115 research outputs found
Development and Implementation of a Concept for the Meta Description of Highway Driving Scenarios with Focus on Interactions of Road Users
Threat Assessment and Risk Analysis (TARA) for Interoperable Medical Devices in the Operating Room Inspired by the Automotive Industry
Prevailing trends in the automotive and medical device industry, such as life cycle overarching configurability, connectivity, and automation, require an adaption of development processes, especially regarding the security and safety thereof. The changing requirements imply that interfaces are more exposed to the outside world, making them more vulnerable to cyberattacks or data leaks. Consequently, not only do development processes need to be revised but also cybersecurity countermeasures and a focus on safety, as well as privacy, have become vital. While vehicles are especially exposed to cybersecurity and safety risks, the medical devices industry faces similar issues. In the automotive industry, proposals and draft regulations exist for security-related risk assessment processes. The medical device industry, which has less experience in these topics and is more heterogeneous, may benefit from drawing inspiration from these efforts. We examined and compared current standards, processes, and methods in both the automotive and medical industries. Based on the requirements regarding safety and security for risk analysis in the medical device industry, we propose the adoption of methods already established in the automotive industry. Furthermore, we present an example based on an interoperable Operating Room table (OR table)
A Generic System for Automotive Software Over the Air (SOTA) Updates Allowing Efficient Variant and Release Management
The introduction of Software Over The Air (SOTA) Updates in the automotive industry offers both the Original Equipment Manufacturer and the driver many advantages such as cost savings through inexpensive over the air bug fixes. Furthermore, it enables enhancing the capabilities of future vehicles throughout their life-cycle. However, before making SOTA a reality for safety-critical automotive functions, major challenges must be deeply studied and resolved: namely the related security risks and the required high system safety. The security concerns are primarily related to the attack and manipulation threats of wireless connected and update-capable cars. The functional safety requirements must be fulfilled despite the agility needed by some software updates and the typically high variants numbers.
We studied the state of the art and developed a generic SOTA updates system based on a Server-Client architecture and covering main security and safety aspects including a rollback capability. The proposed system offers release and variant management, which is the main novelty of this work. The proof of concept implementation with a server running on a host PC and an exemplary Electric/Electronic network showed the feasibility and the benefits of SOTA updates
Unveiling Objects with SOLA: An Annotation-Free Image Search on the Object Level for Automotive Data Sets
Huge image data sets are the fundament for the development of the perception
of automated driving systems. A large number of images is necessary to train
robust neural networks that can cope with diverse situations. A sufficiently
large data set contains challenging situations and objects. For testing the
resulting functions, it is necessary that these situations and objects can be
found and extracted from the data set. While it is relatively easy to record a
large amount of unlabeled data, it is far more difficult to find demanding
situations and objects. However, during the development of perception systems,
it must be possible to access challenging data without having to perform
lengthy and time-consuming annotations. A developer must therefore be able to
search dynamically for specific situations and objects in a data set. Thus, we
designed a method which is based on state-of-the-art neural networks to search
for objects with certain properties within an image. For the ease of use, the
query of this search is described using natural language. To determine the time
savings and performance gains, we evaluated our method qualitatively and
quantitatively on automotive data sets
Model-based resource analysis and synthesis of service-oriented automotive software architectures
Context Automotive software architectures describe distributed functionality by an interaction of software components. One drawback of today\u27s architectures is their strong integration into the onboard communication network based on predefined dependencies at design time. The idea is to reduce this rigid integration and technological dependencies. To this end, service-oriented architecture offers a suitable methodology since network communication is dynamically established at run-time. Aim We target to provide a methodology for analysing hardware resources and synthesising automotive service-oriented architectures based on platform-independent service models. Subsequently, we focus on transforming these models into a platform-specific architecture realisation process following AUTOSAR Adaptive. Approach For the platform-independent part, we apply the concepts of design space exploration and simulation to analyse and synthesise deployment configurations, i. e., mapping services to hardware resources at an early development stage. We refine these configurations to AUTOSAR Adaptive software architecture models representing the necessary input for a subsequent implementation process for the platform-specific part. Result We present deployment configurations that are optimal for the usage of a given set of computing resources currently under consideration for our next generation of E/E architecture. We also provide simulation results that demonstrate the ability of these configurations to meet the run time requirements. Both results helped us to decide whether a particular configuration can be implemented. As a possible software toolchain for this purpose, we finally provide a prototype. Conclusion The use of models and their analysis are proper means to get there, but the quality and speed of development must also be considered
Exploring the Range of Possible Outcomes by means of Logical Scenario Analysis and Reduction for Testing Automated Driving Systems
With the implementation of the new EU regulation 2022/1426 regarding the
type-approval of the automated driving system (ADS) of fully automated
vehicles, scenario-based testing has gained significant importance in
evaluating the performance and safety of advanced driver assistance systems and
automated driving systems. However, the exploration and generation of concrete
scenarios from a single logical scenario can often lead to a number of similar
or redundant scenarios, which may not contribute to the testing goals.
