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

Dynamic-Occlusion-Aware Risk Identification for Autonomous Vehicles Using Hypergames

A particular challenge for both autonomous vehicles (AV) and human drivers is dealing with risk associated with dynamic occlusion, i.e., occlusion caused by other vehicles in traffic. In order to overcome this challenge, we use the theory of hypergames to develop a novel dynamic-occlusion risk measure (DOR). We use DOR to evaluate the safety of strategic planners, a type of AV behaviour planner that reasons over the assumptions other road users have of each other. We also present a method for augmenting naturalistic driving data to artificially generate occlusion situations. Combining our risk identification and occlusion generation methods, we are able to discover occlusion-caused collisions (OCC), which rarely occur in naturalistic driving data. Using our method we are able to increase the number of dynamic-occlusion situations in naturalistic data by a factor of 70, which allows us to increase the number of OCCs we can discover in naturalistic data by a factor of 40. We show that the generated OCCs are realistic and cover a diverse range of configurations. We then characterize the nature of OCCs at intersections by presenting an OCC taxonomy, which categorizes OCCs based on if they are left-turning or right-turning situations, and if they are reveal or tagging-on situations. Finally, in order to analyze the impact of collisions, we perform a severity analysis, where we find that the majority of OCCs result in high-impact collisions, demonstrating the need to evaluate AVs under occlusion situations before they can be released for commercial use