Interactive Prediction and Planning for Autonomous Driving: from Algorithms to Fundamental Aspects

Inevitably, autonomous vehicles need to interact with other road participants in a variety of highly complex or critical driving scenarios. It is still an extremely challenging task even for the forefront companies or institutes to enable autonomous vehicles to interactively predict the behavior of others, and plan safe and high-quality motions accordingly. The major obstacles are not just originated from prediction and planning algorithms with insufficient performances. Several fundamental problems in the fields of interactive prediction and planning still remain open, such as formulation, representation and evaluation of interactive prediction methods, motion dataset with densely interactive driving behavior, as well as interface of interactive prediction and planning algorithms. The aforementioned fundamental aspects of interactive prediction and planning are addressed in this dissertation along with various kinds of algorithms. First, generic environmental representation for various scenarios with topological decomposition is constructed, and a corresponding planning algorithm is designed by combining graph search and optimization. Hard constraints in optimization-based planners are also incorporated into the training loss of imitation learning so that the policy net can generate safe and feasible motions in highly constrained scenarios. Unified problem formulation and motion representation are designed for different paradigms of interactive predictors such as planning-based prediction (inverse reinforcement learning), as well as probabilistic graphical models (hidden Markov model) and deep neural networks (mixture density network), which are utilized for the prediction/planning interface design and prediction benchmark. A framework combing decision network and graph-search/optimization/sample-based planner is proposed to achieve a driving strategy which is defensive to potential violations of others, but not overly conservatively to threats of low probabilities. Such driving strategy is achieved via experiments based on the aforementioned interactive prediction and planning algorithms with proper interface designed. These predictors are also evaluated from closed loop perspective considering planning fatality when using the prediction results instead of pure data approximation metrics. Finally, INTERACTION (INTERnational, Adversarial and Cooperative moTION) dataset with highly interactive driving scenarios and behavior from international locations is constructed with interaction density metric defined to compare different datasets. The dataset has been utilized for various behavior-related research areas such as prediction, planning, imitation learning and behavior modeling, and is inspiring new research fields such as representation learning, interaction extraction and scenario generation

