Right-Hook Crash Scenario: Effects of Environmental Factors on Driver\u27s Visual Attention and Crash Risk

A right-hook (RH) crash is a common type of bicycle–motor vehicle crash that occurs between a right-turning vehicle and through-moving bicycle at an intersection in right-hand driving countries. Despite the frequency and severity of this crash type, no significant driver-performance based evidence of the causes of RH crashes at signalized intersections was found in the literature. This study examined the driver’s visual attention in a right-turning scenario at signalized intersections with bicycle lanes but no exclusive right-turning lanes while interacting with a bicyclist to develop an understanding of RH crash causality. Fifty-one participants in 21 simulated road scenarios performed a right-turning maneuver at a signalized intersection while conflicting with traffic, pedestrians, and bicyclists. Overall, a total of 820 (41 × 20) observable right-turn maneuvers with visual attention data were analyzed. The results show that in the presence of conflicting oncoming left-turning vehicular traffic, drivers spent less visual attention on the approaching bicyclist, thus, making them less likely to be detected by the driver. The presence of oncoming left-turning traffic and the bicyclist’s speed and relative position, and conflicting pedestrians were found likely to increase the risk of RH crashes. The results of the current study will help identify effective crash mitigation strategies that may include improving the vehicle–human interface or the implementation of design treatments in the road environment to improve driver and bicyclist performance

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

Vehicle Tracking and Motion Estimation Based on Stereo Vision Sequences

In this dissertation, a novel approach for estimating trajectories of road vehicles such as cars, vans, or motorbikes, based on stereo image sequences is presented. Moving objects are detected and reliably tracked in real-time from within a moving car. The resulting information on the pose and motion state of other moving objects with respect to the own vehicle is an essential basis for future driver assistance and safety systems, e.g., for collision prediction. The focus of this contribution is on oncoming traffic, while most existing work in the literature addresses tracking the lead vehicle. The overall approach is generic and scalable to a variety of traffic scenes including inner city, country road, and highway scenarios. A considerable part of this thesis addresses oncoming traffic at urban intersections. The parameters to be estimated include the 3D position and orientation of an object relative to the ego-vehicle, as well as the object's shape, dimension, velocity, acceleration and the rotational velocity (yaw rate). The key idea is to derive these parameters from a set of tracked 3D points on the object's surface, which are registered to a time-consistent object coordinate system, by means of an extended Kalman filter. Combining the rigid 3D point cloud model with the dynamic model of a vehicle is one main contribution of this thesis. Vehicle tracking at intersections requires covering a wide range of different object dynamics, since vehicles can turn quickly. Three different approaches for tracking objects during highly dynamic turn maneuvers up to extreme maneuvers such as skidding are presented and compared. These approaches allow for an online adaptation of the filter parameter values, overcoming manual parameter tuning depending on the dynamics of the tracked object in the scene. This is the second main contribution. Further issues include the introduction of two initialization methods, a robust outlier handling, a probabilistic approach for assigning new points to a tracked object, as well as mid-level fusion of the vision-based approach with a radar sensor. The overall system is systematically evaluated both on simulated and real-world data. The experimental results show the proposed system is able to accurately estimate the object pose and motion parameters in a variety of challenging situations, including night scenes, quick turn maneuvers, and partial occlusions. The limits of the system are also carefully investigated.In dieser Dissertation wird ein Ansatz zur Trajektorienschätzung von Straßenfahrzeugen (PKW, Lieferwagen, Motorräder,...) anhand von Stereo-Bildfolgen vorgestellt. Bewegte Objekte werden in Echtzeit aus einem fahrenden Auto heraus automatisch detektiert, vermessen und deren Bewegungszustand relativ zum eigenen Fahrzeug zuverlässig bestimmt. Die gewonnenen Informationen liefern einen entscheidenden Grundstein für zukünftige Fahrerassistenz- und Sicherheitssysteme im Automobilbereich, beispielsweise zur Kollisionsprädiktion. Während der Großteil der existierenden Literatur das Detektieren und Verfolgen vorausfahrender Fahrzeuge in Autobahnszenarien adressiert, setzt diese Arbeit einen Schwerpunkt auf den Gegenverkehr, speziell an städtischen Kreuzungen. Der Ansatz ist jedoch grundsätzlich generisch und skalierbar für eine Vielzahl an Verkehrssituationen (Innenstadt, Landstraße, Autobahn). Die zu schätzenden Parameter beinhalten die räumliche Lage des anderen Fahrzeugs relativ zum eigenen Fahrzeug, die Objekt-Geschwindigkeit und -Längsbeschleunigung, sowie die Rotationsgeschwindigkeit (Gierrate) des beobachteten Objektes. Zusätzlich werden die Objektabmaße sowie die Objektform rekonstruiert. Die Grundidee ist es, diese Parameter anhand der Transformation von beobachteten 3D Punkten, welche eine ortsfeste Position auf der Objektoberfläche besitzen, mittels eines rekursiven Schätzers (Kalman Filter) zu bestimmen. Ein wesentlicher Beitrag dieser Arbeit liegt in der Kombination des Starrkörpermodells der Punktewolke mit einem Fahrzeugbewegungsmodell. An Kreuzungen können sehr unterschiedliche Dynamiken auftreten, von einer Geradeausfahrt mit konstanter Geschwindigkeit bis hin zum raschen Abbiegen. Um eine manuelle Parameteradaption abhängig von der jeweiligen Szene zu vermeiden, werden drei verschiedene Ansätze zur automatisierten Anpassung der Filterparameter an die vorliegende Situation vorgestellt und verglichen. Dies stellt den zweiten Hauptbeitrag der Arbeit dar. Weitere wichtige Beiträge sind zwei alternative Initialisierungsmethoden, eine robuste Ausreißerbehandlung, ein probabilistischer Ansatz zur Zuordnung neuer Objektpunkte, sowie die Fusion des bildbasierten Verfahrens mit einem Radar-Sensor. Das Gesamtsystem wird im Rahmen dieser Arbeit systematisch anhand von simulierten und realen Straßenverkehrsszenen evaluiert. Die Ergebnisse zeigen, dass das vorgestellte Verfahren in der Lage ist, die unbekannten Objektparameter auch unter schwierigen Umgebungsbedingungen, beispielsweise bei Nacht, schnellen Abbiegemanövern oder unter Teilverdeckungen, sehr präzise zu schätzen. Die Grenzen des Systems werden ebenfalls sorgfältig untersucht

