차량간 통신을 이용한 지능형 자동차의 전방차량 위험판단 기법

학위논문 (박사)-- 서울대학교 대학원 공과대학 기계항공공학부, 2017. 8. 이경수.In recent years, advanced driver assistance systems or highly automated driving systems are expected to enhance road traffic safety, transport efficiency, and driver comfort. Practical applications have become possible due to recent advances in vehicle local sensors and inter vehicle communications. These advances have opened up many possibilities for active safety systems to be more intelligent and robust. The further enhancement of these technologies can be utilized as a risk assessment system of automated drive. This dissertation presents a risk assessment for improved vehicle safety using Front Vehicle Dynamic States through vehicle-to-vehicle wireless communication. A vehicle-to-vehicle wireless communication (V2V communication) has been implemented and fused with a radar sensor to obtain the prediction of remote vehicles motion. Based on the predicted behavior of remote vehicles, a collision risk and a human reaction time are determined for a better driver acceptance and active safety control intervention. A human-centered risk assessment using the V2V communication has been incorporated into a collision avoidance algorithm to monitor threat vehicles ahead and to find the best intervention point. The performance of the proposed algorithm has been investigated via computer simulations and vehicle tests for application to urban and highway driving situation. It has been shown from both simulations and vehicle tests that the proposed integrated risk assessment algorithm with the V2V communication can be beneficial to active safety systems in decision of controller intervention moment and in control of automated drive for the guaranteed safety.Chapter 1 Introduction 1 1.1 Background and Motivations 1 1.2 Previous Researches 5 1.3 Thesis Objectives 9 1.4 Thesis Outline 11 Chapter 2 Vehicular Communication 12 2.1. Literature Review 14 2.1.1 An Empirical Model for V2V communication 14 2.1.2 Position based Sampling and Distance based Interpolation 17 2.2. Communication Delay and Packet Loss Ratio 21 2.2.1 Compensation of V2V Communication Delay 21 Chapter 3 Human Factor Considerations 27 3.1. Driver Acceptance 30 3.1.1 Driver inattention and distraction 31 3.1.2 Mode Confusion 31 3.1.3 Motion Sickness 32 3.2. Sight Distance 33 3.2.1 Stopping Sight Distance 35 3.2.2 Decision Sight Distance 35 Chapter 4 Human-Centered Risk Assessment using Vehicular Wireless Communication 37 4.1. Human-Centered Design 41 4.2. Convergence 43 4.2.1. Sensor-Based Solutions 44 4.2.2. The Benefits to Convergence 45 4.2.3. V2V/Radar Information Fusion 45 4.3. Related Work 46 4.3.1. Radar Sensing Characteristics 47 4.3.2. Probabilistic Threat Assessment 50 4.3.3. Human-Centered Vehicle Control 52 4.3.4. High-Level Information Fusion 54 4.3.5. Target Vehicle State Estimation Performance 58 4.4 Remote Vehicle States Prediction 64 4.5. Collision Risk Analysis 67 4.6. Predicted Collision Distance 70 4.7. Active Safety Intervention Moment Decision 72 Chapter 5 Performance Evaluations 77 5.1. Simulations: MPC based Automated Vehicle Control 78 5.1.1. Effects of V2V Communication on the Controller 78 5.2. Simulations : Human-Centered Risk Assessment 84 5.2.1. Scenarios 84 5.2.2. Effects of V2V Communication: Host vehicle perception only 86 5.2.3. Effects of V2V Communication: Controlled host vehicle 90 5.3. Vehicle Tests 94 5.3.1. Test Vehicle Configuration and Scenario 94 5.3.2. Implementation and Evaluation 96 Chapter 6 Conclusion 99 Bibliography 100 국문초록 110Docto

