Vulnerable road users and connected autonomous vehicles interaction: a survey

There is a group of users within the vehicular traffic ecosystem known as Vulnerable Road Users (VRUs). VRUs include pedestrians, cyclists, motorcyclists, among others. On the other hand, connected autonomous vehicles (CAVs) are a set of technologies that combines, on the one hand, communication technologies to stay always ubiquitous connected, and on the other hand, automated technologies to assist or replace the human driver during the driving process. Autonomous vehicles are being visualized as a viable alternative to solve road accidents providing a general safe environment for all the users on the road specifically to the most vulnerable. One of the problems facing autonomous vehicles is to generate mechanisms that facilitate their integration not only within the mobility environment, but also into the road society in a safe and efficient way. In this paper, we analyze and discuss how this integration can take place, reviewing the work that has been developed in recent years in each of the stages of the vehicle-human interaction, analyzing the challenges of vulnerable users and proposing solutions that contribute to solving these challenges.This work was partially funded by the Ministry of Economy, Industry, and Competitiveness of Spain under Grant: Supervision of drone fleet and optimization of commercial operations flight plans, PID2020-116377RB-C21.Peer ReviewedPostprint (published version

Fast, Accurate Thin-Structure Obstacle Detection for Autonomous Mobile Robots

Safety is paramount for mobile robotic platforms such as self-driving cars and unmanned aerial vehicles. This work is devoted to a task that is indispensable for safety yet was largely overlooked in the past -- detecting obstacles that are of very thin structures, such as wires, cables and tree branches. This is a challenging problem, as thin objects can be problematic for active sensors such as lidar and sonar and even for stereo cameras. In this work, we propose to use video sequences for thin obstacle detection. We represent obstacles with edges in the video frames, and reconstruct them in 3D using efficient edge-based visual odometry techniques. We provide both a monocular camera solution and a stereo camera solution. The former incorporates Inertial Measurement Unit (IMU) data to solve scale ambiguity, while the latter enjoys a novel, purely vision-based solution. Experiments demonstrated that the proposed methods are fast and able to detect thin obstacles robustly and accurately under various conditions.Comment: Appeared at IEEE CVPR 2017 Workshop on Embedded Visio

Safety Evaluation Using Counterfactual Simulations: The use of computational driver behavior models in crash avoidance systems and virtual simulations with optimal subsampling

Traffic safety is a problem worldwide. In-vehicle conflict and crash avoidance systems have been under development and assessment for some time, as integral parts of Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS). Among the methods used to assess conflict and crash avoidance systems developed by the automotive industry, virtual safety assessment methods have been shown to have great potential and efficiency. In fact, scenario generation-based virtual safety assessments play—and are likely to continue to play—a very important role in the assessments of vehicles of all levels of automation. The ultimate aim of this thesis is to improve the safety performance of conflict and crash avoidance systems. This aim is addressed through the use of computational driver models in two different ways. First, by using comfort-zone boundaries in system design, and second, by using a behavior-based crash-causation model together with a novel optimized scenario generation method for virtual safety assessment.The first objective of this thesis is to investigate how a driver model which includes road users’ comfortable behaviors in crash avoidance algorithms impacts the systems’ safety performance and the residual crash characteristics. Chinese car-to-two-wheeler crashes were targeted; Automated Emergency Braking (AEB) algorithms, which comprised the proposed crash avoidance systems, were compared to a traditional AEB algorithm. The proposed algorithms showed larger safety performance benefits. In addition, the similarities in residual crash characteristics regarding impact speed and location after different AEB implementations can potentially simplify the designs of in-crash protection system in future.The second objective is to develop and apply a method for efficient subsampling in crash-causation-model-based scenario generation for virtual safety assessment. The method, which is machine-learning-assisted, actively and iteratively updates the sampling probability based on new simulation results. The crash-causation model is based on off-road glances and a distribution of driver maximum decelerations in critical situations. A simple time-to-collision-based AEB algorithm was used to demonstrate the assessment process as well as the benefits of combining crash-causation-model-based scenario generation and optimal subsampling. The sampling methods are designed to target specific safety benefit indicators, such as impact speed reduction and crash avoidance rate. The results of the study show that the proposed sampling method requires almost 50% fewer simulations than traditional importance sampling.Future work aims to focus on applying the active sampling method to driver-model-based car-to-vulnerable road user (VRU) scenario generation. In addition to assessing conflict and crash avoidance system performance, a novel stopping criterion based on Bayesian future prediction will be further developed and demonstrated for use in experiments (e.g., as part of developing driver models) and virtual simulations (e.g., using driver-behavior-based crash-causation models). This criterion will be able to indicate when studies are unlikely to yield actionable results within the budget available, facilitating the decision to discontinue them while they are being run

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

A comprehensive survey on cooperative intersection management for heterogeneous connected vehicles

Nowadays, with the advancement of technology, world is trending toward high mobility and dynamics. In this context, intersection management (IM) as one of the most crucial elements of the transportation sector demands high attention. Today, road entities including infrastructures, vulnerable road users (VRUs) such as motorcycles, moped, scooters, pedestrians, bicycles, and other types of vehicles such as trucks, buses, cars, emergency vehicles, and railway vehicles like trains or trams are able to communicate cooperatively using vehicle-to-everything (V2X) communications and provide traffic safety, efficiency, infotainment and ecological improvements. In this paper, we take into account different types of intersections in terms of signalized, semi-autonomous (hybrid) and autonomous intersections and conduct a comprehensive survey on various intersection management methods for heterogeneous connected vehicles (CVs). We consider heterogeneous classes of vehicles such as road and rail vehicles as well as VRUs including bicycles, scooters and motorcycles. All kinds of intersection goals, modeling, coordination architectures, scheduling policies are thoroughly discussed. Signalized and semi-autonomous intersections are assessed with respect to these parameters. We especially focus on autonomous intersection management (AIM) and categorize this section based on four major goals involving safety, efficiency, infotainment and environment. Each intersection goal provides an in-depth investigation on the corresponding literature from the aforementioned perspectives. Moreover, robustness and resiliency of IM are explored from diverse points of view encompassing sensors, information management and sharing, planning universal scheme, heterogeneous collaboration, vehicle classification, quality measurement, external factors, intersection types, localization faults, communication anomalies and channel optimization, synchronization, vehicle dynamics and model mismatch, model uncertainties, recovery, security and privacy