Exploring Older Driver Lateral Head Rotations at Intersections Using Naturalistic Driving Data

This study represented a meta-analysis across two naturalistic driving databases which were collected in the same geographic area but focused on distinct age groups. Differences in range of lateral head rotation between older and middle-aged drivers traversing the same pathway through unprotected left turn intersections were examined. These driving scenarios are known to be among the riskiest and most difficult for older drivers, who demonstrated an increased range of head rotation compared to their middle-aged counterparts. These results are interpreted in the context of possible compensation for reduced fields of view

A car lane-changing model under bus priority-lane effects

Car lane-changing behaviour has been well investigated at merging locations or weaving sections where the lane-changes are usually due to different origin-destination trip purposes. However, the lane-changing behaviour under the effects of bus priority-lanes in urban streets has not been received much attention. This kind of behaviour is found to initially depend on the existence of oncoming buses in priority-lanes in urban streets. In this paper, a car lane-changing model under bus priority-lane effects in urban streets is proposed. This model comprises three steps: looking-back threshold determination, gap acceptance model and execution model. The model’s parameters are estimated jointly by using the Maximum Likelihood Method. The research results show that the car lane-changing behaviour under bus-priority-lane effects in urban streets is considered compulsory behaviour. The behaviour has specific characteristics with smaller critical gaps compared with those at other normal lane cases and can be modelled by the proposed model

