The Monitor as Key Architecture Element for Safe Self-Driving Cars

Mehmed, Ayhan; Antlanger, Moritz; Steiner, Wilfried

doi:10.1109/dsn-s50200.2020.00015

“…UAVs need to operate robustly in dynamic environments [55], [56], and redundancy in compute or sensor ensures safety in the event of a failure. Sometimes dual redundancy [57] (or triple redundancy [58]) increases reliability where a majority vote determines the final decision. However, though redundancy can increase reliability, it can also increase the cost and negatively affects the UAV's performance.…”

Section: Modular Redundancy Characterizationmentioning

confidence: 99%

Roofline Model for UAVs: A Bottleneck Analysis Tool for Onboard Compute Characterization of Autonomous Unmanned Aerial Vehicles

Wan¹,

Bhardwaj²,

Jadhav³

et al. 2022

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We introduce an early-phase bottleneck analysis and characterization model called the F-1 for designing computing systems that target autonomous Unmanned Aerial Vehicles (UAVs). The model provides insights by exploiting the fundamental relationships between various components in the autonomous UAV, such as sensor, compute, and body dynamics. To guarantee safe operation while maximizing the performance (e.g., velocity) of the UAV, the compute, sensor, and other mechanical properties must be carefully selected or designed. The F-1 model provides visual insights that can aid a system architect in understanding the optimal compute design or selection for autonomous UAVs. The model is experimentally validated using real UAVs, and the error is between 5.1% to 9.5% compared to real-world flight tests. An interactive web-based tool for the F-1 model called Skyline is available for free of cost use at: https://bit.ly/skyline-tool * This work was initiated and conducted while the student was at Harvard University and he is now a Ph.D. student at the Georgia Institute of Technology.

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“…This generic view can also be deduced from more complex approaches, such as the ones analysed in [79] or the functional architecture proposed in [44, p. 68, Figure 27]. Common to the discussion of ADS architectures seem to be the usage of monitoring or surveillance capabilities for reliability [44,78,79,80,81,82,83]. Such capabilities are further discussed in this section.…”

Section: E Supervisor Architecturesmentioning

confidence: 99%

Holistic Perspectives on Safety of Automated Driving Systems - Methods for Provision of Evidence

Gyllenhammar¹,

Campos²,

Törngren³

2022

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In recent years, the enormous investments in Automated Driving Systems (ADSs) have distinctly advanced ADS technologies. Despite promises made by several high profile auto-makers, it has however become clear that the challenges involved for deploying ADS have been drastically underestimated. This paper focuses on the challenge of providing sufficient evidence to support the safety claims of ADSs. The provisioning of such evidence clearly relates both to technical maturity of ADS systems (including actual experiences from deploying such systems), and on the development of methodologies for reasoning about ADS safety claims. Contrary to previous generations of automotive systems, common design, development, verification and validation methods for safety critical systems do not suffice to cope with the increased complexity and operational uncertainties of an ADS. Therefore, the aim of this paper is to provide an understanding of existing methods focusing on the development of a safe ADS and, most importantly, identifying the associated challenges and gaps. We present eight challenges, collectively distinguishing ADSs from safety critical systems in general, and discuss the existing methods in the light of these eight challenges. Based on this discussion, a set of research gaps are identified.

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“…Fault Containment Unit (FCU). Monitoring and deciding when to switch to the supervisory channel, represent an essential ingredient in such solutions [44,78,79,80,81,82,83]. Detecting failures is non-trivial given the inherent uncertainty and risk in ADS operation (again, related to challenges (C1) and (C2)).…”

Section: E Supervisor Architecturesmentioning

confidence: 99%

Holistic Perspectives on Safety of Automated Driving Systems - Methods for Provision of Evidence

Gyllenhammar¹,

Campos²,

Törngren³

2022

Preprint

0

View full text Add to dashboard Cite

In recent years, the enormous investments in Automated Driving Systems (ADSs) have distinctly advanced ADS technologies. Despite promises made by several high profile auto-makers, it has however become clear that the challenges involved for deploying ADS have been drastically underestimated. This paper focuses on the challenge of providing sufficient evidence to support the safety claims of ADSs. The provisioning of such evidence clearly relates both to technical maturity of ADS systems (including actual experiences from deploying such systems), and on the development of methodologies for reasoning about ADS safety claims. Contrary to previous generations of automotive systems, common design, development, verification and validation methods for safety critical systems do not suffice to cope with the increased complexity and operational uncertainties of an ADS. Therefore, the aim of this paper is to provide an understanding of existing methods focusing on the development of a safe ADS and, most importantly, identifying the associated challenges and gaps. We present eight challenges, collectively distinguishing ADSs from safety critical systems in general, and discuss the existing methods in the light of these eight challenges. Based on this discussion, a set of research gaps are identified.

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The Monitor as Key Architecture Element for Safe Self-Driving Cars

Cited by 7 publications

References 7 publications

Roofline Model for UAVs: A Bottleneck Analysis Tool for Onboard Compute Characterization of Autonomous Unmanned Aerial Vehicles

Roofline Model for UAVs: A Bottleneck Analysis Tool for Onboard Compute Characterization of Autonomous Unmanned Aerial Vehicles

Holistic Perspectives on Safety of Automated Driving Systems - Methods for Provision of Evidence

Holistic Perspectives on Safety of Automated Driving Systems - Methods for Provision of Evidence

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