Belief State Planning for Autonomous Driving: Planning with Interaction, Uncertain Prediction and Uncertain Perception

This thesis presents a behavior planning algorithm for automated driving in urban environments with an uncertain and dynamic nature. The uncertainty in the environment arises by the fact that the intentions as well as the future trajectories of the surrounding drivers cannot be measured directly but can only be estimated in a probabilistic fashion. Even the perception of objects is uncertain due to sensor noise or possible occlusions. When driving in such environments, the autonomous car must predict the behavior of the other drivers and plan safe, comfortable and legal trajectories. Planning such trajectories requires robust decision making when several high-level options are available for the autonomous car. Current planning algorithms for automated driving split the problem into different subproblems, ranging from discrete, high-level decision making to prediction and continuous trajectory planning. This separation of one problem into several subproblems, combined with rule-based decision making, leads to sub-optimal behavior. This thesis presents a global, closed-loop formulation for the motion planning problem which intertwines action selection and corresponding prediction of the other agents in one optimization problem. The global formulation allows the planning algorithm to make the decision for certain high-level options implicitly. Furthermore, the closed-loop manner of the algorithm optimizes the solution for various, future scenarios concerning the future behavior of the other agents. Formulating prediction and planning as an intertwined problem allows for modeling interaction, i.e. the future reaction of the other drivers to the behavior of the autonomous car. The problem is modeled as a partially observable Markov decision process (POMDP) with a discrete action and a continuous state and observation space. The solution to the POMDP is a policy over belief states, which contains different reactive plans for possible future scenarios. Surrounding drivers are modeled with interactive, probabilistic agent models to account for their prediction uncertainty. The field of view of the autonomous car is simulated ahead over the whole planning horizon during the optimization of the policy. Simulating the possible, corresponding, future observations allows the algorithm to select actions that actively reduce the uncertainty of the world state. Depending on the scenario, the behavior of the autonomous car is optimized in (combined lateral and) longitudinal direction. The algorithm is formulated in a generic way and solved online, which allows for applying the algorithm on various road layouts and scenarios. While such a generic problem formulation is intractable to solve exactly, this thesis demonstrates how a sufficiently good approximation to the optimal policy can be found online. The problem is solved by combining state of the art Monte Carlo tree search algorithms with near-optimal, domain specific roll-outs. The algorithm is evaluated in scenarios such as the crossing of intersections under unknown intentions of other crossing vehicles, interactive lane changes in narrow gaps and decision making at intersections with large occluded areas. It is shown that the behavior of the closed-loop planner is less conservative than comparable open-loop planners. More precisely, it is even demonstrated that the policy enables the autonomous car to drive in a similar way as an omniscient planner with full knowledge of the scene. It is also demonstrated how the autonomous car executes actions to actively gather more information about the surrounding and to reduce the uncertainty of its belief state

Belief State Planning for Autonomous Driving: Planning with Interaction, Uncertain Prediction and Uncertain Perception

This work presents a behavior planning algorithm for automated driving in urban environments with an uncertain and dynamic nature. The algorithm allows to consider the prediction uncertainty (e.g. different intentions), perception uncertainty (e.g. occlusions) as well as the uncertain interactive behavior of the other agents explicitly. Simulating the most likely future scenarios allows to find an optimal policy online that enables non-conservative planning under uncertainty

