Optimal motion control for collision avoidance at Left Turn Across Path/Opposite Direction intersection scenarios using electric propulsion

Collision avoidance at intersections involving a host vehicle turning left across the path of an oncoming vehicle (Left Turn Across Path/Opposite Direction or LTAP/OD) have been studied in the past, but mostly using simplified interventions and rarely considering the possibility of crossing the intersection ahead of a bullet vehicle. Such a scenario where the driver preference is to avoid a collision by crossing the intersection ahead of a bullet vehicle is considered in this work. The optimal vehicle motion for collision avoidance in this scenario is determined analytically using a particle model within an optimal control framework. The optimal manoeuvres are then verified through numerical optimisations using a two-track vehicle model, where it was seen that the wheel forces followed the analytical global force angle result independently of the other wheels. A Modified Hamiltonian Algorithm (MHA) controller for collision avoidance that uses the analytical optimal control solution is then implemented and tested in CarMaker simulations using a validated Volvo XC90 vehicle model. Simulation results showed that collision risk can be significantly reduced in this scenario using the proposed controller, and that more benefit can be expected in scenarios that require larger speed changes

교통약자 대상 강건 비상제동장치 개발

학위논문 (석사)-- 서울대학교 대학원 공과대학 기계항공공학부, 2017. 8. 이경수.본 연구는 교통약자를 대상으로 하는 자동비상제동 알고리즘을 개발하고자 진행된 연구이다. 자동비상제동장치란 센서로부터 얻은 환경정보를 기반으로 운전자가 예상하지 못한 사고를 회피하거나 사고의 피해를 완화할 수 있도록 차량을 제동해주는 장치이다. 이러한 자동비상제동장치가 점차 양산되고 보급되기 시작한 이후 사람들은 이러한 자동비상제동장치를 이용하여 교통 약자와 관련된 사고까지 예방하기 위한 노력들을 수행하고 있다. 교통 약자는 일반적으로 보행자, 자전거 등의 원동기를 장착하지 않은 도로 사용자로 정의된다. 교통 약자는 비록 그 속도가 차량에 비해 느리지만, 실제 사고가 발생할 경우 그 피해가 커질 우려가 있다. 따라서 이러한 교통 약자와 관련된 사고를 줄이기 위한 노력이 필요하다. 사고가 발생하기 이전에 위험을 인지하기 위해서는 자차량 및 대상 교통 약자의 거동을 예측할 필요가 있다. 이를 위해서는 자차량 및 교통 약자의 거동을 모사할 수 있는 동역학 모델이 필요하다. 차량의 경우 운전자가 사고를 회피할 수 있는지 확인하기 위해서는 실제로 운전자가 사고를 회피할 때 일반적으로 사용하는 회피 거동에 대한 모사 역시 필요하다. 이를 위하여 자차량의 거동은 등가속도 모델을 이용하여 표현하였다. 또한 교통 약자의 경우 보행자와 자전거를 구분하는데 한계가 있기 때문에 대상 교통 약자의 종류 구분 없이 안전 성능을 확보할 수 있어야 한다. 따라서 보행자 및 자전거의 거동은 동일한 등속 직선 운동 모델을 이용하여 표현하고자 하였다. 이렇게 예측된 정보들을 바탕으로 운전자가 사고를 회피할 수 있는지 판단하고자 하였다. 만약 운전자가 사고를 회피하고자 할 때 일정 수준의 안전거리를 확보하지 못할 경우 자동비상제동장치가 작동하여 차량을 제동하도록 하였다. 이 때 자동비상제동장치의 강건 성능을 확보하기 위하여 측정 시에 발생하는 불확실성 및 정보 예측 시에 발생하는 불확실성을 고려하여 안전 거리를 정의하였다. 이렇게 개발된 자동비상제동장치의 성능을 확인하기 위하여 차량 시뮬레이션 툴인 Carsim과 MATLAB/Simulink를 기반으로 시뮬레이션 평가를 수행하였다. 