Collaborative models for autonomous systems controller synthesis

Fraser, Douglas; Giaquinta, Ruben; Hoffmann, R.; Ireland, Murray; Miller, Alice; Norman, Gethin

doi:10.1007/s00165-020-00508-1

Cited by 10 publications

(6 citation statements)

References 65 publications

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“…Applications. Applications of MDP-based probabilistic model checking for autonomous systems include: motion planning (35,36,37), spacecraft reconfiguration (38), task allocation and planning for mobile robots (4,39), analysis of the safety and reliability of robots in extreme environments (40), human-on-the-loop systems (41), robot battery charge scheduling (42) and autonomic computing (43). For a survey on using formal methods (including probabilistic model checking) for the verification of autonomous robotic systems see (44).…”

Section: Tool Supportmentioning

confidence: 99%

Probabilistic Model Checking and Autonomy

Kwiatkowska

Norman

Parker

2022

Annu. Rev. Control Robot. Auton. Syst.

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The design and control of autonomous systems that operate in uncertain or adversarial environments can be facilitated by formal modeling and analysis. Probabilistic model checking is a technique to automatically verify, for a given temporal logic specification, that a system model satisfies the specification, as well as to synthesize an optimal strategy for its control. This method has recently been extended to multiagent systems that exhibit competitive or cooperative behavior modeled via stochastic games and synthesis of equilibria strategies. In this article, we provide an overview of probabilistic model checking, focusing on models supported by the PRISM and PRISM-games model checkers. This overview includes fully observable and partially observable Markov decision processes, as well as turn-based and concurrent stochastic games, together with associated probabilistic temporal logics. We demonstrate the applicability of the framework through illustrative examples from autonomous systems. Finally, we highlight research challenges and suggest directions for future work in this area. Expected final online publication date for the Annual Review of Control, Robotics, and Autonomous Systems, Volume 5 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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Section: Tool Supportmentioning

confidence: 99%

Probabilistic Model Checking and Autonomy

Kwiatkowska

Norman

Parker

2022

Annu. Rev. Control Robot. Auton. Syst.

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“…Model checking and simulation is used for trajectory planning in [17]. The approach described there is for unmanned aerial vehicles rather than self-driving cars, and uses probabilistic models for route planning.…”

Section: Related Workmentioning

confidence: 99%

Simulation and Model Checking for Close to Realtime Overtaking Planning

Pagojus¹,

Miller²,

Porr³

et al. 2021

Electron. Proc. Theor. Comput. Sci.

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Fast and reliable trajectory planning is a key requirement of autonomous vehicles. In this paper we introduce a novel technique for planning the route of an autonomous vehicle on a straight rural road using the Spin model checker. We show how we can combine Spin's ability to identify paths violating temporal properties with sensor information from a 3D Unity simulation of an autonomous vehicle, to plan and perform consecutive overtaking manoeuvres on a traffic-heavy road. This involves discretising the sensory information and combining multiple sequential Spin models with a Linear Time Temporal Logic specification to generate an error path. This path provides the autonomous vehicle with an action plan. The entire process takes place in close to real-time (using no pre-computed data) and the action plan is specifically tailored for individual scenarios. Our experiments demonstrate that the simulated autonomous vehicle implementing our approach can drive on average at least 40 km and overtake 214 vehicles before experiencing a collision -which is usually caused by inaccuracies in the sensory system. While the proposed system has some drawbacks, we believe that our novel approach demonstrates a potentially powerful future tool for efficient trajectory planning for autonomous vehicles.

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“…Gerwinn et al 37 propose a rigorous model for overtaking requirements and generate statistical evidence for driving safety. Fraser et al 38 show collaborative models combining Markov decision process and simulation models together, and use probabilistic model checker PRISM for optimal strategy synthesis. As a new application and research direction, we can use statistical model checking for accident or failure predictions, Calder and Sevegnani 7 propose a stochastic framework considering discrete space and temporal logic to forecast failure time bounds and maintenance cost.…”

Section: Literature Review and Research Motivationmentioning

confidence: 99%

An accident prediction architecture based on spatio‐clock stochastic and hybrid model for autonomous driving safety

Wang

Huang

et al. 2021

Concurrency and Computation

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Summary Collaborative and autonomous driving vehicles combine hardware and software complex processes, also are heavily dependent on and influenced by the world of physical and cyber interactions. They have enabled many new features and advanced functionalities, such as stochastic and hybrid natures, mobile spatial topologies, and time‐critical dependability. However, the existing modeling and verification techniques have not established faith in proving correctness and safety. Spatial and time collision avoidance remains crucial obstacles on the path to becoming ubiquitous and dependable. In order to ensure safety, we first design an accident prediction architecture in system design‐time and run‐time stages. We apply it on collaborative and autonomous overtaking systems involving spatial‐ and time‐critical accident predictions. Then, we develop a novel and dedicated spatio‐clock stochastic specification language (SCSSL) to describe safety invariants and guards in domain‐specific autonomous driving systems. Next, we create the spatio‐clock stochastic and hybrid automata models based on SCSSL in order to model inherently stochastic and hybrid behaviors. To illustrate the effectiveness of spatio‐clock consistency stochastic specification and verification, we adopt statistical model checking natively to provide reliable predictions for the incoming collision instants and positions. Finally, we present an illustrative overtaking case study to verify spatio‐clock stochastic and hybrid related properties and ensure correct modeling, and demonstrate the significance of our proposed approach.

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Collaborative models for autonomous systems controller synthesis

Cited by 10 publications

References 65 publications

Probabilistic Model Checking and Autonomy

Probabilistic Model Checking and Autonomy

Simulation and Model Checking for Close to Realtime Overtaking Planning

An accident prediction architecture based on spatio‐clock stochastic and hybrid model for autonomous driving safety

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