Detecting Vehicles' Relative Position on Two-Lane Highways Through a Smartphone-Based Video Overtaking Aid Application

[EN] In this paper we present a smartphone-based real-time video overtaking architecture for vehicular networks. The developed application aims to prevent head-on collisions that might occur due to attempts to overtake when the view of the driver is obstructed by the presence of a larger vehicle ahead. Under such conditions, the driver does not have a clear view of the road ahead and of any vehicles that might be approaching from the opposite direction, resulting in a high probability of accident occurrence. Our application relies on the use of a dashboard-mounted smartphone with the back camera facing the windshield, and having the screen towards the driver. A video is streamed from the vehicle ahead to the vehicle behind automatically, where it is displayed so that the driver can decide if it is safe to overtake. One of the major challenges is the way to pick the right video source and destination among vehicles in close proximity, depending on their relative position on the road. For this purpose, we have focused on two different methods: one relying solely on GPS data, and the other involving the use of the camera and vehicle heading information. Our experiments show that the faster method, using just the location information, is prone to errors due to GPS inaccuracies. A second method that depends on data fusion from the optical sensor and GPS, although accurate over short distances, becomes more computationally intensive, and its performance significantly depends on the quality of the camera.This work was partially funding by the "Ministerio de Ciencia, Innovacion y Universidades, Programa Estatal de Investigacion, Desarrollo e Innovacion Orientada a los Retos de la Sociedad, Proyectos I+D+I 2018", Spain, under Grant RTI2018-096384-B-I00.Patra, S.; Van Hamme, D.; Veelaert, P.; Tavares De Araujo Cesariny Calafate, CM.; Cano, J.; Manzoni, P.; Zamora, W. (2020). 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Lane detection in autonomous vehicles : A systematic review

One of the essential systems in autonomous vehicles for ensuring a secure circumstance for drivers and passengers is the Advanced Driver Assistance System (ADAS). Adaptive Cruise Control, Automatic Braking/Steer Away, Lane-Keeping System, Blind Spot Assist, Lane Departure Warning System, and Lane Detection are examples of ADAS. Lane detection displays information specific to the geometrical features of lane line structures to the vehicle's intelligent system to show the position of lane markings. This article reviews the methods employed for lane detection in an autonomous vehicle. A systematic literature review (SLR) has been carried out to analyze the most delicate approach to detecting the road lane for the benefit of the automation industry. One hundred and two publications from well-known databases were chosen for this review. The trend was discovered after thoroughly examining the selected articles on the method implemented for detecting the road lane from 2018 until 2021. The selected literature used various methods, with the input dataset being one of two types: self-collected or acquired from an online public dataset. In the meantime, the methodologies include geometric modeling and traditional methods, while AI includes deep learning and machine learning. The use of deep learning has been increasingly researched throughout the last four years. Some studies used stand-Alone deep learning implementations for lane detection problems. Meanwhile, some research focuses on merging deep learning with other machine learning techniques and classical methodologies. Recent advancements imply that attention mechanism has become a popular combined strategy with deep learning methods. The use of deep algorithms in conjunction with other techniques showed promising outcomes. This research aims to provide a complete overview of the literature on lane detection methods, highlighting which approaches are currently being researched and the performance of existing state-of-The-Art techniques. Also, the paper covered the equipment used to collect the dataset for the training process and the dataset used for network training, validation, and testing. This review yields a valuable foundation on lane detection techniques, challenges, and opportunities and supports new research works in this automation field. For further study, it is suggested to put more effort into accuracy improvement, increased speed performance, and more challenging work on various extreme conditions in detecting the road lane

A systematic literature review on the relationship between autonomous vehicle technology and traffic-related mortality.

