Quantitative Safety Analysis of a Coordinated Emergency Brake Protocol for Vehicle Platoons

Bergenhem, Carl; Meinke, Karl; Ström, Fabian · 2018 · Crossref

DOI: 10.1007/978-3-030-03424-5_26

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Summary

This paper presents a methodology for the quantitative safety analysis of cooperative cyber-physical systems (Co-CPS), specifically applied to vehicle platoons equipped with a Coordinated Emergency Brake Protocol (CEBP). The research addresses the challenge of determining safe operational parameters, such as minimum inter-vehicle time headways, in systems where static analysis is infeasible due to system complexity and black-box components. The authors aim to estimate the minimum global time headway that ensures collision-free braking under various communication conditions, including stochastic packet loss. The study employs Learning-Based Testing (LBT), a technique that combines machine learning with model checking to infer behavioral models of a System Under Test (SUT). The authors utilized a platoon simulator integrating vehicle dynamics, control algorithms, and a communication model based on empirical data from road tests of vehicle-to-vehicle (V2V) communication. These road tests, conducted with a four-truck platoon, revealed significant packet error rates and consecutive packet losses, which were modeled stochastically in the simulation. The CEBP algorithm coordinates braking by propagating requests from the rear of the platoon forward, ensuring the last vehicle brakes first, with timeout mechanisms to handle communication failures. The LBT framework uses active automaton learning to construct state machine models of the platoon’s behavior, which are then subjected to model checking against safety requirements expressed in linear temporal logic. The primary finding is the estimation of the minimum safe global time headway for platoons of varying sizes under both perfect communication and stochastic packet loss scenarios. The results demonstrate that the minimum global time headway scales well with respect to platoon size. The methodology successfully identified safety boundaries by iteratively refining estimate intervals using binary chop search within the LBT framework. The authors highlight that their approach offers advantages over traditional Monte-Carlo estimation by providing explicit behavioral models that allow for the analysis of complex global properties like safety and liveness, rather than relying solely on execution traces. The significance of this work lies in providing a scalable, automated method for estimating safety-critical parameters in complex Co-CPS where full static verification is impractical. By integrating empirical communication data with learning-based testing, the study offers a realistic assessment of safety margins in vehicle platooning. The authors conclude that this methodology can be extended to other cyber-physical system-of-systems, supporting the development of safer cooperative technologies by quantifying the impact of environmental factors like communication reliability on system safety.

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