통합 예측 위험 관리 기반 포텐셜 필드 기법을 이용한 자율 주행 제어 알고리즘 개발

학위논문 (박사)-- 서울대학교 대학원 : 기계항공공학부, 2016. 2. 이경수.In recent years, global passenger vehicle sales exceed 60milion units per year. With the increasing number of vehicles on the road, safety has become a focal issue. In order to deal with the safety issue, a number of active safety systems have been developed in passenger vehicles, such as brae assist system (BAS), adaptive cruise control(ACC), lane keeping control(LKS), and collision mitigation(CM). The functionalities of the systems include the assistance in recognizing hazards on roadway e.g. forward vehicles, obstacles, the unexpected lane departure. Beyond the development of each independent safety system, the integrated safety system has been considered nowadays. This dissertation describes design, real-time implementation and test of a fully automated driving algorithm for automated driving in complex urban scenarios and motorways with a satisfactory safety level. The proposed algorithm consists of the following three steps: surround recognition, motion planning, and vehicle control. A full recognition of environment is achieved by data fusion and data interpretation based on the dynamic measurements from the environmental sensors. The recognition of vehicle state including longitudinal, lateral velocity, and position, and driving environment is transformed into a risk potential representation based on probabilistic prediction. The surround recognition system consists of three main modules: object classification, vehicle/non-vehicle tracking and map/lane-based localization. All system modules utilize information from surround sensors close to market such as vision sensors, radars and vehicle sensors. The objective of the motion planning module is to derive an optimal trajectory as a function of time and the surround recognition results. A safety envelope is represented as a complete driving corridor that leads to destination while making sure all objects are either on outside of the left or right corridor bounds. In the case of moving objects such as other traffic participants, their behaviors are anticipated within specific time horizon. The optimal trajectory planning uses the safety envelope as a constraint and computes a trajectory that the vehicle stays in its safe bounds considering drivers pattern and characteristics based on predicted risk potential method. The performance of the proposed algorithm has been verified via computer simulations and vehicle test. From the simulation and vehicle test results, it has been shown that the proposed automated driving control algorithm enhances safety with respect to the potential risk considering driver acceptability.Chapter 1 Introduction 1 1.1 Background and Motivation 1 1.2 Previous Researches 4 1.3 Thesis Objectives 7 1.4 Thesis Outline 9 Chapter 2 Integrated Perception Algorithm 12 2.1 Vehicle Velocity Estimation 15 2.1.1 Longitudinal Velocity Estimation 16 2.1.2 Lateral Velocity Estimation 23 A. Vertical Force Estimation 24 B. Reference Tire Model 25 C. Lateral Velocity Estimation 28 2.2 Perception of Dynamic Driving Environment 33 2.2.1 Vehicle State Prediction 34 2.2.2 Probabilistic Risk Assessment 38 Chapter 3 Development of Integrated Safety Control Algorithm 40 3.1 Integrated Risk Representation 42 3.1.1 Longitudinal and Lateral Collision Risk Indices 44 A. Longitudinal Collision Risk Indices 45 B. Lateral Collision Risk Indices 50 3.1.2 Dynamic Drivable Area Determination via Probabilistic Prediction 56 A. Initial Driving Corridor Decision 56 B. Moving Object Tracking and Prediction 61 C. Dynamic Drivable Area Decision 66 3.2 Desired Motion Determination for Safety Control 70 3.2.1 Potential Field Representation 71 3.2.2 Vehicle Motion Control based on Predictive Risk Potential Energy Function 74 3.2.3 Dynamic Constraints 79 A. Dynamic Constraints of Longitudinal Dynamics 80 B. Dynamic Constraints for lateral stability 81 Chapter 4 Evaluation 86 4.1 Performance Evaluation via Simulation with Multi-traffic Driving Environment 87 4.2 Performance Evaluation via Test Vehicle 91 4.2.1 Test Vehicle Configuration 92 4.2.2 Vehicle Tests 93 Chapter 5 Conclusions and Future Works 103 Bibliography 106 국문초록 113Docto

Implicit personalization in driving assistance: State-of-the-art and open issues

In recent decades, driving assistance systems have been evolving towards personalization for adapting to different drivers. With the consideration of driving preferences and driver characteristics, these systems become more acceptable and trustworthy. This article presents a survey on recent advances in implicit personalized driving assistance. We classify the collection of work into three main categories: 1) personalized Safe Driving Systems (SDS), 2) personalized Driver Monitoring Systems (DMS), and 3) personalized In-vehicle Information Systems (IVIS). For each category, we provide a comprehensive review of current applications and related techniques along with the discussion of industry status, benefits of personalization, application prospects, and future focal points. Both relevant driving datasets and open issues about personalized driving assistance are discussed to facilitate future research. By creating an organized categorization of the field, we hope that this survey could not only support future research and the development of new technologies for personalized driving assistance but also facilitate the application of these techniques within the driving automation community</h2

