Statistical Verification Framework for Platooning System of Systems with Uncertainty

Hyun, Sangwon; Song, Jungsuk; Shin, Seungchyul; Bae, Doo‐Hwan

doi:10.1109/apsec48747.2019.00037

Cited by 7 publications

(4 citation statements)

References 14 publications

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“…In addition, trajectories with constant cruising speed followed by an emergency braking manoeuvre were added to the sample as well in order to also consider this unlikely, but safetyrelevant case. Alternatively, in [29] the authors present a framework that automatically generates platooning scenarios. Such a system or similar could of course also be implemented here.…”

Section: Consideration Of Uncertainties In Stochastic Optimisationmentioning

confidence: 99%

Stochastic Optimisation for the Design of Energy-Efficient Controllers for Cooperative Truck Platoons

2022

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Cooperative adaptive cruise control systems have the potential to improve fuel efficiency and safety. However, due to the large amount of uncertainties, which are encountered in platooning applications, typical controller calibrations are often not reliable. Therefore, in order to ensure a satisfying performance in the presence of various information topologies and relevant uncertainties such as errors in data transmission or extreme manoeuvres of the lead vehicle, a risk-averse stochastic optimisation approach for controller calibration is suggested and demonstrated for a pre-existing control scheme. Realistic vehicle dynamics simulation experiments with a prescribed set of uncertainties, such as transmission delays and different vehicle parameters, are performed. The results show that the collision probability and energy consumption are reduced by the risk-averse calibration of the controller and its spacing policy compared with classical calibration which assumes perfect communication.

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Section: Consideration Of Uncertainties In Stochastic Optimisationmentioning

confidence: 99%

Stochastic Optimisation for the Design of Energy-Efficient Controllers for Cooperative Truck Platoons

2022

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“…Hyun et al [12] have presented a statistical verification framework known as StarPlateS for a platooning system. The proposed framework addresses uncertainties in a platooning system by implanting three modules; (a) a scenario generation module that generates random configurations and scenarios using the condition-based algorithm and addresses internal uncertainties, (b) a simulation module that implements a platooning system on generated configurations and scenarios using a VENTOS simulator, (c) a verification module applies a model checking algorithm to verify the rates of specific goals.…”

Section: Related Workmentioning

confidence: 99%

“…OMNET++ [28] We select VENTOS and its platooning system because (1) it is an open-s lator that includes SUMO as a traffic simulator. For instance, it is quite easy traffic maps for simulations using SUMO than other available simulators, (2) a mature simulator, and several studies have already used it [12,30].…”

Section: Verification With Ventosmentioning

confidence: 99%

Fault-Tolerance by Resilient State Transition for Collaborative Cyber-Physical Systems

2021

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Collaborative Cyber-Physical Systems (CCPS) are systems where several individual cyber-physical systems collaborate to perform a single task. The safety of a single Cyber-Physical System (CPS) can be achieved by applying a safety mechanism and following standard processes defined in ISO 26262 and IEC 61508. However, due to heterogeneity, complexity, variability, independence, self-adaptation, and dynamic nature, functional operations for CCPS can threaten system safety. In contrast to fail-safe systems, where, for instance, the system leads to a safe state when an actuator shuts down due to a fault, the system has to be fail-operational in autonomous driving cases, i.e., a shutdown of a platooning member vehicle during operation on the road is unacceptable. Instead, the vehicle should continue its operation with degraded performance until a safe state is reached or returned to its original state in case of temporal faults. Thus, this paper proposes an approach that considers the resilient behavior of collaborative systems to achieve the fail-operational goal in autonomous platooning systems. First, we extended the state transition diagram and introduced additional elements such as failures, mitigation strategies, and safe exit to achieve resilience in autonomous platooning systems. The extended state transition diagram is called the Resilient State Transition Diagram (R-STD). Second, an autonomous platooning system’s perception, communication, and ego-motion failures are modeled using the proposed R-STD to check its effectiveness. Third, VENTOS simulator is used to verify the resulting resilient transitions of R-STD in a simulation environment. Results show that a resilient state transition approach achieves the fail-operational goal in the autonomous platooning system.

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“…Mallozzi et al [10] and Van and Geihs [11] used the UPPAAL [29] model checker to guarantee the platoon's functions never violate safety properties, including platoon forming, following, leaving, and negotiation behaviors. Focusing on platoon's internal and external safety uncertainties, Hyun et al [30] proposed a statistical verification framework to automatically generate scenario configurations with uncertain factors, bypassing the state explosion problem.…”

Section: Safety Verification Of Platoon Behaviorsmentioning

confidence: 99%

A Multi-Agent Spatial Logic for Scenario-Based Decision Modeling and Verification in Platoon Systems

Huang

Shi

et al. 2021

J. Comput. Sci. Technol.

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To cater for the scenario of coordinated transportation of multiple trucks on the highway, a platoon system for autonomous driving has been extensively explored in the industry. Before such a platoon is deployed, it is necessary to ensure the safety of its driving behavior, whereby each vehicle's behavior is commanded by the decision-making function whose decision is based on the observed driving scenario. However, there is currently a lack of verification methods to ensure the reliability of the scenario-based decision-making process in the platoon system. In this paper, we focus on the platoon driving scenario, whereby the platoon is composed of intelligent heavy trucks driving on cross-sea highways. We propose a formal modeling and verification approach to provide safety assurance for platoon vehicles' cooperative driving behaviors. The existing Multi-Lane Spatial Logic (MLSL) with a dedicated abstract model can express driving scene spatial properties and prove the safety of multi-lane traffic maneuvers under the single-vehicle perspective. To cater for the platoon system's multi-vehicle perspective, we modify the existing abstract model and propose a Multi-Agent Spatial Logic (MASL) that extends MLSL by relative orientation and multi-agent observation. We then utilize a timed automata type supporting MASL formulas to model vehicles' decision controllers for platoon driving. Taking the behavior of a human-driven vehicle (HDV) joining the platoon as a case study, we have implemented the model and verified safety properties on the UPPAAL tool to illustrate the viability of our framework.

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Statistical Verification Framework for Platooning System of Systems with Uncertainty

Cited by 7 publications

References 14 publications

Stochastic Optimisation for the Design of Energy-Efficient Controllers for Cooperative Truck Platoons

Stochastic Optimisation for the Design of Energy-Efficient Controllers for Cooperative Truck Platoons

Fault-Tolerance by Resilient State Transition for Collaborative Cyber-Physical Systems

A Multi-Agent Spatial Logic for Scenario-Based Decision Modeling and Verification in Platoon Systems

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