학위논문(석사) -- 서울대학교대학원 : 행정대학원 글로벌행정전공, 2023. 2. 최태현.The society is anticipated to gain a lot from Autonomous Vehicles (AV), such as improved traffic flow and a decrease in accidents. They heavily rely on improvements in various Artificial Intelligence (AI) processes and strategies. Though some researchers in this field believe AV is the key to enhancing safety, others believe AV creates new challenges when it comes to ensuring the security of these new technology/systems and applications. The article conducts a systematic literature review on the relationship between autonomous vehicle technology and traffic-related mortality. According to inclusion and exclusion criteria, articles from EBSCO, ProQuest, IEEE Explorer, Web of Science were chosen, and they were then sorted. The findings reveal that the most of these publications have been published in advanced transport-related journals. Future improvements in the automobile industry and the development of intelligent transportation systems could help reduce the number of fatal traffic accidents. Technologies for autonomous cars provide effective ways to enhance the driving experience and reduce the number of traffic accidents. A multitude of driving-related problems, such as crashes, traffic, energy usage, and environmental pollution, will be helped by autonomous driving technology. More research is needed for the significant majority of the studies that were assessed. They need to be expanded so that they can be tested in real-world or computer-simulated scenarios, in better and more realistic scenarios, with better and more data, and in experimental designs where the results of the proposed strategy are compared to those of industry standards and competing strategies. Therefore, additional study with improved methods is needed. Another major area that requires additional research is the moral and ethical choices made by AVs. Government, policy makers, manufacturers, and designers all need to do many actions in order to deploy autonomous vehicles on the road effectively. The government should develop laws, rules, and an action plan in particular. It is important to create more effective programs that might encourage the adoption of emerging technology in transportation systems, such as driverless vehicles. In this regard, user perception becomes essential since it may inform designers about current issues and observations made by people. The perceptions of autonomous car users in developing countries like Azerbaijan haven't been thoroughly studied up to this point. The manufacturer has to fix the system flaw and needs a good data set for efficient operation. In the not-too-distant future, the widespread use of highly automated vehicles (AVs) may open up intriguing new possibilities for resolving persistent issues in current safety-related research. Further research is required to better understand and quantify the significant policy implications of Avs, taking into consideration factors like penetration rate, public adoption, technological advancements, traffic patterns, and business models. It only needs to take into account peer-reviewed, full-text journal papers for the investigation, but it's clear that a larger database and more documents would provide more results and a more thorough analysis.자율주행차(AV)를 통해 교통 흐름이 개선되고 사고가 줄어드는 등 사회가 얻는 것이 많을 것으로 예상된다. 그들은 다양한 인공지능(AI) 프로세스와 전략의 개선에 크게 의존한다. 이 분야의 일부 연구자들은 AV가 안전성을 향상시키는 열쇠라고 믿지만, 다른 연구자들은 AV가 이러한 새로운 기술/시스템 및 애플리케이션의 보안을 보장하는 것과 관련하여 새로운 문제를 야기한다고 믿는다. 이 논문은 자율주행차 기술과 교통 관련 사망률 사이의 관계에 대한 체계적인 문헌 검토를 수행한다. 포함 및 제외 기준에 따라 EBSCO, ProQuest, IEEE Explorer 및 Web of Science의 기사를 선택하고 분류했다.연구 결과는 이러한 출판물의 대부분이 고급 운송 관련 저널에 게재되었음을 보여준다. 미래의 자동차 산업의 개선과 지능형 교통 시스템의 개발은 치명적인 교통 사고의 수를 줄이는 데 도움이 될 수 있다. 자율주행 자동차 기술은 운전 경험을 향상시키고 교통 사고의 수를 줄일 수 있는 효과적인 방법을 제공한다. 충돌, 교통, 에너지 사용, 환경 오염과 같은 수많은 운전 관련 문제들은 자율 주행 기술에 의해 도움을 받을 것이다. 평가된 대부분의 연구에 대해 더 많은 연구가 필요하다. 실제 또는 컴퓨터 시뮬레이션 시나리오, 더 좋고 현실적인 시나리오, 더 좋고 더 많은 데이터, 그리고 제안된 전략 결과가 산업 표준 및 경쟁 전략의 결과와 비교되는 실험 설계에서 테스트될 수 있도록 확장되어야 한다. 따라서 개선된 방법에 대한 추가 연구가 필요하다. 추가 연구가 필요한 또 다른 주요 분야는 AV의 도덕적, 윤리적 선택이다. 정부, 정책 입안자, 제조업체 및 설계자는 모두 자율 주행 차량을 효과적으로 도로에 배치하기 위해 많은 조치를 취해야 한다. 정부는 특히 법, 규칙, 실행 계획을 개발해야 한다. 운전자 없는 차량과 같은 운송 시스템에서 새로운 기술의 채택을 장려할 수 있는 보다 효과적인 프로그램을 만드는 것이 중요하다. 이와 관련하여, 설계자에게 현재 이슈와 사람에 의한 관찰을 알려줄 수 있기 때문에 사용자 인식이 필수적이 된다.제조업체는 시스템 결함을 수정해야 하며 효율적인 작동을 위해 좋은 데이터 세트가 필요하다. 멀지 않은 미래에, 고도로 자동화된 차량(AV)의 광범위한 사용은 현재의 안전 관련 연구에서 지속적인 문제를 해결하기 위한 흥미로운 새로운 가능성을 열어줄 수 있다. 보급률, 공공 채택, 기술 발전, 교통 패턴 및 비즈니스 모델과 같은 요소를 고려하여 Avs의 중요한 정책 영향을 더 잘 이해하고 정량화하기 위한 추가 연구가 필요하다. 조사를 위해 동료 검토를 거친 전문 저널 논문만 고려하면 되지만, 데이터베이스가 커지고 문서가 많아지면 더 많은 결과와 더 철저한 분석이 제공될 것이 분명하다.Abstract 3 Table of Contents 6 List of Tables 7 List of Figures 7 List of Appendix 7 CHAPTER 1: INTRODUCTION 8 1.1. Background 8 1.2. Purpose of Research 13 CHAPTER 2: AUTONOMOUS VEHICLES 21 2.1. Intelligent Traffic Systems 21 2.2. System Architecture for Autonomous Vehicles 22 2.3. Key components in AV classification 27 CHAPTER 3: METHODOLOGY AND DATA COLLECTION PROCEDURE 35 CHAPTER 4: FINDINGS AND DISCUSSION 39 4.1. RQ1: Do autonomous vehicles reduce traffic-related deaths 40 4.2. RQ2: Are there any challenges to using autonomous vehicles 63 4.3. RQ3: As a developing country, how effective is the use of autonomous vehicles for reducing traffic mortality 72 CHAPTER 5: CONCLUSION 76 5.1. Summary 76 5.2. Implications and Recommendations 80 5.3. Limitation of the study 91 Bibliography 93 List of Tables Table 1: The 6 Levels of Autonomous Vehicles Table 2: Search strings Table 3: Inclusion and exclusion criteria List of Figures Figure 1: Traffic Death Comparison with Europe Figure 2: Research strategy and study selection process List of Appendix Appendix 1: List of selected articles석

