A hybrid controller for safe and efficient longitudinal collision avoidance control

Wang, Qiang; Zheng, Xinlei; Zhang, Jiyong; Sifakis, Joseph

doi:10.1016/j.sysarc.2022.102432

Cited by 3 publications

(2 citation statements)

References 21 publications

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“…Control policies for vehicles and the runtime can be implemented efficiently. In particular, the speed policy has been tested in various implementations [24,25] and found to be not only safe, but also closer to the optimum when refining the space of possible accelerations.…”

Section: Methodsmentioning

confidence: 99%

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Correct by Design Coordination of Autonomous Driving Systems

Bozga¹,

Sifakis²

2022

Preprint

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The paper proposes a method for the correct by design coordination of autonomous driving systems (ADS ). It builds on previous results on collision avoidance policies and the modeling of ADS by combining descriptions of their static environment in the form of maps, and the dynamic behavior of their vehicles. An ADS is modeled as a dynamic system involving a set of vehicles coordinated by a Runtime that based on vehicle positions on a map and their kinetic attributes, computes free spaces for each vehicle. Vehicles are bounded to move within the corresponding allocated free spaces. We provide a correct by design safe control policy for an ADS if its vehicles and the Runtime respect corresponding assume-guarantee contracts. The result is established by showing that the composition of assumeguarantee contracts is an inductive invariant that entails ADS safety. We show that it is practically possible to define speed control policies for vehicles that comply with their contracts. Furthermore, we show that traffic rules can be specified in a linear-time temporal logic, as a class of formulas that constrain vehicle speeds. The main result is that, given a set of traffic rules, it is possible to derive free space policies of the Runtime such that the resulting system behavior is safe by design with respect to the rules.

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Section: Methodsmentioning

confidence: 99%

“…The behavior of each vehicle is defined by a controller, which given its current speed and its free space, computes the displacement for ∆t so that it can safely move in the free space. Such safe speed policies have been studied in [24,25].…”

Section: Speed Policies Abiding By the Vehicle Contractmentioning

confidence: 99%

Correct by Design Coordination of Autonomous Driving Systems

Bozga¹,

Sifakis²

2022

Preprint

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Runtime Assurance of Learning-Based Lane Changing Control for Autonomous Driving Vehicles

Wang

Kou

Chen

et al. 2022

J CIRCUIT SYST COMP

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Learning techniques such as deep reinforcement learning have been increasingly used in the controller design for autonomous vehicles, e.g., lane changing controllers. Although the use of learning techniques can remarkably increase the level of autonomy, their presence poses a great challenge for safety assurance due to the black-box and data-driven nature of learning techniques. In this work, we study the problem of how to safeguard the learning-based lane changing controller for collision avoidance. Our solution leverages on the runtime assurance framework, in particular the Simplex architecture to bound the behavior of the learning-based controller and to provide safety guarantees. The basic idea is to encompass the learning-based controller with a safety-by-construction controller and a decision module, which monitors the output of the learning-based controller at runtime and implements a switching logic between these two controllers according to the changing environment. We present the detailed design of the decision module and formally prove its correctness for collision avoidance. We also carry out a comprehensive experimental evaluation in a set of realistic highway scenarios using the SUMO simulator. The simulation results show that our proposed solution can not only provide safety guarantee for the learning-based lane changing controller, but also maintain a considerable level of efficiency in different volumes of traffic flow.

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Active Monitoring Mechanism for Control-Based Self-Adaptive Systems

Qin,

Tong,

et al. 2024

Proc. ACM Softw. Eng.

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Control-based self-adaptive systems (control-SAS) are susceptible to deviations from their pre-identified nominal models. If this model deviation exceeds a threshold, the optimal performance and theoretical guarantees of the control-SAS can be compromised. Existing approaches detect these deviations by locating the mismatch between the control signal of the managing system and the response output of the managed system. However, vague observations may mask a potential mismatch where the explicit system behavior does not reflect the implicit variation of the nominal model. In this paper, we propose the Active Monitoring Mechanism (AMM for short) as a solution to this issue. The basic intuition of AMM is to stimulate the control-SAS with an active control signal when vague observations might mask model deviations. To determine the appropriate time for triggering the active signals, AMM proposes a stochastic framework to quantify the relationship between the implicit variation of a control-SAS and its explicit observation. Based on this framework, AMM’s monitor and remediator enhance model deviation detection by generating active control signals of well-designed timing and intensity. Results from empirical evaluations on three representative systems demonstrate AMM’s effectiveness (33.0% shorter detection delay, 18.3% lower FN rate, 16.7% lower FP rate) and usefulness (19.3% lower abnormal rates and 88.2% higher utility).

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A hybrid controller for safe and efficient longitudinal collision avoidance control

Cited by 3 publications

References 21 publications

Correct by Design Coordination of Autonomous Driving Systems

Correct by Design Coordination of Autonomous Driving Systems

Runtime Assurance of Learning-Based Lane Changing Control for Autonomous Driving Vehicles

Active Monitoring Mechanism for Control-Based Self-Adaptive Systems

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