A Review of Traffic Signal Control.

The aim of this paper is to provide a starting point for the future research within the SERC sponsored project "Gating and Traffic Control: The Application of State Space Control Theory". It will provide an introduction to State Space Control Theory, State Space applications in transportation in general, an in-depth review of congestion control (specifically traffic signal control in congested situations), a review of theoretical works, a review of existing systems and will conclude with recommendations for the research to be undertaken within this project

Detecting abundance trends under uncertainty: the influence of budget, observation error and environmental change

ArticleCopyright © 2014 The Authors. Animal Conservation published by John Wiley & Sons Ltd on behalf of The Zoological Society of London.Population monitoring must robustly detect trends over time in a cost-effective manner. However, several underlying ecological changes driving population trends may interact differently with observation uncertainty to produce abundance trends that are more or less detectable for a given budget and over a given time period. Errors in detecting these trends include failing to detect declines when they exist (type II), detecting them when they do not exist (type I), detecting trends in one direction when they are actually in another direction (type III) and incorrectly estimating the shape of the trend. Robust monitoring should be able to avoid each of these error types. Using monitoring of two contrasting ungulate species and multiple scenarios of population change (poaching, climate change and road development) in the Serengeti ecosystem as a case study, we used a ‘virtual ecologist’ approach to investigate monitoring effectiveness under uncertainty. We explored how the prevalence of different types of error varies depending on budgetary, observational and environmental conditions. Higher observation error and conducting surveys less frequently increased the likelihood of not detecting trends and misclassifying the shape of the trend. As monitoring period and frequency increased, observation uncertainty was more important in explaining effectiveness. Types I and III errors had low prevalence for both ungulate species. Greater investment in monitoring considerably decreased the likelihood of failing to detect significant trends (type II errors). Our results suggest that it is important to understand the effects of monitoring conditions on perceived trends before making inferences about underlying processes. The impacts of specific threats on population abundance and structure feed through into monitoring effectiveness; hence, monitoring programmes must be designed with the underlying processes to be detected in mind. Here we provide an integrated modelling framework that can produce advice on robust monitoring strategies under uncertainty.Portuguese Foundation for Science and TechnologyEuropean Commissio

Master's research project : transient migrations and responses to effective change for Mixtec indigenous youth

Master's Project (M.A.) University of Alaska Fairbanks, 2014This qualitative research was developed not knowing the outcome. The primary goal was to find an indigenous group that resided in the Monterey Bay region of the Central Coast of California. Thanks to the Pajaro Valley Unified School District's Migrant Education Office, this goal was accomplished by the introduction of the Mixtec culture of Oaxaca, in Southern Mexico. The research began by 'hanging out' with the Mixtecs at Adult Education English class building a trust and familiarity. Their primary language is Mixteco,. Lessons were in Spanish to learn English. The project all fell into place after being introduced to Lucia, a trilingual 25 yr. old Mixteca. The secondary goal was in understanding their environmental relationship to identity. Since the Mixtecs are immigrants it was necessary to 1) get acquainted with their history, and homeland 2) attempt to understand purpose and risks to 'sneak across the border' 3) adjustment to life in California. Eight interviews were performed. Seven interviews were in their native language, Mixteco. Lucia's interview was in English and Spanish. A hand held device recorded the interviews. All interview questions were formulated in a partnership with Lucia. The translations are composed genuinely from her indigenous perspective. To acquire first hand photos, three cameras were used by the informants as they worked 'on the job' in the agricultural fields. The interviewees from their personal files donated pictures of their Oaxacan villages. Common themes were identified and are summarized in this report. The data was collected and compiled. The research brought forth a narrative nonfiction, young adult book, requested by the Mixtec students, as the rest of this project lays it out

Managing social-ecological systems under uncertainty: Implementation in the real world