준정형화된 환경에서 Look-ahead Point를 이용한 모방학습 기반 자율 내비게이션 방법

학위논문(박사) -- 서울대학교대학원 : 융합과학기술대학원 융합과학부(지능형융합시스템전공), 2023. 2. 박재흥.본 학위논문은 자율주행 차량이 주차장에서 위상지도와 비전 센서로 내비게이션을 수행하는 방법들을 제안합니다. 이 환경에서의 자율주행 기술은 완전 자율주행을 완성하는 데 필요하며, 편리하게 이용될 수 있습니다. 이 기술을 구현하기 위해, 경로를 생성하고 이를 현지화 데이터로 추종하는 방법이 일반적으로 연구되고 있습니다. 그러나, 주차장에서는 도로 간 간격이 좁고 장애물이 복잡하게 분포되어 있어 현지화 데이터를 정확하게 얻기 힘듭니다. 이는 실제 경로와 추종하는 경로 사이에 틀어짐을 발생시켜, 차량과 장애물 간 충돌 가능성을 높입니다. 따라서 현지화 데이터로 경로를 추종하는 대신, 낮은 비용을 가지는 비전 센서로 차량이 주행 가능 영역을 향해 주행하는 방법이 제안됩니다. 주차장에는 차선이 없고 다양한 정적/동적 장애물이 복잡하게 있어, 주행 가능/불가능한 영역을 구분하여 점유 격자 지도를 얻는 것이 필요합니다. 또한, 교차로를 내비게이션하기 위해, 전역 계획에 따른 하나의 갈래 도로만이 주행가능 영역으로 구분됩니다. 갈래 도로는 회전된 바운딩 박스 형태로 인식되며 주행가능 영역 인식과 함께 multi-task 네트워크를 통해 얻어집니다. 주행을 위해 모방학습이 사용되며, 이는 모델-기반 모션플래닝 방법보다 파라미터 튜닝 없이도 다양하고 복잡한 환경을 다룰 수 있고 부정확한 인식 결과에도 강인합니다. 아울러, 이미지에서 제어 명령을 구하는 기존 모방학습 방법과 달리, 점유 격자 지도에서 차량이 도달할 look-ahead point를 학습하는 새로운 모방학습 방법이 제안됩니다. 이 point를 사용함으로써, 모방 학습의 성능을 향상시키는 data aggregation (DAgger) 알고리즘을 별도의 조이스틱 없이 자율주행에 적용할 수 있으며, 전문가는 human-in-loop DAgger 훈련 과정에서도 최적의 행동을 잘 선택할 수 있습니다. 추가로, DAgger 변형 알고리즘들은 안전하지 않거나 충돌에 가까운 상황에 대한 데이터를 샘플링하여 DAgger 성능이 향상됩니다. 그러나, 전체 훈련 데이터셋에서 이 상황에 대한 데이터 비율이 적으면, 추가적인 DAgger 수행 및 사람의 노력이 요구됩니다. 이 문제를 다루기 위해, 가중 손실 함수를 사용하는 새로운 DAgger 훈련 방법인 WeightDAgger 알고리즘이 제안되며, 더 적은 DAgger 반복으로 앞서 언급 것과 유사한 상황에서 전문가의 행동을 더 정확하게 모방할 수 있습니다. DAgger를 동적 상황까지 확장하기 위해, 에이전트와 경쟁하는 적대적 정책이 제안되고, 이 정책을 DAgger 알고리즘에 적용하기 위한 훈련 프레임워크가 제안됩니다. 에이전트는 이전 DAgger 훈련 단계에서 훈련되지 않은 다양한 상황에 대해 훈련될 수 있을 뿐만 아니라 쉬운 상황에서 어려운 상황까지 점진적으로 훈련될 수 있습니다. 실내외 주차장에서의 차량 내비게이션 실험을 통해, 모델-기반 모션 플래닝 알고리즘의 한계 및 이를 다룰 수 있는 제안하는 모방학습 방법의 효용성이 분석됩니다. 또한, 시뮬레이션 실험을 통해, 제안된 WeightDAgger가 기존 DAgger 알고리즘들 보다 더 적은 DAgger 수행 및 사람의 노력이 필요함을 보이며, 적대적 정책을 이용한 DAgger 훈련 방법으로 동적 장애물을 안전하게 회피할 수 있음을 보입니다. 추가적으로, 부록에서는 비전 기반 자율 주차 시스템 및 주차 경로를 빠르게 생성할 수 있는 방법이 소개되어, 비전기반 주행 및 주차를 수행하는 자율 발렛 파킹 시스템이 완성됩니다.This thesis proposes methods for performing autonomous navigation with a topological map and a vision sensor in a parking lot. These methods are necessary to complete fully autonomous driving and can be conveniently used by humans. To implement them, a method of generating a path and tracking it with localization data is commonly studied. However, in such environments, the localization data is inaccurate because the distance between roads is narrow, and obstacles are distributed complexly, which increases the possibility of collisions between the vehicle and obstacles. Therefore, instead of tracking the path with the localization data, a method is proposed in which the vehicle drives toward a drivable area obtained by vision having a low-cost. In the parking lot, there are complicated various static/dynamic obstacles and no lanes, so it is necessary to obtain an occupancy grid map by segmenting the drivable/non-drivable areas. To navigating intersections, one branch road according to a global plan is configured as the drivable area. The branch road is detected in a shape of a rotated bounding box and is obtained through a multi-task network that simultaneously recognizes the drivable area. For driving, imitation learning is used, which can handle various and complex environments without parameter tuning and is more robust to handling an inaccurate perception result than model-based motion-planning algorithms. In addition, unlike existing imitation learning methods that obtain control commands from an image, a new imitation learning method is proposed that learns a look-ahead point that a vehicle will reach on an occupancy grid map. By using this point, the data aggregation (DAgger) algorithm that improves the performance of imitation learning can be applied to autonomous navigating without a separate joystick, and the expert can select the optimal action well even in the human-in-loop DAgger training process. Additionally, DAgger variant algorithms improve DAgger's performance by sampling data for unsafe or near-collision situations. However, if the data ratio for these situations in the entire training dataset is small, additional DAgger iteration and human effort are required. To deal with this problem, a new DAgger training method using a weighted loss function (WeightDAgger) is proposed, which can more accurately imitate the expert action in the aforementioned situations with fewer DAgger iterations. To extend DAgger to dynamic situations, an adversarial agent policy competing with the agent is proposed, and a training framework to apply this policy to DAgger is