As the deployment of AI agents in Automated Driving Systems (ADS) becomes increasingly prevalent, ensuring their safety and reliability is of paramount importance. This paper presents a novel approach to enhance the safety assurance of automated driving systems by employing formal contracts to specify and refine system-level properties. The proposed framework leverages formal methods to specify contracts that capture the expected behavior of perception and control components in ADS. These contracts serve as a basis for systematically validating system behavior against safety requirements during design and testing phases. We demonstrate the efficacy of our approach using the CARLA simulator and an off-the-shelf AI agent. By monitoring the components contracts on the ADS simulation, we could identify not only the cause of system failures, but also the situations which could lead to a system failure, facilitating the debugging and maintenance of the AI agents.
As the deployment of AI agents in Automated Driving Systems (ADS) becomes increasingly prevalent, ensuring their safety and reliability is of paramount importance. This paper presents a novel approach to enhance the safety assurance of automated driving systems by employing formal contracts to specify and refine system-level properties. The proposed framework leverages formal methods to specify contracts that capture the expected behavior of perception and control components in ADS. These contracts serve as a basis for systematically validating system behavior against safety requirements during design and testing phases. We demonstrate the efficacy of our approach using the CARLA simulator and an off-the-shelf AI agent. By monitoring the components contracts on the ADS simulation, we could identify not only the cause of system failures, but also the situations which could lead to a system failure, facilitating the debugging and maintenance of the AI agents.
Leveraging Contracts for Failure Monitoring and Identification in Automated Driving Systems / Goyal, Srajan; Griggio, Alberto; Tonetta, Stefano. - 15280:(2025), pp. 441-460. ( 22nd International Conference on Software Engineering and Formal Methods, SEFM 2024 Aveiro, Portugal November 6-8, 2024) [10.1007/978-3-031-77382-2_25].
Leveraging Contracts for Failure Monitoring and Identification in Automated Driving Systems
Goyal, Srajan
Primo
;Griggio, Alberto;Tonetta, Stefano
2025-01-01
Abstract
As the deployment of AI agents in Automated Driving Systems (ADS) becomes increasingly prevalent, ensuring their safety and reliability is of paramount importance. This paper presents a novel approach to enhance the safety assurance of automated driving systems by employing formal contracts to specify and refine system-level properties. The proposed framework leverages formal methods to specify contracts that capture the expected behavior of perception and control components in ADS. These contracts serve as a basis for systematically validating system behavior against safety requirements during design and testing phases. We demonstrate the efficacy of our approach using the CARLA simulator and an off-the-shelf AI agent. By monitoring the components contracts on the ADS simulation, we could identify not only the cause of system failures, but also the situations which could lead to a system failure, facilitating the debugging and maintenance of the AI agents.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione
