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

Human Motion Trajectory Prediction: A Survey

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page

Pedestrian Environment Model for Automated Driving

Besides interacting correctly with other vehicles, automated vehicles should also be able to react in a safe manner to vulnerable road users like pedestrians or cyclists. For a safe interaction between pedestrians and automated vehicles, the vehicle must be able to interpret the pedestrian's behavior. Common environment models do not contain information like body poses used to understand the pedestrian's intent. In this work, we propose an environment model that includes the position of the pedestrians as well as their pose information. We only use images from a monocular camera and the vehicle's localization data as input to our pedestrian environment model. We extract the skeletal information with a neural network human pose estimator from the image. Furthermore, we track the skeletons with a simple tracking algorithm based on the Hungarian algorithm and an ego-motion compensation. To obtain the 3D information of the position, we aggregate the data from consecutive frames in conjunction with the vehicle position. We demonstrate our pedestrian environment model on data generated with the CARLA simulator and the nuScenes dataset. Overall, we reach a relative position error of around 16% on both datasets.Comment: Accepted for presentation at the 26th IEEE International Conference on Intelligent Transportation Systems (ITSC 2023), 24-28 September 2023, Bilbao, Bizkaia, Spai

Tracking by Prediction: A Deep Generative Model for Mutli-Person localisation and Tracking

Current multi-person localisation and tracking systems have an over reliance on the use of appearance models for target re-identification and almost no approaches employ a complete deep learning solution for both objectives. We present a novel, complete deep learning framework for multi-person localisation and tracking. In this context we first introduce a light weight sequential Generative Adversarial Network architecture for person localisation, which overcomes issues related to occlusions and noisy detections, typically found in a multi person environment. In the proposed tracking framework we build upon recent advances in pedestrian trajectory prediction approaches and propose a novel data association scheme based on predicted trajectories. This removes the need for computationally expensive person re-identification systems based on appearance features and generates human like trajectories with minimal fragmentation. The proposed method is evaluated on multiple public benchmarks including both static and dynamic cameras and is capable of generating outstanding performance, especially among other recently proposed deep neural network based approaches.Comment: To appear in IEEE Winter Conference on Applications of Computer Vision (WACV), 201

Modelling shared space users via rule-based social force model

The promotion of space sharing in order to raise the quality of community living and safety of street surroundings is increasingly accepted feature of modern urban design. In this context, the development of a shared space simulation tool is essential in helping determine whether particular shared space schemes are suitable alternatives to traditional street layouts. A simulation tool that enables urban designers to visualise pedestrians and cars trajectories, extract flow and density relation in a new shared space design and achieve solutions for optimal design features before implementation. This paper presents a three-layered microscopic mathematical model which is capable of representing the behaviour of pedestrians and vehicles in shared space layouts and it is implemented in a traffic simulation tool. The top layer calculates route maps based on static obstacles in the environment. It plans the shortest path towards agents' respective destinations by generating one or more intermediate targets. In the second layer, the Social Force Model (SFM) is modified and extended for mixed traffic to produce feasible trajectories. Since vehicle movements are not as flexible as pedestrian movements, velocity angle constraints are included for vehicles. The conflicts described in the third layer are resolved by rule-based constraints for shared space users. An optimisation algorithm is applied to determine the interaction parameters of the force-based model for shared space users using empirical data. This new three-layer microscopic model can be used to simulate shared space environments and assess, for example, new street designs