suggested. The agent can be trained for a variety of situations not trained in previous DAgger training steps, as well as progressively trained from easy to difficult situations. Through vehicle navigation experiments in real indoor and outdoor parking lots, limitations of the model-based motion-planning algorithms and the effectiveness of the proposed method to deal with them are analyzed. Besides, it is shown that the proposed WeightDAgger requires less DAgger performance and human effort than the existing DAgger algorithms, and the vehicle can safely avoid dynamic obstacles with the DAgger training framework using the adversarial agent policy. Additionally, the appendix introduces a vision-based autonomous parking system and a method to quickly generate the parking path, completing the vision-based autonomous valet parking system that performs driving as well as parking.1 INTRODUCTION 1 1.1 Autonomous Driving System and Environments 1 1.2 Motivation 4 1.3 Contributions of Thesis 6 1.4 Overview of Thesis 8 2 MULTI-TASK PERCEPTION NETWORK FOR VISION-BASED NAVIGATION 9 2.1 Introduction 9 2.1.1 Related Works 10 2.2 Proposed Method 13 2.2.1 Bird's-Eye-View Image Transform 14 2.2.2 Multi-Task Perception Network 15 2.2.2.1 Drivable Area Segmentation (Occupancy Grid Map (OGM)) 16 2.2.2.2 Rotated Road Bounding Box Detection 18 2.2.3 Intersection Decision 21 2.2.3.1 Road Occupancy Grid Map (OGMroad) 22 2.2.4 Merged Occupancy Grid Map (OGMmer) 23 2.3 Experiment 25 2.3.1 Experimental Setup 25 2.3.1.1 Autonomous Vehicle 25 2.3.1.2 Multi-task Network Setup 27 2.3.1.3 Model-based Branch Road Detection Method 29 2.3.2 Experimental Results 30 2.3.2.1 Quantitative Analysis of Multi-Task Network 30 2.3.2.2 Comparison of Branch Road Detection Method 31 2.4 Conclusion 34 3 DATA AGGREGATION (DAGGER) ALGORITHM WITH LOOK-AHEAD POINT FOR AUTONOMOUS DRIVING IN SEMI-STRUCTURED ENVIRONMENT 35 3.1 Introduction 35 3.2 Related Works & Background 41 3.2.1 DAgger Algorithms for Autonomous Driving 41 3.2.2 Behavior Cloning 42 3.2.3 DAgger Algorithm 43 3.3 Proposed Method 45 3.3.1 DAgger with Look-ahead Point Composition (State & Action) 45 3.3.2 Loss Function 49 3.3.3 Data-sampling Function in DAgger 50 3.3.4 Reasons to Use Look-ahead Point As Action 52 3.4 Experimental Setup 54 3.4.1 Driving Policy Network Training 54 3.4.2 Model-based Motion-Planning Algorithms 56 3.5 Experimental Result 57 3.5.1 Quantitative Analysis of Driving Policy 58 3.5.1.1 Collision Rate 58 3.5.1.2 Safe Distance Range Ratio 59 3.5.2 Qualitative Analysis of Driving Policy 60 3.5.2.1 Limitations of Tentacle Algorithm 60 3.5.2.2 Limitations of VVF Algorithm 61 3.5.2.3 Limitations of Both Tentacle and VVF 62 3.5.2.4 Driving Results on Noisy Occupancy Grid Map 63 3.5.2.5 Intersection Navigation 65 3.6 Conclusion 68 4 WEIGHT DAGGER ALGORITHM FOR REDUCING IMITATION LEARNING ITERATIONS 70 4.1 Introduction 70 4.2 Related Works & Background 71 4.3 Proposed Method 74 4.3.1 Weighted Loss Function in WeightDAgger 75 4.3.2 Weight Update Process in Entire Training Dataset 78 4.4 Experiments 80 4.4.1 Experimental Setup 80 4.4.2 Experimental Results 82 4.4.2.1 Ablation Study According to τ 82 4.4.2.2 Ablation Study According to ε 83 4.4.2.3 Ablation Study According to α 84 4.4.2.4 Driving Test Results 85 4.4.3 Walking Robot Experiments 86 4.5 Conclusion 87 5 DAGGER USING ADVERSARIAL AGENT POLICY FOR DYNAMIC SITUATIONS 89 5.1 Introduction 89 5.2 Related Works & Background 91 5.2.1 Motion-planning Algorithms for Dynamic Situations 91 5.2.2 DAgger Algorithm for Dynamic Situation 93 5.3 Proposed Method 95 5.3.1 DAgger Training Framework Using Adversarial Agent Policy 95 5.3.2 Applying to Oncoming Dynamic Obstacle Avoidance Task 97 5.3.2.1 Ego Agent Policy 98 5.3.2.2 Adversarial Agent Policy 100 5.4 Experiments 101 5.4.1 Experimental Setup 101 5.4.1.1 Ego Agent Policy Training 102 5.4.1.2 Adversarial Agent Policy Training 103 5.4.2 Experimental Result 103 5.4.2.1 Performance of Adversarial Agent Policy 103 5.4.2.2 Ego Agent Policy Performance Comparisons Trained with / without Adversarial Agent Policy 104 5.5 Conclusion 106 6 CONCLUSIONS 107 Appendix A 110 A.1 Vision-based Re-plannable Autonomous Parking System 110 A.1.1 Parking Spot Detection 112 A.1.2 Re-planning Method 113 A.2 Biased Target-tree* with RRT* Algorithm for Fast Parking Path Planning 115 A.2.1 Introduction 115 A.2.2 Proposed Method 117 A.2.3 Experiments 119 Abstract (In Korean) 143 Acknowledgement 145박