Management decisions for natural resources are not made in a vacuum; the environmental and ecological conditions as well as the socioeconomic and political contexts affect goals, the choice of interventions, their feasibility, and which outcomes are obtained. Although uncertainty is recognized as a feature of natural resource management, little attention has been given to the uncertainty generated by institutional settings, historical contingency, and individual people's influence. These implementation uncertainties, related to the translation of policy into practice, make it difficult to predict the outcomes of management interventions within social-ecological systems. Using the conservation of species hunted for bushmeat in the Serengeti as a case study, we investigated the challenges and potential barriers to successful implementation of natural resource management policies. We used a mixed-methods approach, combining semistructured interviews with scenario building, social network, and institutional analysis exercises. Using a management strategy evaluation (MSE) conceptual framework, we obtained insights into the constraints and opportunities for fulfilling stakeholder aspirations for the social-ecological system, analyzed the multiple roles played by different institutions in the system, and described the interactions between different actor types. We found that the respondents had generally similar views about the current and future status of the Serengeti but disagreed about how to address issues of conservation concern and were more uncertain about the actual outcomes of management interventions. Improving conservation implementation (rather than research, monitoring, or status assessment) was perceived as the key priority to be addressed. Institutional barriers were perceived as an important challenge given that the decision-making and implementation processes were broadly distributed across a number of institutions. Conservation social networks were centered on very few individuals, suggesting their importance in bridging across conservation arenas but also potentially affecting the resilience of governance structures. Our study gives an improved understanding of the underlying causes of discrepancies between conservation plans and outcomes for this case study, as well as providing a novel framework for the analysis of implementation uncertainties more broadly. A next step would be to use this framework as a basis for collaboratively developed models that integrate research findings with specific management questions. By bringing tools and findings from social psychology, natural resource management, and bioeconomics together into a unified operational framework, researchers may be better able to understand the barriers to successful resource management and engage with stakeholders to overcome them

The application of systems approach for road safety policy making, Deliverable 8.1 of the H2020 project SafetyCube

The present Deliverable (D8.1) describes the co-ordination of the analysis of risks and measures using a systems framework within the SafetyCube project. It outlines the results of Task 8.1 of Work Package (WP) 8 of SafetyCube. This has involved (i) defining the systems approach to be used within SafetyCube, (ii) developing a taxonomy of risks and measures, (iii) identifying a common set of accident scenarios and (iv) initiating work on the Decision Support System (DSS) development. WP8 of the SafetyCube project has a number of specific aims, including developing the European DSS for supporting evidence-based policy making. It also aims to co-ordinate analysis undertaken in other WPs ensuring integrated research outputs, compilation of the project outputs into a suitable form to be incorporated within the DSS and the European Road Safety Observatory, and finally to develop tools to enable the continued support of evidence based road safety policies beyond SafetyCube. Evidence-based policy making enables policy makers to make justified decisions in the complex reality of road safety interventions. It refers to the use of objective, scientifically-based evidence in all stages of the policy making process. Two important pillars for evidence-based road safety policy making are road safety data and statistics and scientific knowledge (Wegman et al, 2015). This type of policy making can be beneficial (e.g. helps to identify road safety problems and select most appropriate interventions) but also has it’s challenges (e.g. a lot of information at varying levels of detail is required to inform decisions). The DSS that is being developed within SafetyCube aims to support decision makers as well as other stakeholders in their evidence-based policy making. In addition to evidence-based policy making, SafetyCube and in particular the DSS is grounded in the systems approach. The systems approach aims to steer away from the more traditionally ‘human error’ blame focussed approach to road safety, and instead takes into account all ‘components’ in a system (i.e. road users, vehicles, roads) which contribute to a risk of an accident occurring. In SafetyCube, the systems approach is being integrated in the DSS in two main ways. First, the risk factors which relate to the road user, the road or the vehicle will be linked to measures in any or all of these areas if appropriate. Second, to clarify the added value of complementary measures rather than measures in isolation, where appropriate, a description of a measure will pay special attention to and link to supporting measures. The SafetyCube DSS is underpinned by four taxonomies; Road User Behaviour (WP4), Infrastructure (WP5), Vehicles (WP6) and Post Impact Care (WP7). The taxonomy is a main structural part of the DSS system, it can be used as a search option in the DSS, it creates a uniform structure over all work packages and it can be used as a basis for linking risk factors with their corresponding measures. The structure consists of three levels, which are topic, subtopic and specific topic. Thirteen main topics were identified for Road User Behaviour (WP4), 10 main topics for Infrastructure (WP5) and six main topics for Vehicle (WP6). Four topics (based on the DaCoTA webtext on Post Impact Care, 2012), were included in WP7 (Post Impact Care). As expected, there was found to be some overlap between risk factors in one taxonomy and risk factors in another (e.g. is poor vehicle maintenance a Vehicle or Road User-related risk factor?), and some overlaps where a topic could be a risk factor or a countermeasure. Discussions between WPs ensured decisions could be made about how to overcome these ambiguities. Accident scenarios are used within SafetyCube. These are considered to be a classification system for crashes whereby crash types may be grouped according to similar characteristics under a particular scenario heading, creating specific clusters. In total, nine high level accident scenarios will form an entry point to the DSS. Each high level has multiple sub-levels which provide more detailed information about the conflict situation (before the crash). A total of 63 sub-level scenarios are considered. The task of linking risks and measures is currently underway within the SafetyCube project. The accident scenarios will provide a useful and systematic way by which to link risks and measures. They will be used, in order to generate a meaningful set of links, between risks related to specific situations, and measures to address them. The primary objective of the DSS is to provide the European and Global road safety community a user friendly, web-based, interactive Decision Support Tool which will enable policy-makers and stakeholders to select and implement the most appropriate strategies, measures and cost-effective approaches to reduce casualties and crash severity for all road users. It consists of information such as risk factors, road safety measures, cost-benefit, casualty reduction effectiveness estimates. In order to develop the DSS, a review of current existing Decision Support Systems was carried out to provide a first insight into such tools (e.g. Crash Modification Factors Clearinghouse, PRACT Repository, Road Safety Engineering Kit, iRAP). No European DSS were found in the search and of the DSS reviewed, the majority focussed on infrastructure and no risk factors were included. The SafetyCube DSS addresses these gaps. To understand user needs better, three stakeholders workshops were carried out, which allowed participants to comment on the proposed DSS and suggest ‘hot topics’ (i.e. important risk factors) to address in SafetyCube, and the findings of these workshops found that the DSS should be suitable for use by a wide range of users, should be impartial, include robust data and access to all studies used and generated results. A comprehensive common SafetyCube methodology was designed, which included: a complete taxonomy of human behaviour, infrastructure and vehicle; a detailed and recorded literature review and the development of a template for coding research studies and existing results to be stored in a database linked to the DSS. The DSS is being created on the basis of a number of design principles (e.g. modern web-based tool, ergonomic interface, simple, easily updated…). As well as a consistent layout the content itself is also of high importance (e.g. quantitative results over qualitative, methodologically sound, clarity). The DSS itself consists of the backend (relational database), the front end (website) and the way they integrate (queries). The heart of the DSS consists of the searchable/dynamic and static aspects, which consists of five entry points and three levels. The design principles of the DSS ensure a smooth integration of the Work Packages in two ways, firstly that the SafetyCube common methodology is applied and secondly that the fully linked search allows the end user to better perceive the interactions between various components in road safety. There are five entry points into DSS: ‘text search’, ‘risk factors’, ‘road safety measures’, ‘road user groups’ and ‘accident scenarios’. Once a search has been undertaken using one of these five entry points, a results page is shown to the user, which consists of a table listing the available synopses1 (overview of the topic created by synthesising findings from the coding of existing studies), meta-analysis and other studies in the database. From this, the user can then also access the individual study pages for each study listed in the results. Finally, a Tools page allows the user to access other SafetyCube tools (e.g. cost-benefit calculator, methodology information, glossary). 1 More details about the synopses can be found in the Milestone M3.1 (Martensen 2016). So far, more than 500 studies have been analysed in the area of road risks with more than 3,500 risk estimates, summarised in more than 60 synopses (including approximately 10 meta-analyses), and the related measures analyses are in progress. This wealth of information will all be incorporated into the DSS and become its core outputs. The overall design of the DSS is finalised and is currently available, with the next stage being the DSS development, including all risk factors and measures. The DSS Pilot Operation will occur later in the project, followed by the final opening of the DSS, with continual updates from the end of the project onwards. The SafetyCube DSS is intended to have a life well beyond the end of the SafetyCube research project