이 때 개발한 자동비상제동장치의 강건 성능을 검증하기 위하여 시뮬레이션을 동일 시나리오에 대해 100회 반복 수행 하였으며, 비교를 위하여 불확실성을 고려하지 않은 자동비상제동장치를 함께 평가하였다.A robust autonomous emergency braking (AEB) algorithm for vulnerable road users (VRU) is studied. Autonomous emergency braking (AEB) is a system which helps driver to avoid or mitigate a collision using sensor information. After many kinds of AEB system is produced by automakers, researchers and automakers are currently focusing on VRU-related collisions. Vulnerable road users (VRU) usually defined as non-motorized road users such as pedestrian and cyclist. Although VRU are relatively slower than vehicle, VRU related collisions should be prevented due to their fatalities. Therefore, many researchers are trying to develop a VRU-AEB. In order to assess the risk of collision before it occurs, the motion of host vehicle and target VRU should be predicted. For this, dynamic models of host vehicle and target VRU is required. In the case of host vehicle, in order to judge whether a driver can avoid a collision or not, drivers evasive maneuver also should be predicted as well as normal driving maneuver. For this, the motion of the host vehicle is predicted using constant acceleration model. In the case of target VRU, since the identification between pedestrian and cyclist is difficult, safety performance of AEB should be guaranteed even if the type of the target is unclear. Therefore, the behavior of pedestrian and cyclist is described using a single constant velocity model. These predicted information is then used to judge whether a collision is inevitable or not. If a driver cannot avoid a collision with pre-defined limits and safety margin, then the proposed AEB system is activated to decelerate the vehicle. To guarantee the robust safety performance of AEB system, measurement uncertainty and prediction uncertainty are also considered while defining the safety margin. To evaluate the safety performance of proposed AEB system, simulation study is conducted via vehicle simulation tool Carsim and MATLAB/Simulink. To investigate the robust safety performance of the proposed AEB system, simulation study is repeated 100 times with same traffic scenario with uncertainties. Performance of the proposed AEB system is compared with the deterministic AEB which is introduced in this work.Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Autonomous Emergency Braking System – Global Trend 4 1.3 Thesis Objectives and Outline 9 Chapter 2 Previous Researches 10 Chapter 3 Autonomous Emergency Braking Algorithm for Vulnerable Road Users 17 Chapter 4 Host Vehicle Motion Prediction 19 4.1 Host Vehicle State Estimation 20 4.2 Host Vehicle Evasive Maneuver Prediction 24 Chapter 5 Target VRU Motion Prediction 28 5.1 Target VRU State Estimation 29 5.2 Target VRU Motion Prediction 34 Chapter 6 Threat Assessment 35 6.1 Collision Judgement 35 6.2 Safety Boundary for Collision Judgement 39 6.3 Emergency Braking Mode Decision 42 Chapter 7 Simulation Result 43 Chapter 8 Conclusion 50 Bibliography 51 국문초록 59Maste

