Model Predictive Control Based Trajectory Generation for Autonomous Vehicles - An Architectural Approach

Research in the field of automated driving has created promising results in the last years. Some research groups have shown perception systems which are able to capture even complicated urban scenarios in great detail. Yet, what is often missing are general-purpose path- or trajectory planners which are not designed for a specific purpose. In this paper we look at path- and trajectory planning from an architectural point of view and show how model predictive frameworks can contribute to generalized path- and trajectory generation approaches for generating safe trajectories even in cases of system failures.Comment: Presented at IEEE Intelligent Vehicles Symposium 2017, Los Angeles, CA, US

Towards Efficient Hazard Identification in the Concept Phase of Driverless Vehicle Development

The complex functional structure of driverless vehicles induces a multitude of potential malfunctions. Established approaches for a systematic hazard identification generate individual potentially hazardous scenarios for each identified malfunction. This leads to inefficiencies in a purely expert-based hazard analysis process, as each of the many scenarios has to be examined individually. In this contribution, we propose an adaptation of the strategy for hazard identification for the development of automated vehicles. Instead of focusing on malfunctions, we base our process on deviations from desired vehicle behavior in selected operational scenarios analyzed in the concept phase. By evaluating externally observable deviations from a desired behavior, we encapsulate individual malfunctions and reduce the amount of generated potentially hazardous scenarios. After introducing our hazard identification strategy, we illustrate its application on one of the operational scenarios used in the research project UNICAR$agil$.Comment: Published in 2020 IEEE Intelligent Vehicles Symposium (IV), Las Vegas, NV, USA, October 19-November 13, 202

Functional Safety Concept Generation within the Process of Preliminary Design of Automated Driving Functions at the Example of an Unmanned Protective Vehicle

Structuring the early design phase of automotive systems is an important part of efficient and successful development processes. Today, safety considerations (e.g., the safety life cycle of ISO 26262) significantly affect the course of development. Preliminary designs are expressed in functional system architectures, which are required to form safety concepts. Thus, mapping tasks and work products to a reference process during early design stages is an important part of structuring the system development. This contribution describes the systematic creation and notation of the functional safety concept within the concept phase of development of an unmanned protective vehicle within the research project aFAS. Different stages of preliminary design and dependencies between them are displayed by the work products created and used. The full set of functional safety requirements and an excerpt of the safety argument structure of the SAE level 4 application are presented

Integration of a Vehicle Operating Mode Management into UNICARagil’s Automotive Service-oriented Software Architecture

Automated vehicles require a central decision unit in order to coordinate the responsibility for the driving task between multiple operating modes. Additionally, other nondriving related tasks such as operation of an automatic door system must be coordinated as well. In this paper, we will motivate the usefulness of such a central decision unit at the example of the operating mode management of the UNICARagil project. We will describe its integration with UNICARagil’s Automotive Service-oriented Software Architecture and how modularity of this service-oriented software architecture is ensured. An example from the project’s context will further illustrate the functioning principle of the operating mode management in combination with the service orchestration of the Automotive Service-oriented Software Architecture

Towards Safety Concepts for Automated Vehicles by the Example of the Project UNICARagil

Striving towards deployment of SAE level 4+ vehicles in public traffic, researchers and developers face several challenges due to the targeted operation in an open environment. Due to the absence of a human supervisor, ensuring and validating safety while driving automatically is one of the key challenges. The arising complexity of the technical system must be handled during the entire research and development process. In this contribution, we outline the coherence of different safety-activities in the research project UNICARagi/. We derive high-level safety requirements and present the central safety mechanisms applied to automated diriving. Moreover, we outline the approaches of the project UNICARagi/ to address the validation challenge for automated vehicles. In order to demonstrate the overall approach towards a coherent safety argumentation, the connection of high-level safety requirements, safety mechanisms, as weil as validation approaches is illustrated by means of a selected example scenario

UNICARagil - Disruptive Modular Architectures for Agile, Automated Vehicle Concepts

This paper introduces UNICARagil, a collaborative project carried out by a consortium of seven German universities and six industrial partners, with funding provided by the Federal Ministry of Education and Research of Germany. In the scope of this project, disruptive modular structures for agile, automated vehicle concepts are researched and developed. Four prototype vehicles of different characteristics based on the same modular platform are going to be build up over a period of four years. The four fully automated and driverless vehicles demonstrate disruptive architectures in hardware and software, as well as disruptive concepts in safety, security, verification and validation. This paper outlines the most important research questions underlying the project

Automation of the UNICARagil Vehicles

The German research project UNICARagil is a collaboration between eight universities and six industrial partners funded by the Federal Ministry of Education and Research. It aims to develop innovative modular architectures and methods for new agile, automated vehicle concepts. This paper summarizes the automation approach of the driverless vehicle concept and its modular realization within the four demonstration vehicles to be built by the consortium. On-board each vehicle, this comprises sensor modules for environment perception and modelling, motion planning for normal driving and safe halts, as well as the respective control algorithms and base functionalities like precise localization. A control room and cloud functionalities provide off-board support to the vehicles, which are additionally addressed in this paper

A Taxonomy to Unify Fault Tolerance Regimes for Automotive Systems: Defining Fail-Operational, Fail-Degraded, and Fail-Safe

This paper presents a taxonomy that allows defining the fault tolerance regimes fail-operational, fail-degraded, and fail-safe in the context of automotive systems. Fault tolerance regimes such as these are widely used in recent publications related to automated driving, yet without definitions. This largely holds true for automotive safety standards, too. We show that fault tolerance regimes defined in scientific publications related to the automotive domain are partially ambiguous as well as taxonomically unrelated. The presented taxonomy is based on terminology stemming from ISO 26262 as well as from systems engineering. It uses four criteria to distinguish fault tolerance regimes. In addition to fail-operational, fail-degraded, and fail-safe, the core terminology consists of operational and fail-unsafe. These terms are supported by definitions of available performance, nominal performance, functionality, and a concise definition of the safe state. For verification, we show by means of two examples from the automotive domain that the taxonomy can be applied to hierarchical systems of different complexity.Comment: 12 pages, 4 figures, 1 table, accepted to appear in IEEE Transactions on Intelligent Vehicle