Driving Manoeuvre Recognition using Mobile Sensors

Automobiles are integral in today's society as they are used for transportation, commerce, and public services. The ubiquity of automotive transportation creates a demand for active safety technologies for the consumer. Recently, the widespread use and improved sensing and computing capabilities of mobile platforms have enabled the development of systems that can measure, detect, and analyze driver behaviour. Most systems performing driver behaviour analysis depend on recognizing driver manoeuvres. Improved accuracy in manoeuvre detection has the potential to improve driving safety, through applications such as monitoring for insurance, the detection of aggressive, distracted or fatigued driving, and for new driver training. This thesis develops algorithms for estimating vehicle kinematics and recognizing driver manoeuvres using a smartphone device. A kinematic model of the car is first introduced to express the vehicle's position and orientation. An Extended Kalman Filter (EKF) is developed to estimate the vehicle's positions, velocities, and accelerations using mobile measurements from inertial measurement units and the Global Positioning System (GPS). The approach is tested in simulation and validated on trip data using an On-board Diagnostic (OBD) device as the ground truth. The 2D state estimator is demonstrated to be an effective filter for measurement noise. Manoeuvre recognition is then formulated as a time-series classification problem. To account for an arbitrary orientation of the mobile device with respect to the vehicle, a novel method is proposed to estimate the phone's rotation matrix relative to the car using PCA on the gyroscope signal. Experimental results demonstrate that e Principal Component (PC) corresponds to a frame axis in the vehicle reference frame, so that the PCA projection matrix can be used to align the mobile device measurement data to the vehicle frame. A major impediment to classifier-manoeuvre recognition is the need for training data, specifically collecting enough data and generating an accurate ground truth. To address this problem, a novel training process is proposed to train the classifier using only simulation data. Training on simulation data bypasses these two issues as data can be cheaply generated and the ground truth is known. In this thesis, a driving simulator is developed using a Markov Decision Process (MDP) to generate simulated data for classifier training. Following training data generation, feature selection is performed using simple features such as velocity and angular velocity. A manoeuvre segmentation classifier is trained using multi-class SVMs. Validation was performed using data collected from driving sessions. A grid search was employed for parameter tuning. The classifier was found to have a 0.8158 average precision rate and a 0.8279 average recall rate across all manoeuvres resulting in an average F1 score of 0.8194 on the dataset

An Overview on the Current Status and Future Perspectives of Smart Cars

In recent years, the smart car sector has been increasing enormously in the Internet of Things (IoT) market. Furthermore, the number of smart cars seems set to increase over the next few years. This goal will be achieved because the application of recent IoT technologies to the automotive sector opens up innovative opportunities for the mobility of the future, in which connected cars will be more and more prominent in smart cities. This paper aims to provide an overview of the current status and future perspectives of smart cars, taking into account technological, transport, and social features. An analysis concerning the approaches to making smart a generic car, the possible evolutions that could occur in the coming decades, the characteristics of 5G, ADAS (advanced driver assistance systems), and the power sources is carried out in this paper. Document type: Articl

A Context Aware Classification System for Monitoring Driver’s Distraction Levels

Understanding the safety measures regarding developing self-driving futuristic cars is a concern for decision-makers, civil society, consumer groups, and manufacturers. The researchers are trying to thoroughly test and simulate various driving contexts to make these cars fully secure for road users. Including the vehicle’ surroundings offer an ideal way to monitor context-aware situations and incorporate the various hazards. In this regard, different studies have analysed drivers’ behaviour under different case scenarios and scrutinised the external environment to obtain a holistic view of vehicles and the environment. Studies showed that the primary cause of road accidents is driver distraction, and there is a thin line that separates the transition from careless to dangerous. While there has been a significant improvement in advanced driver assistance systems, the current measures neither detect the severity of the distraction levels nor the context-aware, which can aid in preventing accidents. Also, no compact study provides a complete model for transitioning control from the driver to the vehicle when a high degree of distraction is detected. The current study proposes a context-aware severity model to detect safety issues related to driver’s distractions, considering the physiological attributes, the activities, and context-aware situations such as environment and vehicle. Thereby, a novel three-phase Fast Recurrent Convolutional Neural Network (Fast-RCNN) architecture addresses the physiological attributes. Secondly, a novel two-tier FRCNN-LSTM framework is devised to classify the severity of driver distraction. Thirdly, a Dynamic Bayesian Network (DBN) for the prediction of driver distraction. The study further proposes the Multiclass Driver Distraction Risk Assessment (MDDRA) model, which can be adopted in a context-aware driving distraction scenario. Finally, a 3-way hybrid CNN-DBN-LSTM multiclass degree of driver distraction according to severity level is developed. In addition, a Hidden Markov Driver Distraction Severity Model (HMDDSM) for the transitioning of control from the driver to the vehicle when a high degree of distraction is detected. This work tests and evaluates the proposed models using the multi-view TeleFOT naturalistic driving study data and the American University of Cairo dataset (AUCD). The evaluation of the developed models was performed using cross-correlation, hybrid cross-correlations, K-Folds validation. The results show that the technique effectively learns and adopts safety measures related to the severity of driver distraction. In addition, the results also show that while a driver is in a dangerous distraction state, the control can be shifted from driver to vehicle in a systematic manner

