Probabilistic Human Mobility Model in Indoor Environment

Understanding human mobility is important for the development of intelligent mobile service robots as it can provide prior knowledge and predictions of human distribution for robot-assisted activities. In this paper, we propose a probabilistic method to model human motion behaviors which is determined by both internal and external factors in an indoor environment. While the internal factors are represented by the individual preferences, aims and interests, the external factors are indicated by the stimulation of the environment. We model the randomness of human macro-level movement, e.g., the probability of visiting a specific place and staying time, under the Bayesian framework, considering the influence of both internal and external variables. We use two case studies in a shopping mall and in a college student dorm building to show the effectiveness of our proposed probabilistic human mobility model. Real surveillance camera data are used to validate the proposed model together with survey data in the case study of student dorm.Comment: 8 pages, 9 figures, International Joint Conference on Neural Networks (IJCNN) 201

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

Inference of Traffic Regulations at Intersections Based on Trajectory Data

Zahlreiche moderne Lösungen im Bereich Autonomes Fahren greifen auf hochpräzises Kartenmaterial zurück. Neben anderen Informationen muss das Kartenmaterial solche über Verkehrsregeln enthalten. In dieser Arbeit wird eine Offline-Lösung für die Inferenz von Verkehrsregeln an Deutschen Kreuzungen entwickelt. Mithilfe dieser Lösung werden für jeden Fahrstreifen einer Kreuzung Klassifikationsentscheidungen für jede mögliche Zielrichtung, welche von diesem Fahrstreifen aus erreichbar ist, getroffen. Verkehrsregeln werden mithilfe von Hidden-Markov-Models repräsentiert und, basierend auf errechneten Likelihood-Werten, bestimmt. Die Modelle werden mithilfe künstlich erzeugter Trajektorien von Kreuzungsüberquerungen parametrisiert und evaluiert. Unter realen Umständen würden solche Daten opportunistisch und sensorgestützt von einer Fahrzeugflotte über einen längeren Zeitraum hinweg gesammelt werden. In einer Reihe von Experimenten wird eine geeignete Trajektorienrepräsentation festgelegt und der Klassifikationsansatz getestet und verfeinert. Die Klassifikationsperformanz des Ansatzes wird mithilfe eines Kreuzvalidierungsverfahren bestimmt. Mittlere F$_1$-Scores zur Quantifizierung der besten Ergebnisse unter den erzielten Testergebnissen variieren zwischen 0.809 und 0.832. Bezüglich der Verkehrsregeln, welche mithilfe von Vorfahrts- und Stoppschildern, sowie Lichtsignalanlagen kommuniziert werden, werden hohe Klassifikationsleistungen erreicht. Allerdings bestehen Schwierigkeiten bei der Klassifikation im Zusammenhang mit den Verkehrsregeln Vorfahrt achten und Rechts vor Links. Da die initial erzielten Ergebnisse vielversprechend sind, wird empfohlen diesen Ansatz in zukünftigen Arbeiten weiterzuentwickeln und zu verbessern

