arXiv subjects

Markus Maurer

Publications and source records attributed to Markus Maurer.

At least 37 records · Page 2Linked to original sources

arXiv2024

Risk Management Core -- Towards an Explicit Representation of Risk in Automated Driving

While current automotive safety standards provide implicit guidance on how unreasonable risk can be avoided, manufacturers are required to specify risk acceptance criteria for Automated Driving Systems (SAE Level 3 and higher). However, the 'unreasonable' level of risk of Automated Driving Systems is not yet concisely defined. Solely applying current safety standards to such novel systems could potentially not be sufficient for their acceptance. As risk is managed with implicit knowledge about safety measures in existing automotive standards, an explicit alignment with risk acceptance criteria is challenging. Hence, we propose an approach for an explicit representation and management of risk, which we call the Risk Management Core. The proposal of this process framework is based on requirements elicited from current safety standards and is applied to the task of specifying safe behavior for an Automated Driving System in an example scenario.

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arXiv2024

On Assumptions with Respect to Occlusions in Urban Environments for Automated Vehicle Speed Decisions

Automated driving systems are subject to various kinds of uncertainty during design, development, and operation. These kinds of uncertainty lead to an inherent risk of the technology that can be mitigated, but never fully eliminated. Situations involving obscured traffic participants have become popular examples in the field to illustrate a subset of these uncertainties that developers must deal with during system design and implementation. In this paper, we describe necessary assumptions for a speed choice in a situation in which an ego-vehicle passes parked vehicles that generate occluded areas where a human intending to cross the road could be obscured. We develop a calculation formula for a dynamic speed limit that mitigates the collision risk in this situation, and investigate the resulting speed profiles in simulation based on example assumptions. This paper has two main results: First, we show that even without worst-case assumptions, dramatically reduced speeds would be driven to avoid collisions. Second, we highlight that design decisions regarding occlusion treatment are directly related to the risk that automated vehicles pose to pedestrians in urban environments. In this respect, we conclude that there needs to be a broader discussion about acceptable assumptions.

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arXiv2023

ODD-Centric Contextual Sensitivity Analysis Applied To A Non-Linear Vehicle Dynamics Model

Advanced driving functions, for assistance or full automation, require strong guarantees to be deployed. This means that such functions may not be available all the time, like now commercially available SAE Level 3 modes that are made available only on some roads and at law speeds. The specification of such restriction is described technically in the Operational Design Domain (ODD) which is a fundamental concept for the design of automated driving systems (ADS). In this work, we focus on the example of trajectory planning and control which are crucial functions for SAE level 4+ vehicles and often rely on model-based methods. Hence, the quality of the underlying models has to be evaluated with respect to the ODD. Mathematical analyses such as uncertainty and sensitivity analysis support the quantitative assessment of model quality in general. In this paper, we present a new approach to assess the quality of vehicle dynamics models using an ODD-centric sensitivity analysis. The sensitivity analysis framework is implemented for a 10-DoF nonlinear double-track vehicle dynamics model used inside a model-predictive trajectory controller. The model sensitivity is evaluated with respect to given ODD and maneuver parameters. Based on the results, ODD-compliant behavior generation strategies with the goal of minimizing model sensitivity are outlined.

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arXiv2023

Robust LSTM-based Vehicle Velocity Observer for Regular and Near-limits Applications

Accurate velocity estimation is key to vehicle control. While the literature describes how model-based and learning-based observers are able to estimate a vehicle's velocity in normal driving conditions, the challenge remains to estimate the velocity in near-limits maneuvers while using only conventional in-car sensors. In this paper, we introduce a novel neural network architecture based on Long Short-Term Memory (LSTM) networks to accurately estimate the vehicle's velocity in different driving conditions, including maneuvers at the limits of handling. The approach has been tested on real vehicle data and it provides more accurate estimations than state-of-the-art model-based and learning-based methods, for both regular and near-limits driving scenarios. Our approach is robust since the performance of the state-of-the-art observers deteriorates with higher dynamics, while our method adapts to different maneuvers, providing accurate estimations even at the vehicle's limits of handling.

