Gerrit Bagschik

Map-relative localization in lane-level maps for ADAS and autonomous driving

Future advanced driver assistant systems put high demands on the environmental perception especially in urban environments. Todays on-board sensors and on-board algorithms still do not reach a satisfying level of development from the point of view of robustness and availability. Thus, map data is often used as an additional data input to support the on-board sensor system and algorithms. The usage of map data requires a highly correct pose within the map even in cases of positioning errors by global navigation satellite systems or geometrical errors in the map data. In this paper we propose and compare two approaches for map-relative localization exclusively using a lane-level map. These approaches deliberately avoid the usage of detailed a priori maps containing point-landmarks, grids or road-markings. Additionally, we propose a grid-based on-board fusion of road-marking information and stationary obstacles addressing the problem of missing or incomplete road-markings in urban scenarios.

Ability and skill graphs for system modeling, online monitoring, and decision support for vehicle guidance systems

In this paper, the ability and skill graphs are introduced for modeling vehicle guidance systems in the concept phase of the development process (abilities), for online monitoring of system operation (skills), and to support driving decisions (skill levels) of automated road vehicles and advanced driver assistance systems. Both graphs rely on a decomposition of the human driving task. An ability is the entirety of conditions which are necessary to provide a certain part of the driving task. The ability graph can be developed in parallel to the item definition according to the ISO 26262 standard in the concept phase of the development process and can be used for supporting further development steps. A skill is defined as an abstract representation of a part of the driving task including information about the skills current performance. The skill graph is used to monitor the current system performance during operation and skill levels are input to driving decisions. Abilities and skills cover all aspects of the driving task including environment and self perception, data processing, decision making, and behavior execution. During operation of the developed item, the skill graph is instantiated as a (distributed) software component to process online information for assessing current skill levels. Each skill uses one or more performance metrics, which represent its current performance capability in relation to the maximum (inherent) ability level. The resulting information could replace the monitoring of the system by a human driver and can be used as an input to driving decisions of the vehicle to support appropriate and safe decisions.

Towards Automated Driving: Unmanned Protective Vehicle for Highway Hard Shoulder Road Works

Mobile road works on the hard shoulder of German highways bear an increased accident risk for the crew of the protective vehicle which safeguards road works against moving traffic. The project “Automated Unmanned Protective Vehicle for Highway Hard Shoulder Road Works” aims at the unmanned operation of the protective vehicle in order to reduce this risk. Simultaneously, this means the very first unmanned operation of a vehicle on German roads in public traffic. This contribution introduces the project by pointing out the main objectives and demonstrates the current state of the system design regarding functionality, modes of operation, as well as the functional system architecture. Pivotal for the project, the scientific challenges raised by the unmanned operation - strongly related to the general challenges in the field of automated driving - are presented as well. The results of the project shall serve as a basis to stimulate an advanced discussion about ensuring safety for fully automated vehicles in public traffic operating at higher speeds and in less defined environments. Thus, this contribution aims at contacting the scientific community in an early state of the project.

Hazard analysis and risk assessment for an automated unmanned protective vehicle

For future application of automated vehicles in public traffic, ensuring functional safety is essential. In this context, a hazard analysis and risk assessment is an important input for designing functionally vehicle automation systems. In this contribution, we present a detailed hazard analysis and risk assessment (HARA) according to the ISO 26262 standard for a specific Level 4 application, namely an unmanned protective vehicle operated without human supervision for motorway hard shoulder roadworks.

Identification of potential hazardous events for an Unmanned Protective Vehicle

The project Automated Unmanned Protective Vehicle for Highway Hard Shoulder Road Works (aFAS) aims to develop an unmanned protective vehicle to reduce the risk of injuries due to crashes for road workers. To ensure functional safety during operation in public traffic the system shall be developed following the ISO 26262 standard. After defining the functional range in the item definition, a hazard analysis and risk assessment has to be done. The ISO 26262 standard gives hints how to process this step and demands a systematic way to identify system hazards. Best practice standards provide systematic ways for hazard identification, but lack applicability for automated vehicles due to the high variety and number of different driving situations even with a reduced functional range. This contribution proposes a new method to identify hazardous events for a system with a given functional description. The method utilizes a skill graph as a functional model of the system and an overall definition of a scene for automated vehicles to identify potential hazardous events. An adapted Hazard and Operability Analysis approach is used to identify system malfunctions. A combination of all methods results in operating scenes with potential hazardous events. These can be assessed afterwards towards their criticality. A use case example is taken from the current development phase of the project aFAS.

Safety goals and functional safety requirements for actuation systems of automated vehicles

Increasing automation of vehicle guidance is one of the major trends in the automotive industry. Some auto makers have announced that automated vehicles will be deployed in public traffic by the end of this decade (level 4 in sense of the definition of SAE, level 5 later). Until then, one central challenge is ensuring functional safety of automated vehicles. Still, it is not clear how safety concepts for automated vehicles can be designed appropriately. This affects all parts of vehicle automation systems: environment perception, decision making, and actuation. In this contribution we derive safety goals and functional safety requirements according to ISO 26262 for actuation systems of automated vehicles systematically, following a systems theory based approach. The findings summarize elaborate measures to be implemented in actuation systems of automated vehicles when operated without human supervision.

Towards a System-Wide Functional Safety Concept for Automated Road Vehicles

In this chapter, a process to derive a system-wide functional safety concept for automated road vehicles is presented and a short introduction of Skill and Ability Graphs for a functional safety concept is given. The process to develop a functional safety concept contains an extension to the ISO 26262 standard’s Driver Assistance System development process. This extension is a Skill Graph to model system skills in the concept phase. The Skill Graph improves the Hazard Analysis and Risk Assessment by modeling driving skills early in the development process. Additionally, the Skill Graph is transferred to an Ability Graph, used to design a self-perception and self-representation, which enables monitoring of the system’s operation and functional capabilities online. This self-representation can be part of a technical safety concept. Based on the ability levels, safety actions can be derived which maintain or reach a safe state of operation. As a result, a self-monitoring system is possible, in which humans, either aboard the vehicle or external, do not have to monitor the system.