Encoding and monitoring responsibility sensitive safety rules for automated vehicles in signal temporal logic

Hekmatnejad, Mohammad; Yaghoubi, Shakiba; Dokhanchi, Adel; Amor, Heni Ben; Shrivastava, Aviral; Karam, Lina J.; Fainekos, Georgios

doi:10.1145/3359986.3361203

Cited by 50 publications

(29 citation statements)

References 10 publications

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“…All the restrictions are lifted when an unsafe situation becomes safe again. For more details see [19] and [22]. In the rest of this section, we represent a slightly modified version of the formula ϕ lat,lon resp from the Remark III.3 in [22].…”

Section: Rssmentioning

confidence: 99%

“…For more details see [19] and [22]. In the rest of this section, we represent a slightly modified version of the formula ϕ lat,lon resp from the Remark III.3 in [22]. For the sake of space and readability, for the lateral requirements, we assumed that the ego car is driving on the left side of the other car.…”

Section: Rssmentioning

confidence: 99%

“…In order to achieve that, RSS specifications are formalized into STL to enable formal, algorithmic reasoning over them. In [22], RSS specifications were utilized to monitor naturalistic traffic data obtained from the CommonRoad library [23]. In this paper, the driving scenarios are generated through simulation.…”

Section: Introductionmentioning

confidence: 99%

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Search-based Test-CASe Generation by Monitoring Responsibility Safety Rules

Hekmatnejad

Hoxha

Fainekos

2020

2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)

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The safety of Automated Vehicles (AV) as Cyber-Physical Systems (CPS) depends on the safety of their consisting modules (software and hardware) and their rigorous integration. Deep Learning is one of the dominant techniques used for perception, prediction and decision making in AVs. The accuracy of predictions and decision-making is highly dependant on the tests used for training their underlying deep-learning. In this work, we propose a method for screening and classifying simulation-based driving test data to be used for training and testing controllers. Our method is based on monitoring and falsification techniques, which lead to a systematic automated procedure for generating and selecting qualified test data. We used Responsibility Sensitive Safety (RSS) rules as our qualifier specifications to filter out the random tests that do not satisfy the RSS assumptions. Therefore, the remaining tests cover driving scenarios that the controlled vehicle does not respond safely to its environment. Our framework is distributed with the publicly available S-TALIRO and Sim-ATAV tools.

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Section: Rssmentioning

confidence: 99%

Section: Rssmentioning

confidence: 99%

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Search-based Test-CASe Generation by Monitoring Responsibility Safety Rules

Hekmatnejad

Hoxha

Fainekos

2020

2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)

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“…While spatial/topological and physical (low-level) aspects are handled, a precise formal language expressing constraints and properties of the assumed trajectory planning is missing, although its need is mentioned. A partial answer is provided in [20], aiming to monitor RSS rules by encoding signal temporal logic (STL); again this work lacks a precise identification of zero-crossing events, as well as constraint modularity.…”

Section: Related Workmentioning

confidence: 99%

Multiform Logical Time & Space for Mobile Cyber-Physical System With Automated Driving Assistance System

Liu

Simone

Chen

et al. 2020

2020 27th Asia-Pacific Software Engineering Conference (APSEC)

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“…An alternative viewpoint is presented by responsibility-sensitive safety [7,11,24,27], in the context of autonomous driving, where some hard safety constraints are replaced by a notion of not causing a crash and avoiding one whenever possible. This is particularly well-suited for scenarios where some of the agents are humancontrolled, which can lead to unpredictable behaviors.…”

Section: Introductionmentioning

confidence: 99%

Compositional safety rules for inter-triggering hybrid automata

Rutledge

Chou

Özay

2021

Proceedings of the 24th International Conference on Hybrid Systems: Computation and Control

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In this paper, we present a compositional condition for ensuring safety of a collection of interacting systems modeled by intertriggering hybrid automata (ITHA). ITHA is a modeling formalism for representing multi-agent systems in which each agent is governed by individual dynamics but can also interact with other agents through triggering actions. These triggering actions result in a jump/reset in the state of other agents according to a global resolution function. A sufficient condition for safety of the collection, inspired by responsibility-sensitive safety, is developed in two parts: self-safety relating to the individual dynamics, and responsibility relating to the triggering actions. The condition relies on having an over-approximation method for the resolution function. We further show how such over-approximations can be obtained and improved via communication. We use two examples, a job scheduling task on parallel processors and a highway driving example, throughout the paper to illustrate the concepts. Finally, we provide a comprehensive evaluation on how the proposed condition can be leveraged for several multi-agent control and supervision examples.

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Encoding and monitoring responsibility sensitive safety rules for automated vehicles in signal temporal logic

Cited by 50 publications

References 10 publications

Search-based Test-CASe Generation by Monitoring Responsibility Safety Rules

Search-based Test-CASe Generation by Monitoring Responsibility Safety Rules

Multiform Logical Time & Space for Mobile Cyber-Physical System With Automated Driving Assistance System

Compositional safety rules for inter-triggering hybrid automata

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