교차로에서 자율주행 차량의 제한된 가시성과 불확실성을 고려한 종방향 거동계획

학위논문(박사) -- 서울대학교대학원 : 공과대학 기계공학부, 2023. 2. 이경수.This dissertation presents a novel longitudinal motion planning of autonomous vehicle at urban intersection to overcome the limited visibility due to complicated road structures and sensor specification, guaranteeing the safety from the potential collision with vehicles appearing from the occluded region. The intersection autonomous driving requires high level of safety due to congested traffics and environmental complexities. Due to complicated road structures and the detection range of perception sensors, the occluded region is generated in urban autonomous driving. The virtual target is one of the motion planning methods to react the sudden appearance of vehicles from the blind spot. The Gaussian Process Regression (GPR) is implemented to train the virtual target model to generate various future driving trajectories interacting with the motion of the ego vehicle. The GPR model provides not only the predicted trajectories of the virtual target but also the uncertainty of the future motion. Therefore, prediction results from GPR can be utilized to a position constraint for the Model Predictive Control (MPC), and the uncertainties are taken into account as a chance constraint in the MPC. In order to comprehend the surrounding environment including dynamic objects, a region of interest (ROI) is defined to determine targets of the interest. With the pre-determined driving route of the ego vehicle and the route information of the intersection, driving lanes intersecting with the ego driving lane can be determined, and the intersecting lanes are defined as ROI, reducing the computational load by eliminating targets of disinterest. Then the future motion of the selected target is predicted by a Long Short-Term Memory-Recurrent Neural Network (LSTM-RNN). Driving data for training are directly obtained with two different autonomous vehicles, providing their odometry information regardless to the limited field of view (FOV). For a widely known autonomous driving datasets such as Waymo and nuScenes, the vehicle odometry information are collected from the perceptive sensors mounted on the test vehicle. Thus, information of target that are out of the FOV of the test vehicle cant be obtained. The obtained training data are organized in the target centered coordinates for better input-domain adaptation and generalization. The mean squared error and the negative log likelihood loss functions are adapted to train and provide the uncertainty information of the target vehicle for the motion planning of the autonomous vehicle. The MPC with a chance constraint is formulated to optimize the longitudinal motion of the autonomous vehicle. The dynamic and actuator constraints are designed to provide ride comfort and safety to drivers. The position constraint with the chance constraint guarantees the safety and prevent the potential collision with target vehicles. The position constraint for the travel distance over the prediction horizon time is determined based on the clearance between the predicted trajectories of the target and ego vehicle at every prediction sample time. The performance and feasibility of the proposed algorithm are evaluated via computer simulation and test-data based simulation. The offline simulation validates the safety of the proposed algorithm, and the suggested motion planner has been implemented on an autonomous driving vehicle and tested in a real road. Through the implementation of the algorithm to an actual vehicle, the suggested algorithm is confirmed to be applicable in real life autonomous driving.본 논문은 복잡한 도로 구조와 센서 사양으로 인한 시야 제한을 극복하며 사각지대에서 등장하는 차량과의 잠재적인 충돌로부터 안전을 보장하기 위한 도심 교차로에서의 자율주행차의 새로운 종방향 거동 계획을 제시한다. 도심 자율주행은 교통체증과 환경의 복잡성으로 인해 높은 수준의 안전성이 요구됩니다. 복잡한 도로 구조와 인지 센서의 인지 범위로 인해 도심 자율주행에서는 사각지대가 발생한다. 가상 타겟은 사각지대에서 차량의 갑작스러운 출현에 대응하기 위한 거동 계획 방법 중 하나입니다. 자차량의 거동과 상호작용하는 다양한 미래 주행 궤적을 생성하는 가상 타겟 모델을 구현하기 위하여 Gaussian Process Regression (GPR) 방법을 사용합니다. GPR 모델은 가상 표적의 예측된 궤적뿐만 아니라 미래 궤적에 대한 불확실성도 제공합니다. 따라서 GPR의 예측 결과는 Model Predictive Control (MPC)에 대한 위치 제약 조건으로 활용될 수 있으며 불확실성은 MPC에서 기회 제약 조건으로 고려됩니다. 동적 객체를 포함한 주변 환경을 파악하기 위해 관심영역을 정의하여 목표 대상을 결정합니다. 미리 결정된 자차량의 주행경로와 교차로의 경로정보를 통하여 자차량의 주행차로와 교차하는 다른 차선을 판단하여 관심영역으로 정의함으로써 관심영역 밖의 차량을 제외하여 연산량을 감소시킬 수 있다. 