Reconsidering accident causation analysis and evaluating the safety benefits of technologies: final results of the TRACE project

The objectives of the EU-funded project TRACE (TRaffic Accident Causation in Europe, 2006-2008) are the up-dating of the etiology of road accidents and the assessment of the safety benefits of promising technology-based solutions. The analyses are based on available, reliable and accessible existing databases (access to which has been greatly facilitated by a number of partners highly experienced in safety analysis, coming from 8 different countries and having access to different kinds of databases, in-depth or regional or national statistics in their own country). Apart from considerable improvements in the methodologies applicable to accident research in the field of human factors, statistics and epidemiology, allowing a better understanding of the crash generating issues, the TRACE project quantified the expected safety benefits for existing and future safety applications. As for existing safety functions or safety packages, the main striking results show that any increment of a passive or active safety function selected in this project produces additional safety benefits. In general, the safety gains are even higher for higher injury severity levels. For example, if all cars were Euro NCAP five stars and fitted with EBA and ESC, compared to four stars without ESC and EBA, injury accidents would be reduced by 47%, all injuries would be mitigated by 68% and severe + fatal injuries by 70%. As for future advanced safety functions, TRACE investigated 19 safety systems. The results show that the greatest additional safety gains potential are expected from intelligent speed adaptation systems, automatic crash notification systems, and collision warning and collision avoidance systems. Their expected benefits (expected reduction in the total number of injured persons if the fleet is 100% equipped) are between 6% and 11%. Safety benefits of other systems are more often below 5%. Some systems have a very low expected safety benefit (around or less than 1%)

