Automated Driving in Complex Real-World Scenarios using a Scalable Risk-Based Behavior Generation Framework

Probst, Malte; Wenzel, Raphael; Puphal, Tim; Komuro, Misa; Weisswange, Thomas H.; Steinhardt, Nico; Bolder, Bram; Flade, Benedict; Sakamoto, Yosuke; Yasui, Yoshiaki; Eggert, Julian

doi:10.1109/itsc48978.2021.9564440

Cited by 3 publications

(1 citation statement)

References 26 publications

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“…Nonetheless, efforts to close loopholes can begin with the AI implementations of human organizations being set up in appropriate systems architectures, such as typical systems architectures for automated driving which include real-time risk estimation. As shown in Figure 2, such architectures can include modules for sensing, understanding, predicting, planning, and acting [78], and can be situated within nested sources of safety requirements including laws, regulations, and standards. In efforts to close loopholes, systems architecture can be designed to incentivize people to make decisions that prioritize accident avoidance.…”

Section: Ai Implementationmentioning

confidence: 99%

Safety of Human–Artificial Intelligence Systems: Applying Safety Science to Analyze Loopholes in Interactions between Human Organizations, Artificial Intelligence, and Individual People

Fox,

Victores

2024

Informatics

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Loopholes involve misalignments between rules about what should be done and what is actually done in practice. The focus of this paper is loopholes in interactions between human organizations’ implementations of task-specific artificial intelligence and individual people. The importance of identifying and addressing loopholes is recognized in safety science and in applications of AI. Here, an examination is provided of loophole sources in interactions between human organizations and individual people. Then, it is explained how the introduction of task-specific AI applications can introduce new sources of loopholes. Next, an analytical framework, which is well-established in safety science, is applied to analyses of loopholes in interactions between human organizations, artificial intelligence, and individual people. The example used in the analysis is human–artificial intelligence systems in gig economy delivery driving work.

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Section: Ai Implementationmentioning

confidence: 99%

Safety of Human–Artificial Intelligence Systems: Applying Safety Science to Analyze Loopholes in Interactions between Human Organizations, Artificial Intelligence, and Individual People

Fox,

Victores

2024

Informatics

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Addressing inequal risk exposure in the development of automated vehicles

Dietrich

2021

Ethics Inf Technol

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Automated vehicles (AVs) are expected to operate on public roads, together with non-automated vehicles and other road users such as pedestrians or bicycles. Recent ethical reports and guidelines raise worries that AVs will introduce injustice or reinforce existing social inequalities in road traffic. One major injustice concern in today’s traffic is that different types of road users are exposed differently to risks of corporal harm. In the first part of the paper, we discuss the responsibility of AV developers to address existing injustice concerns regarding risk exposure as well as approaches on how to fulfill the responsibility for a fairer distribution of risk. In contrast to popular approaches on the ethics of risk distribution in unavoidable accident cases, we focus on low and moderate risk situations, referred to as routine driving. For routine driving, the obligation to distribute risks fairly must be discussed in the context of risk-taking and risk-acceptance, balancing safety objectives of occupants and other road users with driving utility. In the second part of the paper, we present a typical architecture for decentralized automated driving which contains a dedicated module for real-time risk estimation and management. We examine how risk estimation modules can be adjusted and parameterized to redress some inequalities.

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