Real time implementation of socially acceptable collision avoidance of a low speed autonomous shuttle using the elastic band method

This paper presents the real time implementation of socially acceptable collision avoidance using the elastic band method for low speed autonomous shuttles operating in high pedestrian density environments. The modeling and validation of the research autonomous vehicle used in the experimental implementation is presented first, followed by the details of the Hardware-In-the-Loop connected and autonomous vehicle simulator used. The socially acceptable collision avoidance algorithm is formulated using the elastic band method as an online, local path modification algorithm. Parameter space based robust feedback plus feedforward steering controller design is used. Model-in-the-loop, Hardware-In-the-Loop and road testing in a proving ground are used to demonstrate the effectiveness of the real time implementation of the elastic band based socially acceptable collision avoidance method of this paper

MULTI SENSOR DATA FUSION FOR AUTONOMOUS VEHICLES

Multi sensor Data Fusion for Advanced Driver Assistance Systems (ADAS) in Automotive industry has gained a lot of attention lately with the advent of self-driving vehicles and road traffic safety applications. In order to achieve an efficient ADAS, accurate scene object perception in the vicinity of sensor field-of-view (FOV) is vital. It is not only important to know where the objects are, but also the necessity is to predict the object’s behavior in future time space for avoiding the fatalities on the road. The major challenges in multi sensor data fusion (MSDF) arise due to sensor errors, multiple occluding targets and changing weather conditions. Thus, In this thesis to address some of the challenges a novel cooperative fusion architecture is proposed for road obstacle detection. Also, an architecture for multi target tracking is designed with robust track management. In order to evaluate the proposed tracker’s performance with different fusion paradigms, a discrete event simulation model is proposed. Experiments and evaluation of the above mentioned methods in real time and simulated data proves the robustness of the techniques considered for data fusion

Development and evaluation of low cost 2-d lidar based traffic data collection methods

Traffic data collection is one of the essential components of a transportation planning exercise. Granular traffic data such as volume count, vehicle classification, speed measurement, and occupancy, allows managing transportation systems more effectively. For effective traffic operation and management, authorities require deploying many sensors across the network. Moreover, the ascending efforts to achieve smart transportation aspects put immense pressure on planning authorities to deploy more sensors to cover an extensive network. This research focuses on the development and evaluation of inexpensive data collection methodology by using two-dimensional (2-D) Light Detection and Ranging (LiDAR) technology. LiDAR is adopted since it is economical and easily accessible technology. Moreover, its 360-degree visibility and accurate distance information make it more reliable. To collect traffic count data, the proposed method integrates a Continuous Wavelet Transform (CWT), and Support Vector Machine (SVM) into a single framework. Proof-of-Concept (POC) test is conducted in three different places in Newark, New Jersey to examine the performance of the proposed method. The POC test results demonstrate that the proposed method achieves acceptable performances, resulting in 83% ~ 94% accuracy. It is discovered that the proposed method\u27s accuracy is affected by the color of the exterior surface of a vehicle since some colored surfaces do not produce enough reflective rays. It is noticed that the blue and black colors are less reflective, while white-colored surfaces produce high reflective rays. A methodology is proposed that comprises K-means clustering, inverse sensor model, and Kalman filter to obtain trajectories of the vehicles at the intersections. The primary purpose of vehicle detection and tracking is to obtain the turning movement counts at an intersection. A K-means clustering is an unsupervised machine learning technique that clusters the data into different groups by analyzing the smallest mean of a data point from the centroid. The ultimate objective of applying K-mean clustering is to identify the difference between pedestrians and vehicles. An inverse sensor model is a state model of occupancy grid mapping that localizes the detected vehicles on the grid map. A constant velocity model based Kalman filter is defined to track the trajectory of the vehicles. The data are collected from two intersections located in Newark, New Jersey, to study the accuracy of the proposed method. The results show that the proposed method has an average accuracy of 83.75%. Furthermore, the obtained R-squared value for localization of the vehicles on the grid map is ranging between 0.87 to 0.89. Furthermore, a primary cost comparison is made to study the cost efficiency of the developed methodology. The cost comparison shows that the proposed methodology based on 2-D LiDAR technology can achieve acceptable accuracy at a low price and be considered a smart city concept to conduct extensive scale data collection