Probabilistic Motion Planning for Automated Vehicles

This thesis targets the problem of motion planning for automated vehicles. As a prerequisite for their on-road deployment, automated vehicles must show an appropriate and reliable driving behavior in mixed traffic, i.e. alongside human drivers. Besides the uncertainties resulting from imperfect perception, occlusions and limited sensor range, also the uncertainties in the behavior of other traffic participants have to be considered. Related approaches for motion planning in mixed traffic often employ a deterministic problem formulation. The solution of such formulations is restricted to a single trajectory. Deviations from the prediction of other traffic participants are accounted for during replanning, while large uncertainties lead to conservative and over-cautious behavior. As a result of the shortcomings of these formulations in cooperative scenarios and scenarios with severe uncertainties, probabilistic approaches are pursued. Due to the need for real-time capability, however, a holistic uncertainty treatment often induces a strong limitation of the action space of automated vehicles. Moreover, safety and traffic rule compliance are often not considered. Thus, in this work, three motion planning approaches and a scenario-based safety approach are presented. The safety approach is based on an existing concept, which targets the guarantee that automated vehicles will never cause accidents. This concept is enhanced by the consideration of traffic rules for crossing and merging traffic, occlusions, limited sensor range and lane changes. The three presented motion planning approaches are targeted towards the different predominant uncertainties in different scenarios, while operating in a continuous action space. For non-interactive scenarios with clear precedence, a probabilistic approach is presented. The problem is modeled as a partially observable Markov decision process (POMDP). In contrast to existing approaches, the underlying assumption is that the prediction of the future progression of the uncertainty in the behavior of other traffic participants can be performed independently of the automated vehicle\u27s motion plan. In addition to this prediction of currently visible traffic participants, the influence of occlusions and limited sensor range is considered. Despite its thorough uncertainty consideration, the presented approach facilitates planning in a continuous action space. Two further approaches are targeted towards the predominant uncertainties in interactive scenarios. In order to facilitate lane changes in dense traffic, a rule-based approach is proposed. The latter seeks to actively reduce the uncertainty in whether other vehicles willingly make room for a lane change. The generated trajectories are safe and traffic rule compliant with respect to the presented safety approach. To facilitate cooperation in scenarios without clear precedence, a multi-agent approach is presented. The globally optimal solution to the multi-agent problem is first analyzed regarding its ambiguity. If an unambiguous, cooperative solution is found, it is pursued. Still, the compliance of other vehicles with the presumed cooperation model is checked, and a conservative fallback trajectory is pursued in case of non-compliance. The performance of the presented approaches is shown in various scenarios with intersecting lanes, partly with limited visibility, as well as lane changes and a narrowing without predefined right of way