Automotive applications of high precision GNSS

This thesis aims to show that Global Navigation Satellite Systems (GNSS) positioning can play a significant role in the positioning systems of future automotive applications. This is through the adoption of state-of-the-art GNSS positioning technology and techniques, and the exploitation of the rapidly developing vehicle-to-vehicle concept. The merging together of these two developments creates greater performance than can be achieved separately. The original contribution of this thesis comes from this combination: Through the introduction of the Pseudo-VRS concept. Pseudo-VRS uses the princples of Network Real Time Kinematic (N-RTK) positioning to share GNSS information between vehicles, which enables absolute vehicle positioning. Pseudo-VRS is shown to improve the performance of high precision GNSS positioning for road vehicles, through the increased availability of GNSS correction messages and the rapid resolution of the N-RTK fixed solution. Positioning systems in the automotive sector are dominated by satellite-based solutions provided by GNSS. This has been the case since May 2001, when the United States Department of Defense switched off Selective Availability, enabling significantly improved positioning performance for civilian users. The average person most frequently encounters GNSS when using electronic personal navigation devices. The Sat Nav or GPS Navigator is ubiquitous in modern societies, where versions can be found on nomadic devices such as smartphones and dedicated personal navigation devices, or built in to the dashboards of vehicles. Such devices have been hugely successful due to their intrinsic ability to provide position information anywhere in the world with an accuracy of approximately 10 metres, which has proved ideal for general navigation applications. There are a few well known limitations of GNSS positioning, including anecdotal evidence of incorrect navigation advice for personal navigation devices, but these are minor compared to the overall positioning performance. Through steady development of GNSS positioning devices, including the integration of other low cost sensors (for instance, wheel speed or odometer sensors in vehicles), and the development of robust map matching algorithms, the performance of these devices for navigation applications is truly incredible. However, when tested for advanced automotive applications, the performance of GNSS positioning devices is found to be inadequate. In particular, in the most advanced fields of research such as autonomous vehicle technology, GNSS positioning devices are relegated to a secondary role, or often not used at all. They are replaced by terrestrial sensors that provide greater situational awareness, such as radar and lidar. This is due to the high performance demand of such applications, including high positioning accuracy (sub-decimetre), high availability and continuity of solutions (100%), and high integrity of the position information. Low-cost GNSS receivers generally do not meet such requirements. This could be considered an enormous oversight, as modern GNSS positioning technology and techniques have significantly improved satellite-based positioning performance. Other non-GNSS techniques also have their limitations that GNSS devices can minimise or eliminate. For instance, systems that rely on situational awareness require accurate digital maps of their surroundings as a reference. GNSS positioning can help to gather this data, provide an input, and act as a fail-safe in the event of digital map errors. It is apparent that in order to deliver advanced automotive applications - such as semi- or fully-autonomous vehicles - there must be an element of absolute positioning capability. Positioning systems will work alongside situational awareness systems to enable the autonomous vehicles to navigate through the real world. A strong candidate for the positioning system is GNSS positioning. This thesis builds on work already started by researchers at the University of Nottingham, to show that N-RTK positioning is one such technique. N-RTK can provide sub-decimetre accuracy absolute positioning solutions, with high availability, continuity, and integrity. A key component of N-RTK is the availability of real-time GNSS correction data. This is typically delivered to the GNSS receiver via mobile internet (for a roving receiver). This can be a significant limitation, as it relies on the performance of the mobile communications network, which can suffer from performance degradation during dynamic operation. Mobile communications systems are expected to improve significantly over the next few years, as consumers demand faster download speeds and wider availability. Mobile communications coverage already covers a high percentage of the population, but this does not translate into a high percentage of a country's geography. Pockets of poor coverage, often referred to as notspots, are widespread. Many of these notspots include the transportation infrastructure. The vehicle-to-vehicle concept has made significant forward steps in the last few years. Traditionally promoted as a key component of future automotive safety applications, it is now driven primarily by increased demand for in-vehicle infotainment. The concept, which shares similarities with the Internet of Things and Mobile Ad-hoc Networks, relies on communication between road vehicles and other road agents (such as pedestrians and road infrastructure). N-RTK positioning can take advantage of this communication link to minimise its own communications-related limitations. Sharing GNSS information between local GNSS receivers enables better performance of GNSS positioning, based on the principles of differential GNSS and N-RTK positioning techniques. This advanced concept is introduced and tested in this thesis. The Pseudo VRS concept follows the protocols and format of sharing GNSS data used in N-RTK positioning. The technique utilises the latest GNSS receiver design, including multiple frequency measurements and high quality antennas