Intention-Aware Decision-Making for Mixed Intersection Scenarios

This paper presents a white-box intention-aware decision-making for the handling of interactions between a pedestrian and an automated vehicle (AV) in an unsignalized street crossing scenario. Moreover, a design framework has been developed, which enables automated parameterization of the decision-making. This decision-making is designed in such a manner that it can understand pedestrians in urban traffic and can react accordingly to their intentions. That way, a human-like response to the actions of the pedestrian is ensured, leading to a higher acceptance of AVs. The core notion of this paper is that the intention prediction of the pedestrian to cross the street and decision-making are divided into two subsystems. On the one hand, the intention detection is a data-driven, black-box model. Thus, it can model the complex behavior of the pedestrians. On the other hand, the decision-making is a white-box model to ensure traceability and to enable a rapid verification and validation of AVs. This white-box decision-making provides human-like behavior and a guaranteed prevention of deadlocks. An additional benefit is that the proposed decision-making requires low computational resources only enabling real world usage. The automated parameterization uses a particle swarm optimization and compares two different models of the pedestrian: The social force model and the Markov decision process model. Consequently, a rapid design of the decision-making is possible and different pedestrian behaviors can be taken into account. The results reinforce the applicability of the proposed intention-aware decision-making

Multi-Agent Reinforcement Learning for Connected and Automated Vehicles Control: Recent Advancements and Future Prospects

Connected and automated vehicles (CAVs) have emerged as a potential solution to the future challenges of developing safe, efficient, and eco-friendly transportation systems. However, CAV control presents significant challenges, given the complexity of interconnectivity and coordination required among the vehicles. To address this, multi-agent reinforcement learning (MARL), with its notable advancements in addressing complex problems in autonomous driving, robotics, and human-vehicle interaction, has emerged as a promising tool for enhancing the capabilities of CAVs. However, there is a notable absence of current reviews on the state-of-the-art MARL algorithms in the context of CAVs. Therefore, this paper delivers a comprehensive review of the application of MARL techniques within the field of CAV control. The paper begins by introducing MARL, followed by a detailed explanation of its unique advantages in addressing complex mobility and traffic scenarios that involve multiple agents. It then presents a comprehensive survey of MARL applications on the extent of control dimensions for CAVs, covering critical and typical scenarios such as platooning control, lane-changing, and unsignalized intersections. In addition, the paper provides a comprehensive review of the prominent simulation platforms used to create reliable environments for training in MARL. Lastly, the paper examines the current challenges associated with deploying MARL within CAV control and outlines potential solutions that can effectively overcome these issues. Through this review, the study highlights the tremendous potential of MARL to enhance the performance and collaboration of CAV control in terms of safety, travel efficiency, and economy

Pedestrian Models for Autonomous Driving Part II: High-Level Models of Human Behavior

Abstract—Autonomous vehicles (AVs) must share space with pedestrians, both in carriageway cases such as cars at pedestrian crossings and off-carriageway cases such as delivery vehicles navigating through crowds on pedestrianized high-streets. Unlike static obstacles, pedestrians are active agents with complex, inter- active motions. Planning AV actions in the presence of pedestrians thus requires modelling of their probable future behaviour as well as detecting and tracking them. This narrative review article is Part II of a pair, together surveying the current technology stack involved in this process, organising recent research into a hierarchical taxonomy ranging from low-level image detection to high-level psychological models, from the perspective of an AV designer. This self-contained Part II covers the higher levels of this stack, consisting of models of pedestrian behaviour, from prediction of individual pedestrians’ likely destinations and paths, to game-theoretic models of interactions between pedestrians and autonomous vehicles. This survey clearly shows that, although there are good models for optimal walking behaviour, high-level psychological and social modelling of pedestrian behaviour still remains an open research question that requires many conceptual issues to be clarified. Early work has been done on descriptive and qualitative models of behaviour, but much work is still needed to translate them into quantitative algorithms for practical AV control

