Probabilistic Threat Assessment and Driver Modeling in Collision Avoidance Systems

This paper presents a probabilistic framework for decision-making in collision avoidance systems, targeting all types of collision scenarios with all types of single road users and objects. Decisions on when and how to assist the driver are made by taking a Bayesian approach to estimate how a collision can be avoided by an autonomous brake intervention, and the probability that the driver will consider the intervention as motivated. The driver model makes it possible to initiate earlier braking when it is estimated that the driver acceptance for interventions is high. The framework and the proposed driver model are evaluated in several scenarios, using authentic tracker data and a differential GPS. It is shown that the driver model can increase the benefit of collision avoidance systems — particularly in traffic situations where the future trajectory of another road user is hard for the driver to predict, e.g. when a playing child enters the roadway

Probabilistic Decision Making for Collision Avoidance Systems: Postponing Decisions

International audienceFor collision avoidance systems to be accepted by human drivers, it is important to keep the rate of unnecessary interventions very low. This is challenging since the decision to intervene or not is based on incomplete and uncertain information. The contribution of this paper is a decision making strategy for collision avoidance systems which allows the system to occasionally postpone a decision in order to collect more information. The problem is formulated in the framework of statistical decision theory, and the core of the algorithm is to run a preposterior analysis to estimate the benefit of deciding with the additional information. A final decision is made by comparing this benefit with the cost of delaying the intervention. The proposed approach is evaluated in simulation at a two-way stop road intersection for stop sign violation scenarios. The results show that the ability to postpone decisions leads to a significant reduction of false alarms and does not impair the ability of the collision avoidance system to prevent accidents

Making overtaking cyclists safer: Driver intention models in threat assessment and decision-making of advanced driver assistance system

Introduction: The number of cyclist fatalities makes up 3% of all fatalities globally and 7.8% in the European Union. Cars overtaking cyclists on rural roads are complex situations. Miscommunication and misunderstandings between road users may lead to crashes and severe injuries, particularly to cyclists, due to lack of protection. When making a car overtaking a cyclist safer, it is important to understand the interaction between road users and use in the development of an Advanced Driver Assistance System (ADAS). Methods: First, a literature review was carried out on driver and interaction modeling. A Unified Modeling Language (UML) framework was introduced to operationalize the interaction definition to be used in the development of ADAS. Second, the threat assessment and decision-making algorithm were developed that included the driver intention model. The counterfactual simulation was carried out on artificial crash data and field data to understand the intention-based ADAS\u27s performance and crash avoidance compared to a conventional system. The method focused on cars overtaking cyclists when an oncoming vehicle was present. Results: An operationalized definition of interaction was proposed to highlight the interaction between road users. The framework proposed uses UML diagrams to include interaction in the existing driver modeling approaches. The intention-based ADAS results showed that using the intention model, earlier warning or emergency braking intervention can be activated to avoid a potential rear-end collision with a cyclist without increasing more false activations than a conventional system. Conclusion: The approach used to integrate the driver intention model in developing an intention-based ADAS can improve the system\u27s effectiveness without compromising its acceptance. The intention-based ADAS has implications towards reducing worldwide road fatalities and in achieving sustainable development goals and car assessment program

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

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

Overview of Performance Based Practical Design

State transportation agencies (STAs) have increasingly turned to practical design and performance based practical design (PBPD) to inform project development and implementation — and to reduce project costs while optimizing systemwide benefits. PBPD is a design-up philosophy that encourages agencies to formulate projects to meet the purpose and need rather than adhering to ostensibly immutable design standards. This paper reviews practical design and PBPD concepts and initiatives and their application in a variety of contexts. It also summarizes best practices STAs can use to develop a PBPD program. As a holistic approach to project design, PBPD underscores context sensitive solutions that balance the needs of all roadway users, including motorists, bicyclists, and pedestrians. Common PBPD solutions include opting for low-cost enhancements, such as striping, signing, and rumble strips, as opposed to realignment; narrowing shoulder widths; redesigning projects to lower right-of-way costs; modifying interchange designs; and using design exceptions to build projects that fulfill project objectives. STAs committed to establishing robust PBPD programs will typically require 18 to 24 months to get a program off the ground. For an initiative to succeed, it is critical for executive leadership in an agency to advocate for PBPD; that agency staff learn about practical design and ongoing PBPD programs in other states; that a baseline performance evaluation of the tools, concepts, and resources currently used for project development be conducted; and that changes made to the project development process are thoroughly documented. The report closes with a series of recommended performance metrics the Kentucky Transportation Cabinet should consider adopting to improve its monitoring of critical bridge and roadway assets

A Data Fusion Approach to Automated Decision Making in Intelligent Vehicles

The goal of an intelligent transportation system is to increase safety, convenience and efficiency in driving. Besides these obvious advantages, the integration of intelligent features and autonomous functionalities on vehicles will lead to major economic benefits from reduced fuel consumption to efficient exploitation of the road network. While giving this information to the driver can be useful, there is also the possibility of overloading the driver with too much information. Existing vehicles already have some mechanisms to take certain actions if the driver fails to act. Future vehicles will need more complex decision making modules which receive the raw data from all available sources, process this data and inform the driver about the existing or impending situations and suggest, or even take actions. Intelligent vehicles can take advantage of using different sources of data to provide more reliable and more accurate information about driving situations and build a safer driving environment. I have identified five general sources of data which is available for intelligent vehicles: the vehicle itself, cameras on the vehicle, communication between the vehicle and other vehicles, communications between vehicles and roadside units and the driver information. But facing this huge amount of data requires a decision making module to collect this data and provide the best reaction based on the situation. In this thesis, I present a data fusion approach for decision making in vehicles in which a decision making module collects data from the available sources of information and analyses this data and provides the driver with helpful information such as traffic congestion, emergency messages, etc. The proposed approach uses agents to collect the data and the agents cooperate using a black board method to provide the necessary data for the decision making system. The Decision making system benefits from this data and provides the intelligent vehicle applications with the best action(s) to be taken. Overall, the results show that using this data fusion approach for making decision in vehicles shows great potential for improving performance of vehicular systems by reducing travel time and wait time and providing more accurate information about the surrounding environment for vehicles. In addition, the safety of vehicles will increase since the vehicles will be informed about the hazard situations