도심 교차로에서의 자율주행을 위한 주변 차량 경로 예측 및 거동 계획 알고리즘

학위논문(박사)--서울대학교 대학원 :공과대학 기계항공공학부,2020. 2. 이경수.차랑용 센싱 및 처리기술이 발달함에 따라 자동차 기술 연구가 수동 안전 기술에서 능동 안전 기술로 초점이 확장되고 있다. 최근, 주요 자동차 제작사들은 능동형 차간거리 제어, 차선 유지 보조, 그리고 긴급 자동 제동과 같은 능동 안전 기술이 이미 상업화하고 있다. 이러한 기술적 진보는 사상률 제로를 달성하기 위하여 기술 연구 분야를 능동 안전 기술을 넘어서 자율주행 시스템으로 확장시키고 있다. 특히, 도심 도로는 인도, 사각지대, 주차차량, 이륜차, 보행자 등과 같은 교통 위험 요소를 많이 갖고 있기 때문에 고속도로보다 사고 발생률과 사상률이 높으며, 이는 도심 도로에서의 자율주행은 핵심 이슈가 되고 있다. 많은 프로젝트들이 자율주행의 환경적, 인구학적, 사회적, 그리고 경제적 측면에서의 자율주행의 효과를 평가하기 위해 수행되었거나 수행 중에 있다. 예를 들어, 유럽의 AdaptIVE는 다양한 자율주행 기능을 개발하였으며, 구체적인 평가 방법론을 개발하였다. 또한, CityMobil2는 유럽 전역의 9개의 다른 환경에서 무인 지능형 차량을 성공적으로 통합하였다. 일본에서는 2014년 5월에 시작된 Automated Driving System Research Project는 자율주행 시스템과 차세대 도심 교통 수단의 개발 및 검증에 초점을 맞추었다. 기존 연구들에 대한 조사를 통해 자율주행 시스템은 교통 참여자들의 안전도를 향상시키고, 교통 혼잡을 감소시키며, 운전자 편의성을 증진시키는 것이 증명되었다. 다양한 방법론들이 인지, 거동 계획, 그리고 제어와 같은 도심 도로 자율주행차의 핵심 기술들을 개발하기 위하여 사용되었다. 하지만 많은 최신의 자율주행 연구들은 각 기술의 개발을 별개로 고려하여 진행해왔다. 결과적으로 통합적인 관점에서의 자율주행 기술 설계는 아직 충분히 고려되어 않았다. 따라서, 본 논문은 복잡한 도심 도로 환경에서 라이다, 카메라, GPS, 그리고 간단한 경로 맵에 기반한 완전 자율주행 알고리즘을 개발하는 것을 목표로 하였다. 제안된 자율주행 알고리즘은 비통제 교차로를 포함한 도심 도로 상황을 차량 거동 예측기와 모델 예측 제어 기법에 기반하여 설계되었다. 본 논문은 동적, 정적 환경 표현 및 종횡방향 거동 계획을 중점적으로 다루었다. 본 논문은 도심 도로 자율주행을 위한 거동 계획 알고리즘의 개요를 제시하였으며, 실제 교통 상황에서의 실험 결과는 제안된 알고리즘의 효과성과 운전자 거동과의 유사성을 보여주었다. 실차 실험 결과는 비통제 교차로를 포함한 도심 시나리오에서의 강건한 성능을 보여주었다.The foci of automotive researches have been expanding from passive safety systems to active safety systems with advances in sensing and processing technologies. Recently, the majority of automotive makers have already commercialized active safety systems, such as adaptive cruise control (ACC), lane keeping assistance (LKA), and autonomous emergency braking (AEB). Such advances have extended the research field beyond active safety systems to automated driving systems to achieve zero fatalities. Especially, automated driving on urban roads has become a key issue because urban roads possess numerous risk factors for traffic accidents, such as sidewalks, blind spots, on-street parking, motorcycles, and pedestrians, which cause higher accident rates and fatalities than motorways. Several projects have been conducted, and many others are still underway to evaluate the effects of automated driving in environmental, demographic, social, and economic aspects. For example, the European project AdaptIVe, develops various automated driving functions and defines specific evaluation methodologies. In addition, CityMobil2 successfully integrates driverless intelligent vehicles in nine other environments throughout Europe. In Japan, the Automated Driving System Research Project began on May 2014, which focuses on the development and verification of automated driving systems and next-generation urban transportation. From a careful review of a considerable amount of literature, automated driving systems have been proven to increase the safety of traffic users, reduce traffic congestion, and improve driver convenience. Various methodologies have been employed to develop the core technology of automated vehicles on urban roads, such as perception, motion planning, and control. However, the current state-of-the-art automated driving algorithms focus on the development of each technology separately. Consequently, designing automated driving systems from an integrated perspective is not yet sufficiently considered. Therefore, this dissertation focused on developing a fully autonomous driving algorithm in urban complex scenarios using LiDAR, vision, GPS, and a simple path map. The proposed autonomous driving algorithm covered the urban road scenarios with uncontrolled intersections