다음으로 인지된 차량의 미래 이동 궤적은 LSTM-RNN (Long Short-Term Memory Recurrent Neural Network)에 의해 예측됩니다. 훈련을 위한 주행 데이터는 두 대의 자율주행 차량에서 직접 획득하여 제한된 시야에 관계없이 차량의 상태 정보를 제공합니다. 구글 Waymo 및 nuScenes와 같이 널리 알려진 자율주행 데이터의 경우 차량 상태 정보는 테스트 차량에 장착된 인지 센서에서 수집됩니다. 따라서 테스트 차량의 시야에서 벗어나 있는 차량 정보는 얻을 수 없습니다. 취득한 주행 데이터는 더 나은 입력 데이터 적응 및 일반화를 위해 자차가 아닌 타겟차량 중심 좌표로 구성됩니다. 손실함수로 평균 제곱 오차 및 음의 로그 우도함수를 사용하였고 음의 로그 우도함수는 자율주행 차량의 거동계획에 사용될 수 있게 타겟차량의 미래 궤적에 대한 불확실성 정보를 제공한다. 기회 제약 조건이 있는 MPC는 자율차량의 종방향 거동을 최적화하도록 구현됩니다. 동적 제약 조건 및 구동기 제약 조건은 운전자에게 승차감과 안전을 제공하도록 설계되었습니다. 기회 제약 조건은 위치 제약 조건을 강건하게 하여 안전을 보장하고 대상 차량과의 잠재적인 충돌을 방지합니다. 예측 시간동안 이동 거리에 대한 위치 제약 조건은 각 예측시간의 타겟과 자차량의 예측된 궤적 간의 거리 차이에 의해 결정된다. 제안한 알고리즘의 성능과 타당성은 컴퓨터 시뮬레이션과 테스트 데이터 기반 시뮬레이션을 통해 평가된다. 오프라인 시뮬레이션을 통해 제안한 알고리즘의 안전성을 검증하였으며 제안한 거동계획 알고리즘을 자율주행차에 구현하여 실제 도로에서 테스트하였다. 제안한 알고리즘을 실제 차량에 구현하여 실제 자율주행에 적용할 수 있음을 확인하였다.Chapter 1. Introduction 1 1.1. Research Background and Motivation of Intersection Autonomous Driving 1 1.2. Previous Researches on Intersection Autonomous Driving 9 1.2.1. Research on Trajectory Prediction and Intention Inference at Urban Intersection 10 1.2.2. Research on Intersection Motion Planning 11 1.3. Thesis Objectives 18 1.4. Thesis Outline 19 Chapter 2. Overall Architecture of Intersection Autonomous Driving System 22 2.1. Software Configuration of Intersection Autonomous Driving 22 2.2. Hardware Configuration of Autonomous Driving and Test Vehicle 24 2.3. Vehicle Test Environment for Intersection Autonomous Driving 25 Chapter 3. Virtual Target Modelling for Intersection Motion Planning 27 3.1. Limitation of Conventional Virtual Target Model for Intersection 27 3.2. Virtual Target Generation for Intersection Occlusion 31 3.3. Intersection Virtual Target Modeling 34 3.3.1. Gaussian Process Regression based Virtual Target Model at Intersection 35 3.3.2. Data Processing for Gaussian Process Regression based Virtual Target Model 38 3.3.3. Definition of Visibility Index of Virtual Target at Intersection 45 3.3.4. Long Short-Term Memory based Virtual Target Model at Intersection 51 Chapter 4. Surrounding Vehicle Motion Prediction at Intersection 54 4.1. Intersection Surrounding Vehicle Classification 54 4.2. Data-driven Vehicle State based Motion Prediction at Intersection 58 4.2.1. Network Architecture of Motion Predictor 58 4.2.2. Dataset Processing of the Network 65 Chapter 5. Intersection Longitudinal Motion Planning 68 5.1. Outlines of Longitudinal Motion Planning with Model Predictive Control 68 5.2. Stochastic Model Predictive Control of Intersection Motion Planner 69 5.2.1. Definition of System Dynamics Model 69 5.2.2. Ego Vehicle Prediction and Reference States Definition 70 5.2.3. Safety Clearance Decision for Intersection Collision Avoidance 71 5.2.4. Driving Mode Decision of Intersection Motion Planning 79 5.2.5. Formulation of Model Predictive Control with the Chance Constraint 83 Chapter 6. Performance Evaluation of Intersection Longitudinal Motion Planning 86 6.1. Performance Evaluation of Virtual Target Prediction at Intersection 86 6.1.1. GPR based Virtual Target Model Prediction Results 86 6.1.2. Intersection Autonomous Driving Computer Simulation Environment 90 6.1.2.1. Simulation Result of Effect of Virtual Target in Intersection Autonomous Driving 92 6.1.2.2. Virtual Target Simulation Result of the Right Turn Across Path Scenario in the Intersection 96 6.1.2.3. Virtual Target Simulation Result of the Straight Across Path Scenario in the Intersection 102 6.1.2.4. Virtual Target Simulation Result of the Left Turn Across Path Scenario in the Intersection 108 6.1.2.5. Virtual Target Simulation Result of Crooked T-shaped Intersection 113 6.2. Performance Evaluation of Data-driven Vehicle State based Motion Prediction at Intersection 124 6.2.1. Data-driven Motion Prediction Accuracy Analysis 124 6.2.2. Prediction Trajectory Accuracy Analysis 134 6.3. Vehicle Test for Intersection Autonomous Driving 146 6.3.1. Test Vehicle Configuration for Intersection Autonomous Driving 146 6.3.2. Software Configuration for Autonomous Vehicle Operation 147 6.3.3. Vehicle Test Environment for Intersection Autonomous Driving 148 6.3.4. Vehicle Test Result of Intersection Autonomous Driving 151 Chapter 7. Conclusion and Future Work 161 7.1. Conclusion 161 7.2. Future Work 164 Bibliography 166 Abstract in Korean 172박