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arXiv2022

Designing an Automated Vehicle: Strategies for Handling Tasks of a Previously Required Accompanying Person

When using a conventional passenger car, several groups of people are reliant on the assistance of an accompanying person, for example when getting in and out of the car. For the independent use of an automatically driving vehicle by those groups, the absence of a previously required accompanying person needs to be compensated. During the design process of an autonomous family vehicle, we found that a low-barrier vehicle design can only partly contribute to the compensation for the absence of a required human companion. In this paper, we present four strategies we identified for handling the tasks of a previously required accompanying individual. The presented top-down approach supports developers in identifying unresolved problems, in finding, structuring, and selecting solutions as well as in uncovering upcoming problems at an early stage in the development of novel concepts for driverless vehicles. As an example, we consider the hypothetical exit of persons in need of assistance. The application of the four strategies in this example demonstrates the far-reaching impact of consistently considering users in need of support in the development of automated vehicles.

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arXiv2022

Ein Beitrag zur durchgängigen, formalen Verhaltensspezifikation automatisierter Straßenfahrzeuge

Assuring safety of automated vehicles (SAE Level 3+) requires specifying and validating the behavior of such a vehicle in its operational environment. In order to argue and support assumptions that are made during the behavior specification within scenarios, a traceable documentation of design decisions is required. With the introduction of the \textit{semantic norm behavior analysis} a method is proposed, which contributes to a traceable mapping of concerns towards the behavior of an automated vehicle in its operational environment to a formal rule system of semantic concepts for considered scenarios. In this work, a semantic norm behavior analysis is conducted in two selected example scenarios. Thereby, an example of the formalization of behavioral rules from an excerpt of the German traffic code is given. -- Die Absicherung automatisierter Straßenfahrzeuge (SAE Level 3+) setzt die Spezifikation und Überprüfung des Verhaltens eines Fahrzeugs in seiner Betriebsumgebung voraus. Um Annahmen, welche bei der Verhaltensspezifikation innerhalb von Szenarien getroffen werden, begründen und belegen zu können, ist eine durchgängige Dokumentation dieser Entwurfsentscheidungen erforderlich. Mit der Einführung der \textit{semantischen Normverhaltensanalyse} wird eine Methode vorgeschlagen, mithilfe derer Ansprüche an das Verhalten eines automatisierten Fahrzeugs in seiner Betriebsumgebung durchgängig auf ein formales Regelsystem aus semantischen Konzepten für ausgewählte Szenarien abgebildet werden können. Eine semantische Normverhaltensanalyse wird in dieser Arbeit in zwei ausgewählten Szenarien durchgeführt. Hierfür werden Verhaltensregeln aus einem Auszug der Straßenverkehrsordnung exemplarisch formalisiert.

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arXiv2022

Compensating for the Absence of a Required Accompanying Person: A Draft of a Functional System Architecture for an Automated Vehicle

A major challenge in the development of a fully automated vehicle is to enable a large variety of users to use the vehicle independently and safely. Particular demands arise from user groups who rely on human assistance when using conventional cars. For the independent use of a vehicle by such groups, the vehicle must compensate for the absence of an accompanying person, whose actions and decisions ensure the accompanied person's safety even in unknown situations. The resulting requirements cannot be fulfilled only by the geometric design of the vehicle and the nature of its control elements. Special user needs must be taken into account in the entire automation of the vehicle. In this paper, we describe requirements for compensating for the absence of an accompanying person and show how required functions can be located in a hierarchical functional system architecture of an automated vehicle. In addition, we outline the relevance of the vehicle's operational design domain in this context and present a use case for the described functionalities.

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arXiv2022

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.

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arXiv2022

Toward Generating Sufficiently Valid Test Case Results: A Method for Systematically Assigning Test Cases to Test Bench Configurations in a Scenario-Based Test Approach for Automated Vehicles

To successfully launch automated vehicles into the consumer market, there must be credible proof that the vehicles will operate safely. However, finding a method to validate the vehicles' safe operation is a challenging problem. While scenario-based test approaches seem to be possible solutions, they require execution of a large number of test cases. Several test benches, ranging from actual test vehicles to partly or fully simulated environments, are available to execute these test cases. Each test bench provides different elements, which in turn, have different parameters and parameter ranges. The composition of elements with their specific parameter values at a specific test bench that is used to execute a test case is referred to as a test bench configuration. However, selecting the most suitable test bench configuration is difficult. The selected test bench configuration determines whether the execution of a specific test case provides sufficiently valid test case results with respect to the intended purpose, for example, validating a vehicle's safe operation. The effective and efficient execution of a large number of test cases requires a method for systematically assigning test cases to the most suitable test bench configuration. Based on a proposed method for classifying test bench configurations, we propose and illustrate a method for systematically assigning test cases to test bench configurations in a scenario-based test approach for automated vehicles. This assignment method allows for the effective and efficient execution of a large number of test cases while generating sufficiently valid test case results.