보행자 거동 및 운전자 주행 특성 기반의 자율주행 종방향 거동 계획

학위논문 (석사) -- 서울대학교 대학원 : 공과대학 기계공학부, 2020. 8. 이경수.본 연구는 보행자의 미래 거동 방향에 대한 불확실성을 고려한 보행자 모델을 제안하고, 보행자 대응 시의 운전자 주행 특성을 반영하여 자율주행 차량의 종방향 모션을 계획하는 알고리즘을 제시한다. 도심 자율 주행을 가능하게 하기위해서는 보행자와의 상호적인 주행이 필수적이다. 그러나, 보행자는 거동 방향 전환이 쉽게 일어나기 때문에 미래 거동을 예측하기가 어렵고, 이에 대응하는 자차의 거동을 결정짓는 데도 어려움이 있다. 이러한 보행자의 거동 불확실성이 존재함에도 자율 주행 차량이 보행자의 안전성을 확보하고 휴먼 운전자와 같이 거동하기 위해서는, 보행자의 거동 불확실성을 반영하는 보행자 모델이 우선적으로 필요하다. 해당 연구에서는 보행자 거동 특성을 조사하여 보행자 거동 확률 모델을 정의하고, 보행자 대응 상황에서의 운전자의 거동을 조사하여 자율주행 차량의 종방향 거동 계획에 적용한다. 해당 논문은 크게 보행자 모델 정의, 예측 기반 충돌 위험 평가 그리고 보행자 대응 종방향 거동 계획의 세 가지 주요 파트로 이루어져 있다. 첫 번째 파트에서 보행자 모델 정의의 핵심 이론은 보행자의 거동 속도와 방향을 전환하는 거동 사이에는 특정 상관관계를 가지고 있다는 것이다. 보행자의 거동 특성은 자율 주행 차량에 부착된 라이다 센서와 전방 카메라를 통해 획득한 보행자 데이터를 통계적으로 분석한 결과로 도출되었다. 해당 데이터를 통해 속도에 따라 보행자가 모든 방향에 대해서 거동할 확률이 도출되고, 보행자의 미래 거동 범위는 도출된 확률 분포에서 유효 시그마 범위를 설정하여 구획된다. 이는 보행자가 일정 시간 동안 특정 확률로 거동할 영역을 고려하여, 위험이 존재할 수 있는 보행자에 대해서 미리 차량의 움직임을 계획할 수 있도록 한다. 두 번째 파트로 보행자와 자 차량의 일정 시간 동안의 위치 정보를 예측하여 충돌 위험성을 평가한다. 보행자 예측은 앞서 도출한 보행자 유효 예측 거동 범위 내에서 가장 위험성이 큰 방향으로 움직인다고 가정한다. 또한, 자 차량의 경우 주어진 로컬 경로를 따라 움직인다는 가정을 하는 차선 유지 모델을 사용한다. 예측 결과를 통해 현재 추가적인 감속도를 가하지 않았을 때, 충돌 위험이 존재하는지 확인한다. 마지막으로, 타겟이 되는 보행자에 대한 종방향 거동을 결정한다. 우선적으로 보행자 대응 상황에서 적절한 감속도와 감속 시점을 결정하기 위해 휴먼 운전자 주행 데이터를 분석한다. 이를 통해 주행에서 핵심적인 파라미터들이 정의되고, 해당 파라미터들은 종방향 거동 계획에 반영된다. 따라서 최종적으로 보행자 예측 거동 영역에 대해서 자율 주행 차량의 추종 가속도이 결정된다. 제시된 알고리즘은 실차 테스트를 통해 성능이 확인된다. 테스트 결과, 도출한 보행자 모델과 예측 모델을 바탕으로 한 감속 결정 시점과 감속도의 궤적이 동일 상황들에 대해서 능숙한 운전자와 유사함이 확인되었다.This paper presents a pedestrian model considering uncertainty in the direction of future movement and a human-like longitudinal motion planning algorithm for autonomous vehicle in the interaction situation with pedestrians. Interactive driving with pedestrians is essential for autonomous driving in urban environments. However, interaction with pedestrians is very challenging for autonomous vehicle because it is difficult to predict movement direction of pedestrians. Even if there exists uncertainty of the behavior of pedestrians, the autonomous vehicles should plan their motions ensuring pedestrian safety and respond smoothly to pedestrians. To implement this, a pedestrian probabilistic yaw model is introduced based on behavioral characteristics and the human driving parameters are investigated in the interaction situation. The paper consists of three main parts: the pedestrian model definition, collision risk assessment based on prediction and human-like longitudinal motion planning. In the first section, the main key of pedestrian model is the behavior tendency with correlation between pedestrians speed and direction change. The behavior characteristics are statistically investigated based on perceived pedestrian tracking data using light detection and ranging(Lidar) sensor and front camera. Through the behavior characteristics, movement probability for all directions of the pedestrian is derived according to pedestrians velocity. Also, the effective moving area can be limited up to the valid probability criterion. The defined model allows the autonomous vehicle to know the area that pedestrian may head to a certain probability in the future steps. This helps to plan the vehicle motion considering the pedestrian yaw states uncertainty and to predetermine the motion of autonomous vehicle from the pedestrians who may have a risk. Secondly, a risk assessment is required and is based on the pedestrian model. The dynamic states of pedestrians and subject vehicle are predicted to do a risk assessment. In this section, the pedestrian behavior is predicted under the assumption of moving to the most dangerous direction in the effective moving area obtained above. The prediction of vehicle behavior is performed using a lane keeping model in which the vehicle follows a given path. Based on the prediction result, it is checked whether there will be a collision between the pedestrian and the vehicle if deceleration motion is not taken. Finally, longitudinal motion planning is determined for target pedestrians with possibility of collision. Human driving data is first examined to obtain a proper longitudinal deceleration and deceleration starting point in the interaction situation with pedestrians. Several human driving parameters are defined and applied in determining the longitudinal acceleration of the vehicle. The longitudinal motion planning algorithm is verified via vehicle tests. The test results confirm that the proposed algorithm shows similar longitudinal motion and deceleration decision to a human driver based on predicted pedestrian model.Chapter 1. Introduction 1 1.1. Background and Motivation 1 1.2. Previous Researches 3 1.3. Thesis Objective and Outline 5 Chapter 2. Probabilistic Pedestrian Yaw Model 8 2.1. Pedestrian Behavior Characteristics 9 2.2. Probability Movement Range 11 Chapter 3. Prediction Based Risk Assessment 13 3.1. Lane Keeping Behavior Model 15 3.2. Subject Vehicle Prediction 17 3.3. Safety Region Based on Prediction 19 Chapter 4. Human-like Longitudinal Motion Planning 22 4.1. Human Driving Parameters Definition 22 4.1.1 Hard Mode Distance 23 4.1.2 Soft Mode Distance and Velocity 23 4.1.3 Time-To-Collision 23 4.2. Driving Mode and Acceleration Decision 25 4.2.1 Acceleration of Each Mode 25 4.2.2 Mode Selection 26 Chapter 5. Vehicle Test Result 28 5.1. Configuration of Experimental Vehicle 28 5.2. Longitudinal Motion Planning for Pedestiran 30 5.2.1 Soft Mode Scenario 32 5.2.2 Hard Mode Scenario 35 Chapter 6. Colclusion 38 Bibliography 39 국문 초록 42Maste