자율 주행을 위한 학습 기반의 다중 교통 참여자 경로예측 방법

학위논문 (석사)-- 서울대학교 대학원 : 공과대학 전기·정보공학부, 2019. 2. 서승우.자율주행차량이 안전하면서도, 교통 흐름을 방해하지 않는 인간 수준의 interactive한 주행을 실현하기 위해서는 주변 운전자와 보행자를 포함한 교통 참여자들의 의도를 파악하는 것이 필수적이다. 동일한 도로 환경에서도 주변의 교통 참여자들이 어떤 의도를 가지고 이동하고 있는가에 따라서 적합한 주행 전략은 매번 달라진다. 특히 좁은 공간을 많은 에이전트들이 공유하고 있는 골목길 상황에서는 각 에이전트들의 선택은 제한되지만, 각 에이전트들의 의사결정과정은 그들간의 상호작용이 고려되어 매우 복잡하다.이런 상황에 맞는 경로예측은 과거의 궤적, 현재 인식하고 있는 도로 환경, 주변 교통 참여자의 상태 등을 고려하여 수행되어야 한다. 또한 일반화된 대부분의 환경에서 수행되려면, 빠른 러닝 타임과 동시에 고려하는 교통 참여자의 숫자에 대한 제약이 적어야 한다. 이 논문에서는 각 교통 참여자들의 과거 궤적과 현재 위치 상황, 도로 상황을 동시에 고려하여 모든 참여자들의 상호작용이 고려된 경로예측 방법을 제안한다. 각 물체들의 과거 궤적과 동시에 자율주행 차량이 인식하고 있는 도로와 주변 물체를 입력으로하고, 이 두개의 임베딩 결과를 혼합하여 모든 물체들에 대한 경로 예측을 동시에 수행한다. 이 과정에서 네트워크 구조 내부에서 각각 물체들의 임베딩 결과를 위치 정보에 매칭시킴으로써 효과적으로 주변 상황과 과거의 궤적을 동시에 고려하는 경로 예측을 학습할 수 있는 구조를 제안한다.몇가지 네트워크 구조에 따른 성능 비교와 함께, 다양한 주행 환경에서 정량적인 평가와 정성적인 평가로 유효성을 입증한다.또한 보행자 경로예측 데이터에 테스트를 진행함으로써 타 알고리즘과의 성능을 비교한다.Recent autonomous driving research has shown remarkable and promising results. However, safe, sociable driving in an urban environment still has many challenges ahead. For realizing safe, interactive driving in complex alley scenario which shares a narrow area among traffic participants, It is essential to grasp each other's intention. Even in the same road environment, safe, and sociable driving policy may differ depending on the intention of the traffic participant agents around the ego vehicle. But understanding others intention and predicting their trajectories are complicated because each one basically considers multiple factorsroad environment, state of their surrounding traffic participants at the same time which realized as interaction. In this thesis dissertation, we propose a new trajectory prediction algorithm that considers all the information that each of the traffic participants would consider when they make a decision. By combining both each of history trajectories and grid map of surroundings as a latent vector representation, it predicts all the future trajectories of traffic participant agents around ego vehicle at once. This dissertation suggests two main module that fuses spatial and temporal information effectively. We verify the effectiveness of network structure by testing on the various driving scenario comparing with some network variants through quantitative and qualitative evaluation. Also, the proposed network is verified by applying it to public pedestrian trajectory prediction dataset to verify usability as a generalized methodology and to compare it with other SOTA algorithms.Abstract Contents List of Tables List of Figures 1 Introduction 1.1 Background and Motivation 2 Related Work 2.1 Contributions of the Dissertation 3 Conditional Neural Process 3.1 Conditional Neural Process(CNP) Overview 3.2 Trajectory Prediction with Scene Information as CNP 3.2.1 Formulation 3.2.2 Loss and Training Algorithm 4 Efficient Network Architecture for Intention Prediction 4.1 Network Overview 4.2 Trajectory Encoder 4.2.1 Spatio-Temporal Representation 4.3 Scene Feature Extraction 4.3.1 Side Spatial Extraction 4.4 Trajectory Decoder 5 Experiment 5.1 Driving environment dataset 5.1.1 Data acquisition method 5.1.2 Overview 5.1.3 Alley scenario 5.1.4 Urban Scenario 5.2 Public Pedestrian Dataset 6 ConclusionMaste

Real-time motion planning methods for autonomous on-road driving: state-of-the-art and future research directions

Currently autonomous or self-driving vehicles are at the heart of academia and industry research because of its multi-faceted advantages that includes improved safety, reduced congestion, lower emissions and greater mobility. Software is the key driving factor underpinning autonomy within which planning algorithms that are responsible for mission-critical decision making hold a significant position. While transporting passengers or goods from a given origin to a given destination, motion planning methods incorporate searching for a path to follow, avoiding obstacles and generating the best trajectory that ensures safety, comfort and efficiency. A range of different planning approaches have been proposed in the literature. The purpose of this paper is to review existing approaches and then compare and contrast different methods employed for the motion planning of autonomous on-road driving that consists of (1) finding a path, (2) searching for the safest manoeuvre and (3) determining the most feasible trajectory. Methods developed by researchers in each of these three levels exhibit varying levels of complexity and performance accuracy. This paper presents a critical evaluation of each of these methods, in terms of their advantages/disadvantages, inherent limitations, feasibility, optimality, handling of obstacles and testing operational environments. Based on a critical review of existing methods, research challenges to address current limitations are identified and future research directions are suggested so as to enhance the performance of planning algorithms at all three levels. Some promising areas of future focus have been identified as the use of vehicular communications (V2V and V2I) and the incorporation of transport engineering aspects in order to improve the look-ahead horizon of current sensing technologies that are essential for planning with the aim of reducing the total cost of driverless vehicles. This critical review on planning techniques presented in this paper, along with the associated discussions on their constraints and limitations, seek to assist researchers in accelerating development in the emerging field of autonomous vehicle research