114 research outputs found
SOTIF Entropy: Online SOTIF Risk Quantification and Mitigation for Autonomous Driving
Autonomous driving confronts great challenges in complex traffic scenarios,
where the risk of Safety of the Intended Functionality (SOTIF) can be triggered
by the dynamic operational environment and system insufficiencies. The SOTIF
risk is reflected not only intuitively in the collision risk with objects
outside the autonomous vehicles (AVs), but also inherently in the performance
limitation risk of the implemented algorithms themselves. How to minimize the
SOTIF risk for autonomous driving is currently a critical, difficult, and
unresolved issue. Therefore, this paper proposes the "Self-Surveillance and
Self-Adaption System" as a systematic approach to online minimize the SOTIF
risk, which aims to provide a systematic solution for monitoring,
quantification, and mitigation of inherent and external risks. The core of this
system is the risk monitoring of the implemented artificial intelligence
algorithms within the AV. As a demonstration of the Self-Surveillance and
Self-Adaption System, the risk monitoring of the perception algorithm, i.e.,
YOLOv5 is highlighted. Moreover, the inherent perception algorithm risk and
external collision risk are jointly quantified via SOTIF entropy, which is then
propagated downstream to the decision-making module and mitigated. Finally,
several challenging scenarios are demonstrated, and the Hardware-in-the-Loop
experiments are conducted to verify the efficiency and effectiveness of the
system. The results demonstrate that the Self-Surveillance and Self-Adaption
System enables dependable online monitoring, quantification, and mitigation of
SOTIF risk in real-time critical traffic environments.Comment: 16 pages, 10 figures, 2 tables, submitted to IEEE TIT
PeSOTIF: a Challenging Visual Dataset for Perception SOTIF Problems in Long-tail Traffic Scenarios
Perception algorithms in autonomous driving systems confront great challenges
in long-tail traffic scenarios, where the problems of Safety of the Intended
Functionality (SOTIF) could be triggered by the algorithm performance
insufficiencies and dynamic operational environment. However, such scenarios
are not systematically included in current open-source datasets, and this paper
fills the gap accordingly. Based on the analysis and enumeration of trigger
conditions, a high-quality diverse dataset is released, including various
long-tail traffic scenarios collected from multiple resources. Considering the
development of probabilistic object detection (POD), this dataset marks trigger
sources that may cause perception SOTIF problems in the scenarios as key
objects. In addition, an evaluation protocol is suggested to verify the
effectiveness of POD algorithms in identifying the key objects via uncertainty.
The dataset never stops expanding, and the first batch of open-source data
includes 1126 frames with an average of 2.27 key objects and 2.47 normal
objects in each frame. To demonstrate how to use this dataset for SOTIF
research, this paper further quantifies the perception SOTIF entropy to confirm
whether a scenario is unknown and unsafe for a perception system. The
experimental results show that the quantified entropy can effectively and
efficiently reflect the failure of the perception algorithm.Comment: 7 pages, 5 figures, 4 tables, submitted to 2023 ICR
Safety of the Intended Functionality Concept Integration into a Validation Tool Suite
Nowadays, the increasing complexity of Advanced Driver Assistance Systems
(ADAS) and Automated Driving (AD) means that the industry must move towards a
scenario-based approach to validation rather than relying on established
technology-based methods. This new focus also requires the validation process
to take into account Safety of the Intended Functionality (SOTIF), as many
scenarios may trigger hazardous vehicle behaviour. Thus, this work demonstrates
how the integration of the SOTIF process within an existing validation tool
suite can be achieved. The necessary adaptations are explained with
accompanying examples to aid comprehension of the approach
Introducing Safety and Security Co-engineering Related Research Orientations in the Field of Automotive Security
Since modern vehicles are connected and their transport processes are strongly supported by different automated functions, malicious external interventions can impair safety integrity. Therefore, it seems to be reasonable in the future to introduce safety and security co-engineering approaches in the automotive industry. With regard to the performed evaluation, three main promising research orientations have been identified. Automotive safety and security related development of co-engineering methodology and validation framework are of key importance from the viewpoint of autonomous transportation. Accordingly, a scenario based, integrated evaluation of automotive safety and security would be closely fit to the concept of SOTIF and the SoS approach. Beyond this, the communication and network security of "vehicle to everything" channels have to also be in the focus of automotive researches. Additionally, the development of automotive anomaly detection systems, especially focusing on the complex SoS operation processes will be a highly important research orientation
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A Framework to Handle Uncertainties of Machine Learning Models in Compliance with ISO 26262
YesAssuring safety and thereby certifying is a key challenge of
many kinds of Machine Learning (ML) Models. ML is one of the most
widely used technological solutions to automate complex tasks such as
autonomous driving, traffic sign recognition, lane keep assist etc. The
application of ML is making a significant contributions in the automotive
industry, it introduces concerns related to the safety and security of these
systems. ML models should be robust and reliable throughout and prove
their trustworthiness in all use cases associated with vehicle operation.
Proving confidence in the safety and security of ML-based systems and
there by giving assurance to regulators, the certification authorities, and
other stakeholders is an important task. This paper proposes a framework
to handle uncertainties of ML model to improve the safety level and
thereby certify the ML Models in the automotive industry.The full-text of this book chapter will be released for public view at the end of the publisher embargo on 18 Nov 2023
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