Compositional controller synthesis for vehicular traffic networks

Kim, Eric S.; Arcak, Murat; Seshia, Sanjit A.

doi:10.1109/cdc.2015.7403189

Cited by 43 publications

(29 citation statements)

References 10 publications

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“…The traffic model used in this example is taken from the modified, discrete-time cell transmission model presented in [3]. The traffic network can be represented as directed graph with a set E of edges or links and a set V of vertices or junctions.…”

Section: B Traffic Modelmentioning

confidence: 99%

Sparsity-aware finite abstraction

Gruber¹,

Kim²,

Arcak³

2017

2017 IEEE 56th Annual Conference on Decision and Control (CDC)

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Abstraction of a continuous-space model into a finite state and input dynamical model is a key step in formal controller synthesis tools. To date, these software tools have been limited to systems of modest size (typically ≤ 6 dimensions) because the abstraction procedure suffers from an exponential runtime with respect to the sum of state and input dimensions. We present a simple modification to the abstraction algorithm that dramatically reduces the computation time for systems exhibiting a sparse interconnection structure. This modified procedure recovers the same abstraction as the one computed by a brute force algorithm that disregards the sparsity. Examples highlight speed-ups from existing benchmarks in the literature, synthesis of a safety supervisory controller for a 12-dimensional and abstraction of a 51-dimensional vehicular traffic network.

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Section: B Traffic Modelmentioning

confidence: 99%

Sparsity-aware finite abstraction

Gruber¹,

Kim²,

Arcak³

2017

2017 IEEE 56th Annual Conference on Decision and Control (CDC)

Self Cite

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“…Synthesis approaches typically rely on specifications given in linear temporal logic and have been developed for low-complexity tasks such as adaptive cruise control (127) and control of signalized vehicular networks (128). Yet controller synthesis is currently limited in scope and deployment owing to its very large computational cost.…”

Section: Verification and Synthesismentioning

confidence: 99%

Planning and Decision-Making for Autonomous Vehicles

Schwarting

Alonso–Mora

Rus

2018

Annu. Rev. Control Robot. Auton. Syst.

709

378

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In this review, we provide an overview of emerging trends and challenges in the field of intelligent and autonomous, or self-driving, vehicles. Recent advances in the field of perception, planning, and decision-making for autonomous vehicles have led to great improvements in functional capabilities, with several prototypes already driving on our roads and streets. Yet challenges remain regarding guaranteed performance and safety under all driving circumstances. For instance, planning methods that provide safe and systemcompliant performance in complex, cluttered environments while modeling the uncertain interaction with other traffic participants are required. Furthermore, new paradigms, such as interactive planning and end-to-end learning, open up questions regarding safety and reliability that need to be addressed. In this survey, we emphasize recent approaches for integrated perception and planning and for behavior-aware planning, many of which rely on machine learning. This raises the question of verification and safety, which we also touch upon. Finally, we discuss the state of the art and remaining challenges for managing fleets of autonomous vehicles. 8.1

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“…Its main idea is to formally define the assumptions and guarantees of each subsystem, which are called contracts among the subsystems, and based on the contracts, to design (or establish) formal guarantees on the overall system. Different types of assume-guarantee formalisms have been proposed, such as temporal logic formulas [19] and supply/demand rates [20]. The composition of LK and ACC has been studied on the basis of contracts.…”

Section: Introductionmentioning

confidence: 99%

Correctness Guarantees for the Composition of Lane Keeping and Adaptive Cruise Control

Grizzle

Tabuada

et al. 2018

IEEE Trans. Automat. Sci. Eng.

149

115

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This paper develops a control approach with correctness guarantees for the simultaneous operation of lane keeping and adaptive cruise control. The safety specifications for these driver assistance modules are expressed in terms of set invariance. Control barrier functions are used to design a family of control solutions that guarantee the forward invariance of a set, which implies satisfaction of the safety specifications. The control barrier functions are synthesized through a combination of sum-of-squares program and physics-based modeling and optimization. A real-time quadratic program is posed to combine the control barrier functions with the performancebased controllers, which can be either expressed as control Lyapunov function conditions or as black-box legacy controllers. In both cases, the resulting feedback control guarantees the safety of the composed driver assistance modules in a formally correct manner. Importantly, the quadratic program admits a closed-form solution that can be easily implemented. The effectiveness of the control approach is demonstrated by simulations in the industry-standard vehicle simulator Carsim.

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Compositional controller synthesis for vehicular traffic networks

Cited by 43 publications

References 10 publications

Sparsity-aware finite abstraction

Sparsity-aware finite abstraction

Planning and Decision-Making for Autonomous Vehicles

Correctness Guarantees for the Composition of Lane Keeping and Adaptive Cruise Control

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