Application of Functional Safety in Autonomous Vehicles Using ISO 26262 Standard: A Survey

Gosavi, Mukul Anil; Rhoades, Benjamin B.; Conrad, James M.

doi:10.1109/secon.2018.8479057

Cited by 16 publications

(9 citation statements)

References 12 publications

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“…Risk assessment is extensively used in automotive development as part of ISO 26262 [12]. With the development of higher SAE automation levels in vehicles, a new standard such as ISO/PAS 21448 [3], also known as the Safety Of The Intended Functions (SOTIF) [13], is introduced to identify risks from improper vehicle functions and their operational limits.…”

Section: Related Workmentioning

confidence: 99%

“…The linear distribution of the risk probability is thus applied across the different risk zonal areas. The amount of linear drop across different regions can be formulated for each region with (9) to (12). One example is to use a similar model like LID, where layer interval Z can be represented as Z D for the calculation of the rest of the YELLOW and GREEN regions shown in ( 9) to (12).…”

Section: ) Interzonal Probability Distribution (Ipd)mentioning

confidence: 99%

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Infrastructure-Based Pedestrian Risk Tagging Methodology to Support AV Risk Assessment

et al. 2022

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Section: Related Workmentioning

confidence: 99%

Section: ) Interzonal Probability Distribution (Ipd)mentioning

confidence: 99%

Infrastructure-Based Pedestrian Risk Tagging Methodology to Support AV Risk Assessment

et al. 2022

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“…Several works assess functional safety for AD systems. Prior to the publication of the SOTIF standard, where specific safety considerations are made for AD systems, Gosavi et al [20] analyzed the software architecture of AD systems and showed that ISO 26262 is not enough to prove their functional safety. Helmut et al [21] also reviewed issues concerning the availability, reliability, and architecture of AD systems, and proposed ways to handle these challenges and to adapt ISO 26262.…”

Section: Related Workmentioning

confidence: 99%

Enabling Unit Testing of Already-Integrated AI Software Systems: The Case of Apollo for Autonomous Driving

Alcon

Tabani

Abella

et al. 2021

2021 24th Euromicro Conference on Digital System Design (DSD)

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The advanced AI-based software used for autonomous driving comprises multiple highly-coupled modules that are data and control dependent. Deploying those alreadyintegrated software frameworks makes unit testing, a fundamental step in the validation process of critical software, very challenging in safety-critical systems. To tackle this issue, in this paper, we show the steps we followed to develop standalone versions of the modules in an industry-level autonomous driving framework (Apollo) by applying several modifications to its architectural design. We show how the standalone modules have the same functional behavior as their integrated counterpart modules. We exemplify the benefits of standalone modules by performing incremental analysis of the software timing requirements of each module running on a heterogeneous System on Chip (SoC). This is a mandatory step to consolidate and integrate software modules guaranteeing timing constraints (e.g. related to freedom from interference) while maximizing SoC utilization.Index Terms-Unit testing, autonomous driving, Apollo1 The residual risk is the amount of risk remaining after specific risk mitigation measures are put in place.

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“…Even if this can never be achieved 100 percent, emergency strategies can be implemented that mitigate the consequences of an error. The ISO 26262 standard deals with the attainment of functional safety for road vehicles and therefore also includes measures and recommendations for the development of software functions [37]. The automotive safety integrity level (ASIL) classifies hazardous situations caused by system or component failures in four groups.…”

Section: Enhanced Error Detection Mechanismsmentioning

confidence: 99%

Autonomous Driving—A Crash Explained in Detail

et al. 2019

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Since 2017, a research team from the Technical University of Munich has developed a software stack for autonomous driving. The software was used to participate in the Roborace Season Alpha Championship. The championship aims to achieve autonomous race cars competing with different software stacks against each other. In May 2019, during a software test in Modena, Italy, the greatest danger in autonomous driving became reality: A minor change in environmental influences led an extensively tested software to crash into a barrier at speed. Crashes with autonomous vehicles have happened before but a detailed explanation of why software failed and what part of the software was not working correctly is missing in research articles. In this paper we present a general method that can be used to display an autonomous vehicle disengagement to explain in detail what happened. This method is then used to display and explain the crash from Modena. Firstly a brief introduction into the modular software stack that was used in the Modena event, consisting of three individual parts—perception, planning, and control—is given. Furthermore, the circumstancescausing the crash are elaborated in detail. By presented and explaining in detail which softwarepart failed and contributed to the crash we can discuss further software improvements. As a result, we present necessary functions that need to be integrated in an autonomous driving software stack to prevent such a vehicle behavior causing a fatal crash. In addition we suggest an enhancement of the current disengagement reports for autonomous driving regarding a detailed explanation of the software part that was causing the disengagement. In the outlook of this paper we present two additional software functions for assessing the tire and control performance of the vehicle to enhance the autonomous.

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Application of Functional Safety in Autonomous Vehicles Using ISO 26262 Standard: A Survey

Cited by 16 publications

References 12 publications

Infrastructure-Based Pedestrian Risk Tagging Methodology to Support AV Risk Assessment

Infrastructure-Based Pedestrian Risk Tagging Methodology to Support AV Risk Assessment

Enabling Unit Testing of Already-Integrated AI Software Systems: The Case of Apollo for Autonomous Driving

Autonomous Driving—A Crash Explained in Detail

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