in the event of motor accidents. In order to maintain the safety of pregnant drivers and their fetuses, we assessed the severity of placental abruption caused by motor vehicle collisions using computer simulations. We employed a validated pregnant finite element model to determine the area of placental abruption. We investigated frontal vehicle collisions with a speed of 40 km/h or less involving restrained pregnant drivers with a gestational age of 30 weeks. For a crash speed of 40 km/h, the placental abruption area was 7.0% with a correctly positioned lap belt across the lower abdomen; it was 36.3% with the belt positioned at the umbilicus. The area of placental abruption depended on collision speed, but we found that with a correctly positioned belt it likely would not lead to negative fetal outcomes. We examined the effects on placental abruptions of reconfiguring seat belt width and force limiter setting. A wider lap belt and lower force limiter setting reduced the area of placental abruption to 3.5% and 1.1%, respectively; however, they allowed more forward movement upon collision. A 2.5 kN force limiter setting may be appropriate with respect to both forward movement and reduced placental abruption area. This study confirmed the importance of correctly using seat belts for pregnant drivers. It provides valuable evidence about improving safety equipment settings.

Evaluation of a coach door under a frontal impact

PurposeThe vehicle´s body front pillar should absorb most of the striker kinetic energy, while only a fraction of that is absorbed by the door structure. This study aims to discuss the aforementioned issue. In this test the striker is a virtual entity. Six uniaxial strain gauges are installed throughout the door. Additionally, contactless 3D digital image correlation (DIC) allows to assess the major door panel’s continuous deformation and strain fields.Design/methodology/approachA coach is a large and heavy long-distance passenger transport vehicle. Their structural certification, classifies coaches as M3 Class III vehicles. New coach structures’ designs need analyses of each sub-system for critical pre-validation of the entire structure, aiming driver and passenger carrier safety. Also, a thorough examination due to increased travel speed is needed.FindingsExperimental pseudo-dynamic (PSD) results were compared and validated using finite element method (FEM) with two pieces of distinct FEM software (Abaqus® and PamCrash®). The time dependent solution was carried out by explicit techniques. Results by FEM and PSD test showed good agreement, evidencing the reliability of the tools selected. Results by PamCrash® were closer to the experimental data.Practical implicationsR-29 is truck-only regulation, however can be adapted to coaches in case of a frontal collision. The present work focuses on the impact behavior of the passenger front door subsystem.Originality/valueAs a first validation the entire structure, the behavior of a vehicle door, under in-plane impacts was studied. The corresponding deformation energy absorbed by the frontal passenger coach door under virtual impacts of a swinging striker was assessed using a PSD approach.

ANALYSIS OF THE APPLICATION OF DIGITAL SOLUTIONS IN LOGISTICS AND THEIR IMPACT ON THE BUSINESS OF LOGISTICS OPERATORS IN SOUTHEAST EUROPE

In the last few years, new digital solutions have appeared that expand the horizons of logistics operators. These solutions improved the existing business, but also enabled them to deploy new services. While technology has made cargo delivery more efficient in the eyes of customers, it has also improved driver and cargo safety. These benefits create value for the supply chain, but can be difficult to achieve. The analysis of the impact and application of digital solutions in logistics on the operations of logistics operators in Southeast Europe gives the value of research into supply chains of both complete and partial segments in the domain of storage, transportation, distribution and production itself. They can accelerate the digitization of transport and logistics and the potential for growth in a multi-sector environment. Assumptions for the elimination of costs, as a result of the inadequate response of the administration (customs, border police, barriers, etc.) in Southeast Europe can be mitigated through e-CMR - a key investment in the new digital world. The world of transport and logistics is advancing rapidly. Digital development in particular is a game changer with new opportunities and challenges in the logistics sector, especially in terms of data security. Digitization is redefining the way logistics participants collaborate. It can be expected to increase efficiency and reduce costs within the supply chain, but significant performance requires much more than just networking between participants in the trade of goods and services in Southeast Europe. The aim of this work is to conduct research on the impact of digital business in logistics on the business and efficiency of logistics operators in Southeastern Europe. The data that was used in the data analysis is primary, which was researched by the author, through a questionnaire sent to the employees of logistics operators in the area of Southeastern Europe. Accordingly, a hypothesis was set up in the paper, which was examined, and reads: “There is a positive impact of improving business processes by applying digital solutions on the operations of logistics operators in Southeast Europe”. According to the results of the analysis and the coefficients presented in the previous table, it can be clearly seen that the hypothesis has been proven. Namely, according to the results, it is clearly seen that there is a positive and significant impact of the digital solution in logistics on the operations of logistics operators, and that the same is positive and statistically significant in terms of significance.