This paper focuses on the the goal to reduce the scenario set by clustering
concrete scenarios from a single logical scenario. By employing clustering
techniques, redundant and uninteresting scenarios can be identified and
eliminated, resulting in a representative scenario set. This reduction allows
for a more focused and efficient testing process, enabling the allocation of
resources to the most relevant and critical scenarios. Furthermore, the
identified clusters can provide valuable insights into the scenario space,
revealing patterns and potential problems with the system's behavior.Comment: submitted to IEEE ITSC 202
Context-aware Security for Vehicles and Fleets: A Survey
Vehicles are becoming increasingly intelligent and connected. Interfaces for communication with the vehicle, such as WiFi and 5G, enable seamless integration into the user’s life, but also cyber attacks on the vehicle. Therefore, research is working on in-vehicle countermeasures such as authentication, access controls, or intrusion detection. Recently, legal regulations have also become effective that require automobile manufacturers to set up a monitoring system for fleet-wide security analysis. The growing amount of software, networking, and the automation of driving create new challenges for security. Context-awareness, situational understanding, adaptive security, and threat intelligence are necessary to cope with these ever-increasing risks. In-vehicle security should be adaptive to secure the car in an infinite number of (driving) situations. For fleet-wide analysis and alert triage, knowledge and understanding of the circumstances are required. Context-awareness, nonetheless, has been sparsely considered in the field of vehicle security. This work aims to be a precursor to context-aware, adaptive and intelligent security for vehicles and fleets. To this end, we provide a comprehensive literature review that analyzes the vehicular as well as related domains. Our survey is mainly characterized by the detailed analysis of the context information that is relevant for vehicle security in the future
An Overview of Automotive Service-Oriented Architectures and Implications for Security Countermeasures
New requirements from the customers\u27 and manufacturers\u27 point of view such as adding new software functions during the product life cycle require a transformed architecture design for future vehicles. The paradigm of signal-oriented communication established for many years will increasingly be replaced by service-oriented approaches in order to increase the update and upgrade capability. In this article, we provide an overview of current protocols and communication patterns for automotive architectures based on the service-oriented architecture (SOA) paradigm and compare them with signal-oriented approaches. Resulting challenges and opportunities of SOAs with respect to information security are outlined and discussed. For this purpose, we explain different security countermeasures and present a state of the section of automotive approaches in the fields of firewalls, Intrusion Detection Systems (IDSs) and Identity and Access Management (IAM). Our final discussion is based on an exemplary hybrid architecture (signal- and service-oriented) and examines the adaptation of existing security measures as well as their specific security features
1001 Ways of Scenario Generation for Testing of Self-driving Cars: A Survey
Scenario generation is one of the essential steps in scenario-based testing
and, therefore, a significant part of the verification and validation of driver
assistance functions and autonomous driving systems. However, the term scenario
generation is used for many different methods, e.g., extraction of scenarios
from naturalistic driving data or variation of scenario parameters. This survey
aims to give a systematic overview of different approaches, establish different
categories of scenario acquisition and generation, and show that each group of
methods has typical input and output types. It shows that although the term is
often used throughout literature, the evaluated methods use different inputs
and the resulting scenarios differ in abstraction level and from a systematical
point of view. Additionally, recent research and literature examples are given
to underline this categorization.Comment: accepted at IEEE IV 202
Generic Patterns for Intrusion Detection Systems in Service-Oriented Automotive and Medical Architectures
To implement new software functions and more flexible updates in the future as well as to provide cloud-based functionality, the service-oriented architecture (SOA) paradigm is increasingly being integrated into automotive electrical and electronic architecture (E/E architectures). In addition to the automotive industry, the medical industry is also researching SOA-based solutions to increase the interoperability of devices (vendor-independent). The resulting service-oriented communication is no longer fully specified during design time, which affects information security measures. In this paper, we compare different SOA protocols for the automotive and medical fields. Furthermore, we explain the underlying communication patterns and derive features for the development of an SOA-based Intrusion Detection System (IDS)
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