A survey on motion prediction and risk assessment for intelligent vehicles

International audienceWith the objective to improve road safety, the automotive industry is moving toward more “intelligent” vehicles. One of the major challenges is to detect dangerous situations and react accordingly in order to avoid or mitigate accidents. This requires predicting the likely evolution of the current traffic situation, and assessing how dangerous that future situation might be. This paper is a survey of existing methods for motion prediction and risk assessment for intelligent vehicles. The proposed classification is based on the semantics used to define motion and risk. We point out the tradeoff between model completeness and real-time constraints, and the fact that the choice of a risk assessment method is influenced by the selected motion model

Nachweislich sichere Bewegungsplanung für autonome Fahrzeuge durch Echtzeitverifikation

This thesis introduces fail-safe motion planning as the first approach to guarantee legal safety of autonomous vehicles in arbitrary traffic situations. The proposed safety layer verifies whether intended trajectories comply with legal safety and provides fail-safe trajectories when intended trajectories result in safety-critical situations. The presented results indicate that the use of fail-safe motion planning can drastically reduce the number of traffic accidents.Die vorliegende Arbeit führt ein neuartiges Verifikationsverfahren ein, mit dessen Hilfe zum ersten Mal die verkehrsregelkonforme Sicherheit von autonomen Fahrzeugen gewährleistet werden kann. Das Verifikationsverfahren überprüft, ob geplante Trajektorien sicher sind und generiert Rückfalltrajektorien falls diese zu einer unsicheren Situation führen. Die Ergebnisse zeigen, dass die Verwendung des Verfahrens zu einer deutlichen Reduktion von Verkehrsunfällen führt

Safety of autonomous vehicles: A survey on Model-based vs. AI-based approaches

The growing advancements in Autonomous Vehicles (AVs) have emphasized the critical need to prioritize the absolute safety of AV maneuvers, especially in dynamic and unpredictable environments or situations. This objective becomes even more challenging due to the uniqueness of every traffic situation/condition. To cope with all these very constrained and complex configurations, AVs must have appropriate control architectures with reliable and real-time Risk Assessment and Management Strategies (RAMS). These targeted RAMS must lead to reduce drastically the navigation risks. However, the lack of safety guarantees proves, which is one of the key challenges to be addressed, limit drastically the ambition to introduce more broadly AVs on our roads and restrict the use of AVs to very limited use cases. Therefore, the focus and the ambition of this paper is to survey research on autonomous vehicles while focusing on the important topic of safety guarantee of AVs. For this purpose, it is proposed to review research on relevant methods and concepts defining an overall control architecture for AVs, with an emphasis on the safety assessment and decision-making systems composing these architectures. Moreover, it is intended through this reviewing process to highlight researches that use either model-based methods or AI-based approaches. This is performed while emphasizing the strengths and weaknesses of each methodology and investigating the research that proposes a comprehensive multi-modal design that combines model-based and AI approaches. This paper ends with discussions on the methods used to guarantee the safety of AVs namely: safety verification techniques and the standardization/generalization of safety frameworks

Motion Planning for Autonomous Vehicles in Partially Observable Environments

Unsicherheiten, welche aus Sensorrauschen oder nicht beobachtbaren Manöverintentionen anderer Verkehrsteilnehmer resultieren, akkumulieren sich in der Datenverarbeitungskette eines autonomen Fahrzeugs und führen zu einer unvollständigen oder fehlinterpretierten Umfeldrepräsentation. Dadurch weisen Bewegungsplaner in vielen Fällen ein konservatives Verhalten auf. Diese Dissertation entwickelt zwei Bewegungsplaner, welche die Defizite der vorgelagerten Verarbeitungsmodule durch Ausnutzung der Reaktionsfähigkeit des Fahrzeugs kompensieren. Diese Arbeit präsentiert zuerst eine ausgiebige Analyse über die Ursachen und Klassifikation der Unsicherheiten und zeigt die Eigenschaften eines idealen Bewegungsplaners auf. Anschließend befasst sie sich mit der mathematischen Modellierung der Fahrziele sowie den Randbedingungen, welche die Sicherheit gewährleisten. Das resultierende Planungsproblem wird mit zwei unterschiedlichen Methoden in Echtzeit gelöst: Zuerst mit nichtlinearer Optimierung und danach, indem es als teilweise beobachtbarer Markov-Entscheidungsprozess (POMDP) formuliert und die Lösung mit Stichproben angenähert wird. Der auf nichtlinearer Optimierung basierende Planer betrachtet mehrere Manöveroptionen mit individuellen Auftrittswahrscheinlichkeiten und berechnet daraus ein Bewegungsprofil. Er garantiert Sicherheit, indem er die Realisierbarkeit einer zufallsbeschränkten Rückfalloption gewährleistet. Der Beitrag zum POMDP-Framework konzentriert sich auf die Verbesserung der Stichprobeneffizienz in der Monte-Carlo-Planung. Erstens werden Informationsbelohnungen definiert, welche die Stichproben zu Aktionen führen, die eine höhere Belohnung ergeben. Dabei wird die Auswahl der Stichproben für das reward-shaped Problem durch die Verwendung einer allgemeinen Heuristik verbessert. Zweitens wird die Kontinuität in der Reward-Struktur für die Aktionsauswahl ausgenutzt und dadurch signifikante Leistungsverbesserungen erzielt. Evaluierungen zeigen, dass mit diesen Planern große Erfolge in Fahrversuchen und Simulationsstudien mit komplexen Interaktionsmodellen erreicht werden

Criticality Metrics for Automated Driving: A Review and Suitability Analysis of the State of the Art

The large-scale deployment of automated vehicles on public roads has the potential to vastly change the transportation modalities of today's society. Although this pursuit has been initiated decades ago, there still exist open challenges in reliably ensuring that such vehicles operate safely in open contexts. While functional safety is a well-established concept, the question of measuring the behavioral safety of a vehicle remains subject to research. One way to both objectively and computationally analyze traffic conflicts is the development and utilization of so-called criticality metrics. Contemporary approaches have leveraged the potential of criticality metrics in various applications related to automated driving, e.g. for computationally assessing the dynamic risk or filtering large data sets to build scenario catalogs. As a prerequisite to systematically choose adequate criticality metrics for such applications, we extensively review the state of the art of criticality metrics, their properties, and their applications in the context of automated driving. Based on this review, we propose a suitability analysis as a methodical tool to be used by practitioners. Both the proposed method and the state of the art review can then be harnessed to select well-suited measurement tools that cover an application's requirements, as demonstrated by an exemplary execution of the analysis. Ultimately, efficient, valid, and reliable measurements of an automated vehicle's safety performance are a key requirement for demonstrating its trustworthiness

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