Verifying Collision Risk Estimation using Autonomous Driving Scenarios Derived from a Formal Model

Horel, Jean-Baptiste; Ledent, Philippe; Marsso, Lina; Muller, Lucie; Laugier, Christian; Mateescu, Radu; Paigwar, Anshul; Renzaglia, Alessandro; Serwe, Wendelin

doi:10.1007/s10846-023-01808-3

Cited by 3 publications

(4 citation statements)

References 62 publications

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“…Some references focus on the ethical and numerical aspects of risk evaluation [59], dynamic safety enhancements using anomaly detection techniques [60], and alternative techniques for self-driven vehicles [61]. The use of formal verification tools for perception components in AVs [62], challenges in adverse weather conditions [63], and comprehensive control approaches for collision avoidance [64] are explored as well. Further areas of exploration involve the necessity for robust V2X blockchains [135], challenges related to LiDAR safety and imaging costs [136], methods for localization adaptability [137], as well as geolocalization approaches discussed in [138].…”

Section: Discussionmentioning

confidence: 99%

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Collision Risk in Autonomous Vehicles: Classification, Challenges, and Open Research Areas

Goudarzi,

Hassanzadeh

2024

Vehicles

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When car following is controlled by human drivers (i.e., by their behavior), the traffic system does not meet stability conditions. In order to ensure the safety and reliability of self-driving vehicles, an additional hazard warning system should be incorporated into the adaptive control system in order to prevent any possible unavoidable collisions. The time to contact is a reasonable indicator of potential collisions. This research examines systems and solutions developed in this field to determine collision times and uses various alarms in self-driving cars that prevent collisions with obstacles. In the proposed analysis, we have tried to classify the various techniques and methods, including image processing, machine learning, deep learning, sensors, and so on, based on the solutions we have investigated. Challenges, future research directions, and open problems in this important field are also highlighted in the paper.

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Section: Discussionmentioning

confidence: 99%

“…Hortel et al have used a formal model for collision risk assessment of AVs [62]. In the work [63], a potential risk assessment for the safe driving of autonomous vehicles under occluded vision has been investigated.…”

Section: Miscellaneous Techniquesmentioning

confidence: 99%

Collision Risk in Autonomous Vehicles: Classification, Challenges, and Open Research Areas

Goudarzi,

Hassanzadeh

2024

Vehicles

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“…Verification is a critical step in ensuring the efficacy of safety measures as the safety of manufacturing processes can be verified before the start of production on the factory floor. For verification of safety, model-based approaches leverage formal methods in traditional and autonomous control systems [21,34,35]. By adding safety models to the system model, agents can use these models to reason about safe behaviour and adapt to the new manufacturing requirements.…”

Section: Related Workmentioning

confidence: 99%

“…In this section, To validate the efficacy of the suggested method, a Plug & Produce manufacturing scenario is formalized. Model checking is employed for formal verification of the strategy [35]. This section is divided into five subsections.…”

Section: Formal Verification Of a Manufacturing Scenariomentioning

confidence: 99%

Safe and Reconfigurable Manufacturing: Safety Aware Multi-Agent Control for Plug & Produce System.

Massouh,

Danielsson,

Lennartson

et al. 2024

Preprint

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Plug & Produce is a concept that is envisioned to revolutionise manufacturing by enabling the seamless integration of machines into production processes without extensive programming, offering increased flexibility, and faster production ramp-up times after reconfiguration. This concept implies a smart and advanced system control architecture typically achieved with Multi-Agent Systems (MAS). In the context of moving towards more human participation in smart manufacturing, the realization of the Plug & Produce concept depends on enabling the safe operation of the manufacturing system which is the focus of this paper. The main challenge to safe operation comes from autonomous decision-making, as agents in the multi-agent system are typically not aware of the risk consequence of their behaviour. Another challenge is the difficulty of perceiving the exact behaviour of the system which leads to the implementation of overly restrictive safety measures. This in turn limits the system’s flexibility and ability to make decisions for efficient production. This paper proposes a method utilizing multi-agent control of Plug & Produce to conduct automatic safety analysis and reason the task allocations to avoid risks. The benefit of this method is it enables generating control actions in compliance with safety requirements during operation. Also, the method eliminates the need for overly restrictive safety measures, this flexibility allows for more effective utilization of equipment while maintaining safety requirements. Illustrated through a manufacturing scenario with two different reconfigurations, safety requirements are proven satisfied by the formal verification of the proposed method using the model checker NuSMV. The formal verification results confirm the benefits of the proposed method.

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Safe and reconfigurable manufacturing: safety aware multi-agent control for Plug & Produce system

Massouh,

Danielsson,

Lennartson

et al. 2024

Int J Adv Manuf Technol

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Plug & Produce aims to revolutionize manufacturing by enabling seamless machine integration into production processes without extensive programming. This concept, leveraging multi-agent systems (MAS), offers increased flexibility and faster production ramp-up times after reconfiguration. As automated manufacturing moves towards greater human integration, this paper addresses safe operation within the Plug & Produce concept. The main safety challenge arises from autonomous decision-making, as agents in the MAS lack awareness of the risk consequences of their behavior. Additionally, the difficulty of perceiving the system’s exact behavior leads to the implementation of overly restrictive safety measures. This limits the system’s flexibility and ability to make decisions for efficient production. This paper proposes a method utilizing multi-agent control to conduct automatic safety analysis and reason task allocations to avoid risks. The method’s benefits are the generation of control actions that comply with safety requirements during operation, eliminating the need for overly restrictive safety measures and allowing more effective equipment utilization. The method’s benefit is illustrated through a manufacturing scenario with two different configurations: one using a hazardous machine and the other using a less hazardous one. Formal verification using the model checker NuSMV demonstrated that safety requirements were satisfied in both configurations, without the need for manual modifications of the safety control system after reconfiguration. The results for this specific manufacturing scenario showed that there are more reachable states (20 states) in the safer second configuration, compared to the first configuration (16 states). This means that the presented control strategy dynamically adjusts the system’s behavior to confirm safety. Hence, this method maintains safety without fixed safety rules that limit the operations.

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Verifying Collision Risk Estimation using Autonomous Driving Scenarios Derived from a Formal Model

Cited by 3 publications

References 62 publications

Collision Risk in Autonomous Vehicles: Classification, Challenges, and Open Research Areas

Collision Risk in Autonomous Vehicles: Classification, Challenges, and Open Research Areas

Safe and Reconfigurable Manufacturing: Safety Aware Multi-Agent Control for Plug & Produce System.

Safe and reconfigurable manufacturing: safety aware multi-agent control for Plug & Produce system

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Verifying Collision Risk Estimation using Autonomous Driving Scenarios Derived from a Formal Model

Cited by 3 publications

References 62 publications

Collision Risk in Autonomous Vehicles: Classification, Challenges, and Open Research Areas

Collision Risk in Autonomous Vehicles: Classification, Challenges, and Open Research Areas

Safe and Reconfigurable Manufacturing: Safety Aware Multi-Agent Control for Plug &amp; Produce System.

Safe and reconfigurable manufacturing: safety aware multi-agent control for Plug & Produce system

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Product

Resources

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Safe and Reconfigurable Manufacturing: Safety Aware Multi-Agent Control for Plug & Produce System.