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arXiv2021

Toward a Consistent Taxonomy for Scenario-Based Development and Test Approaches for Automated Vehicles: A Proposal for a Structuring Framework, a Basic Vocabulary, and its Application

Ensuring and validating the safe operation of automated vehicles are key challenges for their market launch. Scenario-based development and test approaches are currently being pursued as possible solutions. An essential prerequisite for researching, applying, and standardizing these approaches is a consistent and agreed-upon taxonomy. This taxonomy must include relevant terms, their description, and the relationships between the respective terms. To the best of our knowledge, such a taxonomy does not yet exist, which often leads to misunderstandings, for example, in coordination processes and discussions. This publication contributes to this taxonomy. For this purpose, we propose a framework for structuring the taxonomy. Within this framework, we propose a basic vocabulary by identifying and describing terms that we consider particularly relevant for an overview of such scenario-based development and test approaches. Additionally, we visualize the proposed terms and the relationships between these terms with UML diagrams and explain the application of the proposed basic vocabulary with an example.

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arXiv2021

A Knowledge-based Approach for the Automatic Construction of Skill Graphs for Online Monitoring

Automated vehicles need to be aware of the capabilities they currently possess. Skill graphs are directed acylic graphs in which a vehicle's capabilities and the dependencies between these capabilities are modeled. The skills a vehicle requires depend on the behaviors the vehicle has to perform and the operational design domain (ODD) of the vehicle. Skill graphs were originally proposed for online monitoring of the current capabilities of an automated vehicle. They have also been shown to be useful during other parts of the development process, e.g. system design, system verification. Skill graph construction is an iterative, expert-based, manual process with little to no guidelines. This process is, thus, prone to errors and inconsistencies especially regarding the propagation of changes in the vehicle's intended ODD into the skill graphs. In order to circumnavigate this problem, we propose to formalize expert knowledge regarding skill graph construction into a knowledge base and automate the construction process. Thus, all changes in the vehicle's ODD are reflected in the skill graphs automatically leading to a reduction in inconsistencies and errors in the constructed skill graphs.

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arXiv2021

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$agil$.

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arXiv2020

Supporting Safe Decision Making Through Holistic System-Level Representations & Monitoring -- A Summary and Taxonomy of Self-Representation Concepts for Automated Vehicles

The market introduction of automated vehicles has motivated intense research efforts into the safety of automated vehicle systems. Unlike driver assistance systems, SAE Level 3+ systems are not only responsible for executing (parts of) the dynamic driving task (DDT), but also for monitoring the automation system's performance at all times. Key components to fulfill these surveillance tasks are system monitors which can assess the system's performance at runtime, e.g. to activate fallback modules in case of partial system failures. In order to implement reasonable monitoring strategies for an automated vehicle, holistic system-level approaches are required, which make use of sophisticated internal system models. In this paper we present definitions and an according taxonomy, subsuming such models as a vehicle's self-representation and highlight the terms' roles in a scene and situation representation. Holistic system-level monitoring does not only provide the possibility to use monitors for the activation of fallbacks. In this paper we argue, why holistic system-level monitoring is a crucial step towards higher levels of automation, and give an example how it also enables the system to react to performance loss at a tactical level by providing input for decision making.

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arXiv2020

Sensitivity Analysis for Vehicle Dynamics Models -- An Approach to Model Quality Assessment for Automated Vehicles

Model-based approaches have become increasingly popular in the domain of automated driving. This includes runtime algorithms, such as Model Predictive Control, as well as formal and simulative approaches for the verification of automated vehicle functions. With this trend, the quality of models becomes crucial for automated vehicle safety. Established tools from model theory which can be applied to assure model quality are uncertainty and sensitivity analysis [1]. In this paper, we conduct sensitivity analyses for a single and double track vehicle dynamics model to gain insights about the models' behavior under different operating conditions. We compare the models, point out the most important findings regarding the obtained parameters sensitivities, and provide examples of possible applications of the gained insights.