based on vehicle motion prediction and model predictive control approach. Mainly, four research issues are considered: dynamic/static environment representation, and longitudinal/lateral motion planning. In the remainder of this thesis, we will provide an overview of the proposed motion planning algorithm for urban autonomous driving and the experimental results in real traffic, which showed the effectiveness and human-like behaviors of the proposed algorithm. The proposed algorithm has been tested and evaluated using both simulation and vehicle tests. The test results show the robust performance of urban scenarios, including uncontrolled intersections.Chapter 1 Introduction 1 1.1. Background and Motivation 1 1.2. Previous Researches 4 1.3. Thesis Objectives 9 1.4. Thesis Outline 10 Chapter 2 Overview of Motion Planning for Automated Driving System 11 Chapter 3 Dynamic Environment Representation with Motion Prediction 15 3.1. Moving Object Classification 17 3.2. Vehicle State based Direct Motion Prediction 20 3.2.1. Data Collection Vehicle 22 3.2.2. Target Roads 23 3.2.3. Dataset Selection 24 3.2.4. Network Architecture 25 3.2.5. Input and Output Features 33 3.2.6. Encoder and Decoder 33 3.2.7. Sequence Length 34 3.3. Road Structure based Interactive Motion Prediction 36 3.3.1. Maneuver Definition 38 3.3.2. Network Architecture 39 3.3.3. Path Following Model based State Predictor 47 3.3.4. Estimation of predictor uncertainty 50 3.3.5. Motion Parameter Estimation 53 3.3.6. Interactive Maneuver Prediction 56 3.4. Intersection Approaching Vehicle Motion Prediction 59 3.4.1. Driver Behavior Model at Intersections 59 3.4.2. Intention Inference based State Prediction 63 Chapter 4 Static Environment Representation 67 4.1. Static Obstacle Map Construction 69 4.2. Free Space Boundary Decision 74 4.3. Drivable Corridor Decision 76 Chapter 5 Longitudinal Motion Planning 81 5.1. In-Lane Target Following 82 5.2. Proactive Motion Planning for Narrow Road Driving 85 5.2.1. Motivation for Collision Preventive Velocity Planning 85 5.2.2. Desired Acceleration Decision 86 5.3. Uncontrolled Intersection 90 5.3.1. Driving Phase and Mode Definition 91 5.3.2. State Machine for Driving Mode Decision 92 5.3.3. Motion Planner for Approach Mode 95 5.3.4. Motion Planner for Risk Management Phase 98 Chapter 6 Lateral Motion Planning 105 6.1. Vehicle Model 107 6.2. Cost Function and Constraints 109 Chapter 7 Performance Evaluation 115 7.1. Motion Prediction 115 7.1.1. Prediction Accuracy Analysis of Vehicle State based Direct Motion Predictor 115 7.1.2. Prediction Accuracy and Effect Analysis of Road Structure based Interactive Motion Predictor 122 7.2. Prediction based Distance Control at Urban Roads 132 7.2.1. Driving Data Analysis of Direct Motion Predictor Application at Urban Roads 133 7.2.2. Case Study of Vehicle Test at Urban Roads 138 7.2.3. Analysis of Vehicle Test Results on Urban Roads 147 7.3. Complex Urban Roads 153 7.3.1. Case Study of Vehicle Test at Complex Urban Roads 154 7.3.2. Closed-loop Simulation based Safety Analysis 162 7.4. Uncontrolled Intersections 164 7.4.1. Simulation based Algorithm Comparison of Motion Planner 164 7.4.2. Monte-Carlo Simulation based Safety Analysis 166 7.4.3. Vehicle Tests Results in Real Traffic Conditions 172 7.4.4. Similarity Analysis between Human and Automated Vehicle 194 7.5. Multi-Lane Turn Intersections 197 7.5.1. Case Study of a Multi-Lane Left Turn Scenario 197 7.5.2. Analysis of Motion Planning Application Results 203 Chapter 8 Conclusion & Future Works 207 8.1. Conclusion 207 8.2. Future Works 209 Bibliography 210 Abstract in Korean 219Docto