A Systematic Literature Review on Safety of the Intended Functionality for Automated Driving Systems
In the automobile industry, ensuring the safety of automated vehicles equipped with the Automated Driving System (ADS) is becoming a significant focus due to the increasing development and deployment of automated driving. Automated driving depends on sensing both the external and internal environments of a vehicle, utilizing perception sensors and algorithms, and Electrical/Electronic (E/E) systems for situational awareness and response. ISO 21448 is the standard for Safety of the Intended Functionality (SOTIF) that aims to ensure that the ADS operate safely within their intended functionality. SOTIF focuses on preventing or mitigating potential hazards that may arise from the limitations or failures of the ADS, including hazards due to insufficiencies of specification, or performance insufficiencies, as well as foreseeable misuse of the intended functionality. However, the challenge lies in ensuring the safety of vehicles despite the limited availability of extensive and systematic literature on SOTIF. To address this challenge, a Systematic Literature Review (SLR) on SOTIF for the ADS is performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The objective is to methodically gather and analyze the existing literature on SOTIF. The major contributions of this paper are: (i) presenting a summary of the literature by synthesizing and organizing the collective findings, methodologies, and insights into distinct thematic groups, and (ii) summarizing and categorizing the acknowledged limitations based on data extracted from an SLR of 51 research papers published between 2018 and 2023. Furthermore, research gaps are determined, and future research directions are proposed.
Code (0)
등록된 구현이 없습니다.
Tasks
Systematic Literature ReviewMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Safety of the Intended Driving Behavior Using Rulebooks
Autonomous Vehicles (AVs) are complex systems that drive in uncertain environments and potentially navigate unforeseeable situations. Safety of these systems requires not only an absence of malfunctions but also high per…
Autonomous VehiclesNavigateA Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety
Autonomous Vehicles (AV) are expected to bring considerable benefits to society, such as traffic optimization and accidents reduction. They rely heavily on advances in many Artificial Intelligence (AI) approaches and tec…
Autonomous VehiclesSystematic Literature ReviewSafety by Measurement: A Systematic Literature Review of AI Safety Evaluation Methods
As frontier AI systems advance toward transformative capabilities, we need a parallel transformation in how we measure and evaluate these systems to ensure safety and inform governance. While benchmarks have been the pri…
Red TeamingSystematic Literature ReviewA Systematic Literature Review of Spatio-Temporal Graph Neural Network Models for Time Series Forecasting and Classification
In recent years, spatio-temporal graph neural networks (GNNs) have attracted considerable interest in the field of time series analysis, due to their ability to capture dependencies among variables and across time points…
Graph Neural NetworkSystematic Literature ReviewTime SeriesTime Series Analysis+2Diffusion Model for Planning: A Systematic Literature Review
Diffusion models, which leverage stochastic processes to capture complex data distributions effectively, have shown their performance as generative models, achieving notable success in image-related tasks through iterati…
Autonomous DrivingDenoisingmodelSystematic Literature Review