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arXiv2020

A LiDAR-based real-time capable 3D Perception System for Automated Driving in Urban Domains

We present a LiDAR-based and real-time capable 3D perception system for automated driving in urban domains. The hierarchical system design is able to model stationary and movable parts of the environment simultaneously and under real-time conditions. Our approach extends the state of the art by innovative in-detail enhancements for perceiving road users and drivable corridors even in case of non-flat ground surfaces and overhanging or protruding elements. We describe a runtime-efficient pointcloud processing pipeline, consisting of adaptive ground surface estimation, 3D clustering and motion classification stages. Based on the pipeline's output, the stationary environment is represented in a multi-feature mapping and fusion approach. Movable elements are represented in an object tracking system capable of using multiple reference points to account for viewpoint changes. We further enhance the tracking system by explicit consideration of occlusion and ambiguity cases. Our system is evaluated using a subset of the TUBS Road User Dataset. We enhance common performance metrics by considering application-driven aspects of real-world traffic scenarios. The perception system shows impressive results and is able to cope with the addressed scenarios while still preserving real-time capability.

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arXiv2019

A Method for Classifying Test Bench Configurations in a Scenario-Based Test Approach for Automated Vehicles

The introduction of automated vehicles demands a way to prove their safe operation. However, validating the safety of automated vehicles is still an unsolved problem. While the scenario-based test approach seems to provide a possible solution, it requires the execution of a high amount of test cases. Several test benches, from actual test vehicles to partly or fully simulated environments, are available, but choosing the optimal test bench, e.g. in terms of required execution time and costs, is a difficult task. Every test bench provides different elements, e.g. simulation models which can be used for test case execution. The composition of elements at a specific test bench is called test bench configuration. This test bench configuration determines the actual performance of a test bench and, therefore, whether the run of a particular test case provides valid test case results with respect to the intended purpose, e.g. a safety validation. For an effective and efficient test case execution, a method is required to assign test cases to the most appropriate test bench configuration. Therefore, it is indispensable to have a method to classify test bench configurations in a clear and reproducible manner. In this paper, we propose a method for classifying test benches and test bench configurations and illustrate the classification method with some examples. The classification method serves as a basis for a systematic assignment of test cases to test bench configurations which allows for an effective and efficient test case execution.

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arXiv2019

From Functional to Logical Scenarios: Detailing a Keyword-Based Scenario Description for Execution in a Simulation Environment

Scenario-based development and test processes are a promising approach for verifying and validating automated driving functions. For this purpose, scenarios have to be generated during the development process in a traceable manner. In early development stages, the operating scenarios of the item to be developed are usually described in an abstract, linguistic way.Within the scope of a simulation-assisted test process, these linguistically described scenarios have to be transformed into a state space representation and converted into data formats which can be used with the respective simulation environment. Currently, this step of detailing scenarios takes a considerable manual effort. Furthermore, a standardized interpretation of the linguistically described scenarios and a consistent transformation into the data formats are not guaranteed due to multiple authors as well as many constraints between the scenario parameters. In this paper, the authors present an approach to automatically detail a keyword-based scenario description for execution in a simulation environment and provide a basis for test case generation. As a first step, the keyword-based description is transformed into a parameter space representation. At the same time, constraints regarding the selection and combination of parameter values are documented for the following process steps (e. g. evolutionary or stochastic test methods). As a second step, the parameter space representation is converted into data formats required by the simulation environment. As an example, the authors use scenarios on German freeways and convert them into the data formats OpenDRIVE (description of the road) and OpenSCENARIO (description of traffic participants and environmental conditions) for execution in the simulation environment Virtual Test Drive.

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arXiv2018

Investigating Functional Redundancies in the Context of Vehicle Automation - A Trajectory Tracking Perspective

Level 3+ automated driving implies highest safety demands for the entire vehicle automation functionality. For the part of trajectory tracking, functional redundancies among all available actuators provide an opportunity to reduce safety requirements for single actuators. Yet, the exploitation of functional redundancies must be well argued if employed in a safety concept as physical limits can be reached. In this paper, we want to examine from a trajectory tracking perspective whether such a concept can be used. For this, we present a model predictive fault-tolerant trajectory tracking approach for over-actuated vehicles featuring wheel individual all-wheel drive, brakes, and steering. Applying this approach exemplarily demonstrates for a selected reference trajectory that degradations such as missing or undesired wheel torques as well as reduced steering dynamics can be compensated. Degradations at the physical actuator limits lead to significant deviations from the reference trajectory while small constant steering angles are partially critical.

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