二分決定図と空間行動粒度に基づくローカルダイナミックマップを実装可能にする手法に関する研究

Autonomous vehicles (AVs) have been increasing rapidly on the road in recent years. However, the safety of AVs is of significant concern, which we must ensure. AVs use sensor information to achieve autonomy, but sensors such as cameras and lidar have limitations, and vehicles cannot rely on them entirely for safe navigation. To assist AVs with static information, high-definition maps (HD maps) can facilitate the complex static details of the surrounding for safe autonomy. However, we can model complex static information using HD maps for navigation; detecting and maintaining the traffic participant’s dynamic information using sensors of the ego vehicle alone is still a significant concern for safe navigation. In such a situation of sensing limitations, Cooperative Intelligent Transport Systems (C-ITS) is one approach to facilitate vehicle navigation through sharing information between the traffic participants. The C-ITS approach has various Intelligent transportation system (ITS) station units, namely Personal, Vehicle, Road-side and Central ITS station units. A Local Dynamic Map (LDM) is a critical component in any ITS station’s facilities layer. LDM is one way to maintain static and dynamic information of the traffic participants in a consistent geometrical way. It is a necessary facility in C-ITS to share sensor information between participating traffic agents. Moreover, it maintains information about the objects that are either part of the traffic or influenced by it. The International Organization for Standardization (ISO) and European Telecommunications Standards Institute (ETSI) have also made standardization efforts. Since its inception in the SAFESPOT project, implementations of LDM have been mostly four-layer data organizations. Where Layer 1 and Layer 2 maintain static information and transient static information. Then, Layer 3 and Layer 4 contain transient dynamic and highly dynamic data. Depending upon the requirement, the LDM community realized memory-based or database-based LDM. We utilized the decision diagram to enhance the safety aspect of the traffic participants in the memory/ database-based LDM setup. We utilized Shared Binary Decision Diagram (SBDD) and Geohash granular properties to detect the near-miss situation, i.e. when vehicles come very close. However, besides DynaMap, there is also a common understanding since the SAFESPOT project introduced LDM to use the database and supported query language to retrieve data from the LDM. Hence, most implementations use different databases and query languages to execute it. Although, the LDM community has explored LDM depending on the database variants. Nevertheless, remarkably less emphasis has been given to the type of data stored in the LDM. This thesis attempted to fill this gap in the LDM to enhance the moving vehicle’s safety aspect. We proposed a novel method of data representation for vehicle future geographical occupancy information using a binary decision diagram (BDD). We show that sharing BDD-based information is consistent with the C-ITS nature of the data sharing since the algebraic operation between the exchanged BDDs can confirm the possibility of future interaction. We calculated potential future occupancy using Kamm’s circle, shown in the ROS-based simulator and modified the mid-point circle generation algorithm to find the BDD representing the set of Geohash enclosing the Kamm’s circle. We also reported data insertion and collision avoidance check time of the linked list-based BDD on PostgreSQL database-based LDM.九州工業大学博士学位論文 学位記番号：生工博甲第449号 学位授与年月日：令和4年9月26日1 Introduction|2 Literature Review|3 Methodology|4 Results|5 Discussion|6 Summary九州工業大学令和4年

Model Predictive Control of Highway Emergency Maneuvering and Collision Avoidance

Autonomous emergency maneuvering (AEM) is an active safety system that automates safe maneuvers to avoid imminent collision, particularly in highway driving situations. Uncertainty about the surrounding vehicles’ decisions and also about the road condition, which has significant effects on the vehicle’s maneuverability, makes it challenging to implement the AEM strategy in practice. With the rise of vehicular networks and connected vehicles, vehicles would be able to share their perception and also intentions with other cars. Therefore, cooperative AEM can incor- porate surrounding vehicles’ decisions and perceptions in order to improve vehicles’ predictions and estimations and thereby provide better decisions for emergency maneuvering. In this thesis, we develop an adaptive, cooperative motion planning scheme for emergency maneuvering, based on the model predictive control (MPC) approach, for vehicles within a ve- hicular network. The proposed emergency maneuver planning scheme finds the best combination of longitudinal and lateral maneuvers to avoid imminent collision with surrounding vehicles and obstacles. To implement real-time MPC for the non-convex problem of collision free motion planning, safety constraints are suggested to be convexified based on the road geometry. To take advantage of vehicular communication, the surrounding vehicles’ decisions are incorporated in the prediction model to improve the motion planning results. The MPC approach is prone to loss of feasibility due to the limited prediction horizon for decision-making. For the autonomous vehicle motion planning problem, many of detected ob- stacles, which are beyond the prediction horizon, cannot be considered in the instantaneous de- cisions, and late consideration of them may cause infeasibility. The conditions that guarantee persistent feasibility of a model predictive motion planning scheme are studied in this thesis. Maintaining the system’s states in a control invariant set of the system guarantees the persis- tent feasibility of the corresponding MPC scheme. Specifically, we present two approaches to compute control invariant sets of the motion planning problem; the linearized convexified ap- proach and the brute-force approach. The resulting computed control invariant sets of these two approaches are compared with each other to demonstrate the performance of the proposed algorithm. Time-variation of the road condition affects the vehicle dynamics and constraints. Therefore, it necessitates the on-line identification of the road friction parameter and implementation of an adaptive emergency maneuver motion planning scheme. In this thesis, we investigate coopera- tive road condition estimation in order to improve collision avoidance performance of the AEM system. Each vehicle estimates the road condition individually, and disseminates it through the vehicular network. Accordingly, a consensus estimation algorithm fuses the individual estimates to find the maximum likelihood estimate of the road condition parameter. The performance of the proposed cooperative road condition estimation has been validated through simulations