A Review of Model Predictive Controls Applied to Advanced Driver-Assistance Systems

Advanced Driver-Assistance Systems (ADASs) are currently gaining particular attention in the automotive field, as enablers for vehicle energy consumption, safety, and comfort enhancement. Compelling evidence is in fact provided by the variety of related studies that are to be found in the literature. Moreover, considering the actual technology readiness, larger opportunities might stem from the combination of ADASs and vehicle connectivity. Nevertheless, the definition of a suitable control system is not often trivial, especially when dealing with multiple-objective problems and dynamics complexity. In this scenario, even though diverse strategies are possible (e.g., Equivalent Consumption Minimization Strategy, Rule-based strategy, etc.), the Model Predictive Control (MPC) turned out to be among the most effective ones in fulfilling the aforementioned tasks. Hence, the proposed study is meant to produce a comprehensive review of MPCs applied to scenarios where ADASs are exploited and aims at providing the guidelines to select the appropriate strategy. More precisely, particular attention is paid to the prediction phase, the objective function formulation and the constraints. Subsequently, the interest is shifted to the combination of ADASs and vehicle connectivity to assess for how such information is handled by the MPC. The main results from the literature are presented and discussed, along with the integration of MPC in the optimal management of higher level connection and automation. Current gaps and challenges are addressed to, so as to possibly provide hints on future developments

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

Perception architecture exploration for automotive cyber-physical systems

2022 Spring.Includes bibliographical references.In emerging autonomous and semi-autonomous vehicles, accurate environmental perception by automotive cyber physical platforms are critical for achieving safety and driving performance goals. An efficient perception solution capable of high fidelity environment modeling can improve Advanced Driver Assistance System (ADAS) performance and reduce the number of lives lost to traffic accidents as a result of human driving errors. Enabling robust perception for vehicles with ADAS requires solving multiple complex problems related to the selection and placement of sensors, object detection, and sensor fusion. Current methods address these problems in isolation, which leads to inefficient solutions. For instance, there is an inherent accuracy versus latency trade-off between one stage and two stage object detectors which makes selecting an enhanced object detector from a diverse range of choices difficult. Further, even if a perception architecture was equipped with an ideal object detector performing high accuracy and low latency inference, the relative position and orientation of selected sensors (e.g., cameras, radars, lidars) determine whether static or dynamic targets are inside the field of view of each sensor or in the combined field of view of the sensor configuration. If the combined field of view is too small or contains redundant overlap between individual sensors, important events and obstacles can go undetected. Conversely, if the combined field of view is too large, the number of false positive detections will be high in real time and appropriate sensor fusion algorithms are required for filtering. Sensor fusion algorithms also enable tracking of non-ego vehicles in situations where traffic is highly dynamic or there are many obstacles on the road. Position and velocity estimation using sensor fusion algorithms have a lower margin for error when trajectories of other vehicles in traffic are in the vicinity of the ego vehicle, as incorrect measurement can cause accidents. Due to the various complex inter-dependencies between design decisions, constraints and optimization goals a framework capable of synthesizing perception solutions for automotive cyber physical platforms is not trivial. We present a novel perception architecture exploration framework for automotive cyber- physical platforms capable of global co-optimization of deep learning and sensing infrastructure. The framework is capable of exploring the synthesis of heterogeneous sensor configurations towards achieving vehicle autonomy goals. As our first contribution, we propose a novel optimization framework called VESPA that explores the design space of sensor placement locations and orientations to find the optimal sensor configuration for a vehicle. We demonstrate how our framework can obtain optimal sensor configurations for heterogeneous sensors deployed across two contemporary real vehicles. We then utilize VESPA to create a comprehensive perception architecture synthesis framework called PASTA. This framework enables robust perception for vehicles with ADAS requiring solutions to multiple complex problems related not only to the selection and placement of sensors but also object detection, and sensor fusion as well. Experimental results with the Audi-TT and BMW Minicooper vehicles show how PASTA can intelligently traverse the perception design space to find robust, vehicle-specific solutions