HAZARD PERCEPTION TRAINING FOR ADOLESCENTS WITH AUTISM SPECTRUM DISORDER ON THE INTERACTIVE DRIVING SIMULATOR: USING EYE TRACKING TECHNOLOGY TO DETERMINE EFFECTIVENESS

Rationale: Driving is an important developmental milestone for all adolescents as it increases their independence and ability to participate in vehicle-dependent activities. However, adolescents with high functioning autism spectrum disorder (HFASD) are less likely to obtain licenses and drive independently due to characteristics related to their diagnosis. Although current research exists exploring the efficacy of driving simulator training for adolescent drivers with HFASD and eye tracking, there is a gap in the literature related to training on the simulator and its effects on overall driving performance and hazard perception and response in this population. Purpose: This pilot study utilized a training protocol on the simulator that included hazard perception to determine its effect on overall driving performance. Eye tracking technology was used to determine if there was a change in hazard perception and response to non-social and social hazards after training. Design: This study was a one group, pretest-posttest intervention design. Methods: There were 17 participants between the ages of 15 and 22 with a self-reported diagnosis of ASD and a desire to learn to drive independently. Each participant completed a pre-test and post-test on the driving simulator while wearing eye tracking technology. Each participant completed a protocol of 30 learning modules with scenarios related to driving skills and hazard detection and response in one-to-one training. Analysis: Driving performance was measured by a quantitative score from a standardized observational tool for driving. Eye tracking measures including fixation duration, fixation count, and time to first fixation were analyzed using a Wilcoxon Signed Rank Test. Results: Participants significantly increased their overall driving performance scores pre-test to post-test. Results of hazard perception using eye tracking technology tended towards improvement overall, but specific hazard results were inconsistent and varied for both non-social and social hazards in terms of fixation duration, fixation count, and time to first fixation. Discussion: Findings from this study indicate driving simulator training related to hazard perception was effective in improving overall driving simulator performance in adolescents with HFASD. Additionally, findings indicate hazard perception and response differs for this population after hazard perception training, but specific eye tracking measures may increase or decrease, and results may not be specific to non-social or social hazards

iDriving: Toward Safe and Efficient Infrastructure-directed Autonomous Driving

Autonomous driving will become pervasive in the coming decades. iDriving improves the safety of autonomous driving at intersections and increases efficiency by improving traffic throughput at intersections. In iDriving, roadside infrastructure remotely drives an autonomous vehicle at an intersection by offloading perception and planning from the vehicle to roadside infrastructure. To achieve this, iDriving must be able to process voluminous sensor data at full frame rate with a tail latency of less than 100 ms, without sacrificing accuracy. We describe algorithms and optimizations that enable it to achieve this goal using an accurate and lightweight perception component that reasons on composite views derived from overlapping sensors, and a planner that jointly plans trajectories for multiple vehicles. In our evaluations, iDriving always ensures safe passage of vehicles, while autonomous driving can only do so 27% of the time. iDriving also results in 5x lower wait times than other approaches because it enables traffic-light free intersections

Right-hook crash causality at signalized intersections

A Right-hook (RH) crash is a common type of bicycle-motor vehicle crash that occurs between a right-turning vehicle and through-moving bicycle at an intersection. At signalized intersections, RH crashes can occur at the onset of the green or during the latter portion of the green phase. In spite of the frequency and severity of this crash type, no experimental studies have provided compelling evidence as to the root causes of RH crashes at signalized intersections. This research provided improved understanding of RH crash causal factors during the latter portion of the green phase through an online survey and driving simulator experiment. From the 209 self-reported online survey responses, it was found that 78% of bicyclists were unaware of their stopping position with respect to stopped vehicles queued at an intersection during a red indication, and 19% of motorists (n = 246) reported that they would not yield to the adjacent bicyclist approaching from behind if they were detected in rear-view or side-view mirrors. The driving simulator experiment (n = 51) investigated RH crash causal factors related to the motorist and built environment using three different motorist performance measures: i) visual attention, ii) situation awareness (SA) and iii) crash avoidance behavior. Motorist’s visual attention measure revealed that in the presence of oncoming vehicular traffic, motorists spent the majority of their visual attention looking at the oncoming traffic that posed immediate hazard to them and failed to detect a bicyclist approaching from behind. Motorists' SA measure indicated that motorists detect a bicyclist riding in their forward field of view more successfully than a bicyclist approaching from behind in the vehicle's blind spot. Motorist's crash avoidance behavior revealed that 92% of 26 observed crashes occurred with a bicyclist approaching from behind in the vehicle's blind spot and oncoming vehicles were present in 88% of those crashes. Also, 81% of observed crashes occurred due to inadequate surveillance.Keywords: driving simulator study, bicycle-motor vehicle crash, right-hook cras