Fahrerverhaltensvorhersage an Kreisverkehren

Roundabouts are considered important because converting an intersection into a roundabout has been proven to improve safety. However, the absolute number of crashes at roundabouts is still high. Many crashes occur because car drivers fail to yield. Intelligent systems can increase safety if they can prevent crashes by precisely predicting driver maneuvers. Therefore, a reliable and trustworthy predictive model of driver maneuvers is needed. A few studies analyze human behavior at roundabouts. However, they focus on an operational timescale rather than on maneuvers on a tactical timescale. Tactical maneuvers have mostly been investigated in scenarios about typical intersections and overtaking. Thus, there is still a lack of research on driver maneuver prediction at roundabouts. To fill this gap, the objective of this thesis is to develop a model that can predict driver maneuvers at single-lane roundabouts. Two types of driver maneuvers are possible in front of each exit of a roundabout: exiting the roundabout and staying in the roundabout. To predict which maneuver a driver will execute in front of an exit, a driver maneuver predictive model was developed on the basis of an analysis of driver behavior data acquired from a field study and a simulator study. Soft-classification algorithms were proposed to train the predictive model. The model consisted of four sub-models for four different scenarios, which were defined by the correlation between roundabout layouts and drivers' steering behavior. The sub-models make it possible to predict the exiting or staying maneuvers executed in the corresponding scenarios. Furthermore, a personalized predictive model was developed to adapt to individual drivers because different drivers have different driving styles. The driver maneuver predictive model shows excellent predictability: In the scenarios without traffic, the model reported prediction results for more than 97.60% of test drives at the position 10 m from the exits of the roundabouts. Of these drives, more than 97.10% were predicted correctly. The personalized predictive model provided even better prediction results for individual drivers with significantly different driving styles. Moreover, the driver maneuver predictive model also successfully predicts drivers' maneuvers in most scenarios with cyclists. The prediction results show that steering angle, steering angle speed, velocity, and acceleration of the ego car provide the most important information. With this information, the model can predict the maneuver of a driver with any type of driving style at a single-lane roundabout with any type of layout.Kreisverkehre gelten als ein wichtiger Bestandteil der Verkehrsinfrastruktur, da ihre Verwendung anstelle von traditionellen Kreuzungen einen wesentlichen Beitrag zur Verkehrssicherheit leistet. Die absolute Anzahl von Unfällen bleibt jedoch auch an Kreisverkehren noch hoch. Viele Kollisionen werden dabei durch Missachtung der Vorfahrt verursacht. Intelligente Fahrzeugassistenzsysteme könnten hier eingreifen, vorausgesetzt sie verfügen über eine zuverlässige Vorhersage des Fahrerverhaltens. Hierfür wird ein robustes und präzises Modell für die Vorhersage von Fahrmanövern im Kreisverkehr benötigt. Empirische Studien zu menschlichem Verhalten an Kreisverkehren fokussieren in der Regel auf die operationale Ebene der Fahraufgabe, also auf eine zeitlich hoch aufgelöste Zeitskala. Die taktische Ebene, auf der Manöver wie "Verlassen des Kreisverkehr" stattfinden, wurde dabei jedoch nicht ausreichend analysiert. Insbesondere fehlen Modelle, die Fahrmanöver im Kreisverkehr vorhersagen. Ziel dieser Arbeit ist es daher, ein solches Modell für einspurige Kreisverkehre zu entwickeln. Zwei Arten von Manövern sind innerhalb eines einspurigen Kreisverkehrs möglich: Im Kreisel zu bleiben, oder ihn zu verlassen. Um möglichst früh eines der beiden Manöver vorherzusagen wurden im Rahmen dieser Arbeit verschiedene Modelle entwickelt, welche auf Fahrdaten aus dem Realverkehr sowie Simulationsstudien basieren. Für das Training der jeweiligen Modelle werden Soft-Klassifikationsalgorithmen vorgeschlagen, die auf einem Quasi-Hidden-Markov-Modell basieren. Dieses Modell besteht aus vier Teilmodellen für jeweils vier verschiedene Szenarien, die durch die Korrelation zwischen Kreisverkehrlayouts und Lenkverhalten von Fahrern definiert wurden. Mit den Teilmodellen können die in den entsprechenden Szenarien ausgeführten Manöver "Verlassen" oder "Bleiben" vorhergesagt werden. Des Weiteren wurde ein personalisiertes Vorhersagemodell entwickelt, um sich an den individuellen Fahrer anzupassen, da verschiedene Fahrer unterschiedliche Fahrstile aufweisen. Das Fahrmanöver-Vorhersagemodell zeigt eine ausgezeichnete Performanz: In den Szenarien ohne Verkehr lieferte das Modell in einem Abstand von 10 m vor der Kreisverkehrsausfahrt Vorhersagen für mindestens 97,60% aller Testfahrten. Von diesen Fahrten wurden wiederum über 97,10% korrekt vorhergesagt. Personalisierte Modelle erreichen noch bessere Vorhersageergebnisse. Sind weitere Verkehrsteilnehmer in den analysierten Szenarien anwesend liegt die Vorhersagegüte etwas darunter. Die Ergebnisse zeigen, dass Lenkwinkel, Lenkwinkelgeschwindigkeit sowie Eigengeschwindigkeit und -beschleunigung die wichtigsten Informationen liefern. Hiermit kann das Modell das Manöver eines Fahrers mit jeder Art von Fahrstil an einem Kreisverkehr mit jeder Art von Layout vorhersagen

10. Workshop Fahrerassistenzsysteme : FAS 2015

Fahrerassistenzsysteme mit maschineller Wahrnehmung sind inzwischen im Automobil bis in die Mittelklasse hinein etabliert. Dadurch rücken Fragen der Leistungs- und Kostenoptimierung immer stärker in den Vordergrund. Ferner werden neue Mobilitätskonzepte wie auch die Elektromobilität weitere Chancen aber auch neue Herausforderungen für die Fahrer-assistenzsysteme, insbesondere für das HMI, schaffen. Zukünftige funktionale Heraus-forderungen liegen im innerstädtischen Bereich, bei der Realisierung hoch automatisierter Assistenzfunktionen sowie bei Funktionen für spezielle Zielgruppen mit erhöhtem Assistenzbedarf. Der steigende Grad der Autonomie erfordert allerdings neben funktionalen Herausforderungen die sorgfältige Diskussion von Fragen der Systemsicherheit, Systemtransparenz aber auch der möglichen Überautomatisierung. Wie in den vergangenen Jahren bietet der Workshop in Walting ein Diskussionsforum für ausgewiesene Experten im deutschsprachigen Raum, auf dem technische, gesellschaftliche aber auch ethische Frage-stellungen der Fahrerassistenz interdisziplinär diskutiert werden

Turn Prediction At Generalized Intersections

Navigating a car at intersections is one of the most challenging parts of urban driving. Successful navigation needs predicting of intention of other traffic participants at the intersection. Such prediction is an important component for both Advanced Driver Assistance Systems (ADAS) and Autonomous Driving (AD) Systems. In this paper, we present a driver intention prediction model for general intersections. Our model incorporates lane-level maps of an intersection and makes a prediction based on past position and movement of the vehicle. We create a real-world dataset of 375 turning tracks at a variety of intersections. We present turn prediction results based on Hidden Markov Model (HMM), Support Vector Machine (SVM), and Dynamic Bayesian Network (DBN). SVM and DBN models give higher accuracy compared to HMM models. We get over 90% turn prediction accuracy 1.6 seconds before the intersection. Our work advances the state of art in ADAS/AD systems with a turn prediction model for general intersections