Egocentric Vision-based Future Vehicle Localization for Intelligent Driving Assistance Systems

Predicting the future location of vehicles is essential for safety-critical applications such as advanced driver assistance systems (ADAS) and autonomous driving. This paper introduces a novel approach to simultaneously predict both the location and scale of target vehicles in the first-person (egocentric) view of an ego-vehicle. We present a multi-stream recurrent neural network (RNN) encoder-decoder model that separately captures both object location and scale and pixel-level observations for future vehicle localization. We show that incorporating dense optical flow improves prediction results significantly since it captures information about motion as well as appearance change. We also find that explicitly modeling future motion of the ego-vehicle improves the prediction accuracy, which could be especially beneficial in intelligent and automated vehicles that have motion planning capability. To evaluate the performance of our approach, we present a new dataset of first-person videos collected from a variety of scenarios at road intersections, which are particularly challenging moments for prediction because vehicle trajectories are diverse and dynamic.Comment: To appear on ICRA 201

Deep Learning for Safe Autonomous Driving: Current Challenges and Future Directions

[EN] Advances in information and signal processing technologies have a significant impact on autonomous driving (AD), improving driving safety while minimizing the efforts of human drivers with the help of advanced artificial intelligence (AI) techniques. Recently, deep learning (DL) approaches have solved several real-world problems of complex nature. However, their strengths in terms of control processes for AD have not been deeply investigated and highlighted yet. This survey highlights the power of DL architectures in terms of reliability and efficient real-time performance and overviews state-of-the-art strategies for safe AD, with their major achievements and limitations. Furthermore, it covers major embodiments of DL along the AD pipeline including measurement, analysis, and execution, with a focus on road, lane, vehicle, pedestrian, drowsiness detection, collision avoidance, and traffic sign detection through sensing and vision-based DL methods. In addition, we discuss on the performance of several reviewed methods by using different evaluation metrics, with critics on their pros and cons. Finally, this survey highlights the current issues of safe DL-based AD with a prospect of recommendations for future research, rounding up a reference material for newcomers and researchers willing to join this vibrant area of Intelligent Transportation Systems.This work was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) Grant funded by the Korea Government (MSIT) (2019-0-00136, Development of AI-Convergence Technologies for Smart City Industry Productivity Innovation); The work of Javier Del Ser was supported by the Basque Government through the EMAITEK and ELKARTEK Programs, as well as by the Department of Education of this institution (Consolidated Research Group MATHMODE, IT1294-19); VHCA received support from the Brazilian National Council for Research and Development (CNPq, Grant #304315/2017-6 and #430274/2018-1).Muhammad, K.; Ullah, A.; Lloret, J.; Del Ser, J.; De Albuquerque, VHC. (2021). Deep Learning for Safe Autonomous Driving: Current Challenges and Future Directions. IEEE Transactions on Intelligent Transportation Systems. 22(7):4316-4336. https://doi.org/10.1109/TITS.2020.30322274316433622

Learning-Aware Safety for Interactive Autonomy

One of the outstanding challenges for the widespread deployment of robotic systems like autonomous vehicles is ensuring safe interaction with humans without sacrificing efficiency. Existing safety analysis methods often neglect the robot's ability to learn and adapt at runtime, leading to overly conservative behavior. This paper proposes a new closed-loop paradigm for synthesizing safe control policies that explicitly account for the system's evolving uncertainty under possible future scenarios. The formulation reasons jointly about the physical dynamics and the robot's learning algorithm, which updates its internal belief over time. We leverage adversarial deep reinforcement learning (RL) for scaling to high dimensions, enabling tractable safety analysis even for implicit learning dynamics induced by state-of-the-art prediction models. We demonstrate our framework's ability to work with both Bayesian belief propagation and the implicit learning induced by a large pre-trained neural trajectory predictor.Comment: Conference on Robot Learning 202