A Survey on an Emerging Safety Challenge for Autonomous Vehicles:Safety of the Intended Functionality

As the complexity of autonomous vehicles(AVs)continues to increase and artificial intelligence algorithms are becoming increasingly ubiquitous,a novel safety concern known as the safety of the intended functionality(SOTIF)has emerged,presenting significant challenges to the widespread deployment of AVs.SOTIF focuses on issues arising from the functional insufficiencies of the AVs’intended functionality or its implementation,apart from conventional safety considerations.From the systems engineering standpoint,this study offers a comprehensive exploration of the SOTIF landscape by reviewing academic research,practical activities,challenges,and perspectives across the development,verification,validation,and operation phases.Academic research encompasses system-level SOTIF studies and algorithm-related SOTIF issues and solutions.Moreover,it encapsulates practical SOTIF activities undertaken by corporations,government entities,and academic institutions spanning international and Chinese contexts,focusing on the overarching methodologies and practices in different phases.Finally,the paper presents future challenges and outlook pertaining to the development,verification,validation,and operation phases,motivating stakeholders to address the remaining obstacles and challenges.

An Adversarial Attack on Salient Regions of Traffic Sign

The state-of-the-art deep neural networks are vulnerable to the attacks of adversarial examples with small-magnitude per-turbations.In the field of deep-learning-based automated driving,such adversarial attack threats testify to the weakness of AI models.This limitation can lead to severe issues regarding the safety of the intended functionality(SOTIF)in automated driving.From the perspective of causality,the adversarial attacks can be regarded as confounding effects with spurious corre-lations established by the non-causal features.However,few previous research works are devoted to building the relationship between adversarial examples,causality,and SOTIF.This paper proposes a robust physical adversarial perturbation genera-tion method that aims at the salient image regions of the targeted attack class with the guidance of class activation mapping(CAM).With the utilization of CAM,the maximization of the confounding effects can be achieved through the intermediate variable of the front-door criterion between images and targeted attack labels.In the simulation experiment,the proposed method achieved a 94.6%targeted attack success rate(ASR)on the released dataset when the speed-speed-limit-60 km/h(speed-limit-60)signs could be attacked as speed-speed-limit-80 km/h(speed-limit-80)signs.In the real physical experiment,the targeted ASR is 75%and the untargeted ASR is 100%.Besides the state-of-the-art attack result,a detailed experiment is implemented to evaluate the performance of the proposed method under low resolutions,diverse optimizers,and multifarious defense methods.The code and data are released at the repository:https://github.com/yebin999/rp2-with-cam.

SOTIF risk mitigation based on unified ODD monitoring for autonomous vehicles

Purpose–The purpose of this paper is to design a unified operational design domain(ODD)monitoring framework for mitigating Safety of the Intended Functionality(SOTIF)risks triggered by vehicles exceeding ODD boundaries in complex traffic scenarios.Design/methodology/approach–A unified model of ODD monitoring is constructed,which consists of three modules:weather condition monitoring for unusual weather conditions,such as rain,snow and fog;vehicle behavior monitoring for abnormal vehicle behavior,such as traffic rule violations;and road condition monitoring for abnormal road conditions,such as road defects,unexpected obstacles and slippery roads.Additionally,the applications of the proposed unified ODD monitoring framework are demonstrated.The practicability and effectiveness of the proposed unified ODD monitoring framework for mitigating SOTIF risk are verified in the applications.Findings–First,the application of weather condition monitoring demonstrates that the autonomous vehicle can make a safe decision based on the performance degradation of Lidar on rainy days using the proposed monitoring framework.Second,the application of vehicle behavior monitoring demonstrates that the autonomous vehicle can properly adhere to traffic rules using the proposed monitoring framework.Third,the application of road condition monitoring demonstrates that the proposed unified ODD monitoring framework enables the ego vehicle to successfully monitor and avoid road defects.Originality/value–The value of this paper is that the proposed unified ODD monitoring framework establishes a new foundation for monitoring and mitigating SOTIF risks in complex traffic environments.