Trajektorienplanung zur Kollisionsvermeidung im Straßenverkehr

In kritischen Situationen sind viele Fahrer von PKWs mit der Fahrzeugführungsaufgabe überfordert. Die Unfallzahlen konnten bis 2013 auch durch die Einführung von aktiven Fahrerassistenzsystemen wie ABS, ASR und ESC gesenkt werden. In den folgenden Jahren ist ein leichter Anstieg zu verzeichnen. Um die Unfallzahlen wieder zu senken, werden neue Fahrerassistenzsysteme benötigt, die neben fahrdynamischen Größen auch Informationen über das Fahrzeugumfeld miteinbeziehen. Dies kann durch assistierende Funktionen, welche der Fahrer im Fehlerfall übersteuern kann, und/oder durch automatisierte Fahrfunktionen realisiert werden. Die Arbeit beschreibt und vergleicht vier verschiedene Verfahren zur Fahrzeugführung, die zur Kollisionsvermeidung im Straßenverkehr eingesetzt werden können. Das Bahnfolgeverfahren verwendet eine analytische Funktion zur Beschreibung der Ausweichbahn und eine Folgeregelung zur Führung des Fahrzeugs entlang der Bahn. Es ist ein einfaches Konzept, welches mit wenig Rechenleistung auskommt, sich aber nicht an viele verschiedene Situationen anpassen lässt. Deshalb wird das Online-Trajektorienoptimierungsverfahren entwickelt. Zur Berechnung der Ausweichtrajektorien wird ein Gütemaß minimiert, welches Anteile zur Kollisionsvermeidung und zur Minimierung fahrdynamischer Reaktionen enthält. Die Realisierung der fortlaufend neu geplanten Trajektorie wird mit einer unterlagerten Geschwindigkeits- und Kurswinkelregelung durchgeführt. Das modellprädiktive Planungs- und Regelungsverfahren löst analog zum Online-Trajektorienoptimierungsverfahrens in jedem Abtastschritt ein Optimierungsproblem. Die kollisionsfreie Trajektorie wird zusätzlich an die Dynamikgleichungen eines Einspurmodells angepasst. Das Optimierungsproblem ist daher ein Optimalsteuerungsproblem, dessen Lösung neben der optimalen Trajektorie auch die zugehörigen Stellgrößen enthält. Die bisher getrennt behandelten Probleme, Trajektorienplanung und Folgeregelung, werden also in einem Schritt gelöst. Der Nachteil dieses Verfahrens ist der nochmals höhere Rechenaufwand im Vergleich zum Online-Trajektorienoptimierungsverfahren. Durch die Beschränkung auf konstante Stellgrößen während der Prädiktion und eine grobe Stellgrößendiskretisierung weist das modellprädiktive Trajektorienscharverfahren eine deutlich niedrigere Rechenlast auf. Die Vorteile der modellprädiktiven simultanen Planung und Regelung bleiben erhalten, jedoch können auf Grund des kurzen Prädiktionshorizontes weiter entfernte Hindernisse nicht in der Planung berücksichtigt werden. Durch die adaptive Wahl der Diskretisierung wird auch im stationären Zustand eine hohe Regelungsgüte erreicht. Der abschließende Vergleich durch eine Nutzwertanalyse zeigt, dass die vier Verfahren, in Abhängigkeit des Anwendungsfalles, unterschiedlich gut geeignet sind