Safe Perception: On Relevance of Objects for Vehicle Safety

Buerkle, Cornelius; Oboril, Fabian; Jarquin, Julio; Pasch, Frederik; Scholl, Kay-Ulrich

doi:10.1109/itsc48978.2021.9564749

Cited by 6 publications

(3 citation statements)

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“…In [5] the need to estimate vehicle-level MTBF was discussed, yet a generic and scalable model is missing. The authors in [8] introduced the notion of safetyrelevant perception errors and underlined that not every perception error will lead to a collision. However, also in this work, a concrete model capturing the relation between collisions and perception errors is missing.…”

Section: Preliminariesmentioning

confidence: 99%

“…On the other hand, comprehensive safety solutions for the perception systems are still missing. To make matters worse, even the best perception systems have inherent weaknesses and limitations, which can cause undetected objects even in the close vicinity of the AV, as shown in [8]. If such a perception error appears in a critical traffic situation (e.g.…”

Section: Introductionmentioning

confidence: 99%

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MTBF Model for AVs -- From Perception Errors to Vehicle-Level Failures

Oboril,

Buerkle,

Sussmann

et al. 2022

Preprint

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The development of Automated Vehicles (AVs) is progressing quickly and the first robotaxi services are being deployed worldwide. However, to receive authority certification for mass deployment, manufactures need to justify that their AVs operate safer than human drivers. This in turn creates the need to estimate and model the collision rate (failure rate) of an AV taking all possible errors and driving situations into account. In other words, there is the strong demand for comprehensive Mean Time Between Failure (MTBF) models for AVs. In this paper, we will introduce such a generic and scalable model that creates a link between errors in the perception system to vehicle-level failures (collisions). Using this model, we are able to derive requirements for the perception quality based on the desired vehicle-level MTBF or vice versa to obtain an MTBF value given a certain mission profile and perception quality.

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Section: Preliminariesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

MTBF Model for AVs -- From Perception Errors to Vehicle-Level Failures

Oboril,

Buerkle,

Sussmann

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…An AV should be able to detect a road hazard and adapt its control accordingly (e.g., if an obstacle is seen on the road, then the AV speed may need to be reduced [13,14]. Hazard recognition is part of the AV perception system and as such may be subject to failures.…”

Section: Modelling Road Hazards and The Effect On Av Safety Of Hazard...mentioning

confidence: 99%

Modelling road hazards and the effect on AV safety of hazardous failures

Buerkle

Oboril

Paulitsch

et al. 2022

2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)

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Autonomous vehicles (AV) are about to appear on our roads within the next few years. However, to achieve the final breakthrough, not only functional progress is required, but also fundamental safety questions must be solved. Among those, a question demanding special attention is the need to assess the overall safety of an AV and quantify that it is safe enough to take part in normal traffic despite its inherent imperfections. Therefore, this paper describes a probabilistic model, which allows to study how imperfections of an AV perception system and of mechanisms responsible for AV safety (e.g., Safety Monitors), can impact AV safety in the presence of road hazards. We also demonstrate how the model can be used to validate if the AV is safe enough, to understand the criticality of (perception) errors, and to identify areas/parameters that have more influence on safety than others.

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