A Conceptual Safety Supervisor Definition and Evaluation Framework for Autonomous Systems

Feth, Patrik; Schneider, Daniel; Adler, Rasmus

doi:10.1007/978-3-319-66266-4_9

Cited by 18 publications

(12 citation statements)

References 22 publications

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“…By contrast, methods relying on formal and deterministic fundamentals can provide guarantees based on imposed requirements. Among them are reachable sets [5,34,35], runtime verification [36], and metric-based approaches [37], including the RSS model [17]. However, some of these approaches tailored to a specific software lack flexibility and cannot be bundled with other software components or approaches.…”

Section: Related Workmentioning

confidence: 99%

A verification framework for behavioral safety of self‐driving cars

Lyu

Zhang

et al. 2022

IET Intelligent Trans Sys

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While self‐driving cars have already been widely investigated and achieved spectacular progress, a major obstacle in applications is the great difficulty in providing formal guarantees about their behaviors. Since the environment of the self‐driving is usually not known beforehand and highly uncertain, classical verification approaches cannot be applied to guarantee safety. To cope with any traffic situation, a novel online verification framework is presented for verifying behavioral safety of self‐driving cars. The framework is based on the proposed five safety considerations: new longitudinal and lateral safe distances, lane changes, overtaking and how to face new traffic participants. Different from the previous verification considerations, this verification framework allows actual behaviors of self‐driving cars to be temporarily inconsistent with the popular strict safe distance. As long as the self‐driving car respects the minimum safe distance calculated by our technique and executes improvement behaviors to restore the safe distance, it is still believed that the predictive behavior is safe. The framework can easily be integrated to existing self‐driving systems and evaluate different indicators involving the steering angle, acceleration and braking. The benefits of the framework in different urban scenarios of the CARLA simulator and real traffic data provided by the NGSIM project are demonstrated. Results show that the technology can successfully detect unsafe behaviors and provide effective measures to avoid potential collisions.

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Section: Related Workmentioning

confidence: 99%

A verification framework for behavioral safety of self‐driving cars

Lyu

Zhang

et al. 2022

IET Intelligent Trans Sys

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“…With 11 primary studies, ML quality assurance frameworks constitute another important topic. These frameworks normally focus on specific quality aspects of ML products, such as allowability, achievability, robustness, avoidability and improvability [147], safety [46,135], specific safety issues like forward collision mitigation based on the ISO 22839 standard [55], security [50], algorithmic auditing [161], robustness diversity [187], data validation [30], or the reconciliation of product and service aspects [141]. Other approaches focus on continuous quality assurance with simulations [12] and on run-time monitoring to manage identified risks [106].…”

Section: Software Quality (59 Studies)mentioning

confidence: 99%

Software Engineering for AI-Based Systems: A Survey

Martínez-Fernández,

Bogner,

Franch

et al. 2021

Preprint

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AI-based systems are software systems with functionalities enabled by at least one AI component (e.g., for image-and speechrecognition, and autonomous driving). AI-based systems are becoming pervasive in society due to advances in AI. However, there is limited synthesized knowledge on Software Engineering (SE) approaches for building, operating, and maintaining AI-based systems.To collect and analyze state-of-the-art knowledge about SE for AI-based systems, we conducted a systematic mapping study. We considered 248 studies published between January 2010 and March 2020. SE for AI-based systems is an emerging research area, where more than 2/3 of the studies have been published since 2018. The most studied properties of AI-based systems are dependability and safety. We identified multiple SE approaches for AI-based systems, which we classified according to the SWEBOK areas. Studies related to software testing and software quality are very prevalent, while areas like software maintenance seem neglected. Data-related issues are the most recurrent challenges. Our results are valuable for: researchers, to quickly understand the state of the art and learn which topics need more research; practitioners, to learn about the approaches and challenges that SE entails for AI-based systems; and, educators, to bridge the gap among SE and AI in their curricula. CCS Concepts: • Software and its engineering → Software creation and management; • Computing methodologies → Machine learning;

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“…While offline methods (e.g. formal offline approval) cannot cope with continuing learning during runtime, online monitoring methods are considered a promising approach [8], [21]. Parallel online monitoring (also known as doer/checker principle) goes well with the principles of ASILdecomposition in the current version of the ISO 26262 [11], [22].…”

Section: A Standardsmentioning

confidence: 99%

Online Verification Enabling Approval of Driving Functions—Implementation for a Planner of an Autonomous Race Vehicle

Stahl

Diermeyer

2021

IEEE Open J. Intell. Transp. Syst.

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Safety guarantees and regulatory approval for autonomous vehicles remain an ongoing challenge. In particular, software that is frequently adapted or contains complex, non-transparent components, such as artificial intelligence, is exceeding the limits of safety standards. This paper presents a detailed implementation of an online verification module -the Supervisor -that copes with these challenges. The presented implementation focuses on autonomous race vehicles without loss of generality. Following an identified holistic list of safety-relevant requirements for a trajectory, metrics are developed to monitor whether the trajectory can safely be executed. To evaluate safety with respect to dynamic objects in a semi-structured and highly dynamic racing environment, rule-based reachable sets are presented. As a result, the pure reachable set is further constrained by applicable regulations. Real-time capability and effectiveness are demonstrated in fault-injected scenario-based tests and on real-world run data. The implemented Supervisor will be publicly available on GitHub.

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A Conceptual Safety Supervisor Definition and Evaluation Framework for Autonomous Systems

Cited by 18 publications

References 22 publications

A verification framework for behavioral safety of self‐driving cars

A verification framework for behavioral safety of self‐driving cars

Software Engineering for AI-Based Systems: A Survey

Online Verification Enabling Approval of Driving Functions—Implementation for a Planner of an Autonomous Race Vehicle

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