Correct-by-Construction Adaptive Cruise Control: Two Approaches

Nilsson, Petter; Hussien, Omar; Balkan, Ayça; Chen, Yuxiao; Ames, Aaron D.; Grizzle, Jessy W.; Özay, Necmiye; Peng, Huei; Tabuada, Paulo

doi:10.1109/tcst.2015.2501351

Cited by 152 publications

(98 citation statements)

References 33 publications

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“…We computed a controlled invariant set S ACC for the longitudinal dynamics in (13), and sampled the boundary of this set with the proposed approach to find falsifying initial conditions. The disturbance profile is computed by (i) solving the dual game, (ii) a simple heuristic that corresponds to the lead car doing a maximum braking, 5 or (iii) the lead car trying to achieve v des .…”

Section: Adaptive Cruise Control Resultsmentioning

confidence: 99%

“…As an additional advantage, in case a control design is found unsafe using the proposed method, we can supervise this unsafe controller with the controlled invariant set in order to guarantee safety while still using the unsafe controller, which may have favorable performance related properties [13]. This supervision idea is similar to the simplex architecture [3,20], where a performance controller is used together with a simpler controller that has a certified safety envelope and that overwrites the performance controller only when its actions risk safety.…”

Section: Introductionmentioning

confidence: 99%

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Using Control Synthesis to Generate Corner Cases: A Case Study on Autonomous Driving

Chou

Sahin

Yang

et al. 2018

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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This paper employs correct-by-construction control synthesis, in particular controlled invariant set computations, for falsification. Our hypothesis is that if it is possible to compute a "large enough" controlled invariant set either for the actual system model or some simplification of the system model, interesting corner cases for other control designs can be generated by sampling initial conditions from the boundary of this controlled invariant set. Moreover, if falsifying trajectories for a given control design can be found through such sampling, then the controlled invariant set can be used as a supervisor to ensure safe operation of the control design under consideration. In addition to interesting initial conditions, which are mostly related to safety violations in transients, we use solutions from a dual game, a reachability game for the safety specification, to find falsifying inputs. We also propose optimization-based heuristics for input generation for cases when the state is outside the winning set of the dual game. To demonstrate the proposed ideas, we consider case studies from basic autonomous driving functionality, in particular, adaptive cruise control and lane keeping. We show how the proposed technique can be used to find interesting falsifying trajectories for classical control designs like proportional controllers, proportional integral controllers and model predictive controllers, as well as an open source real-world autonomous driving package. MAIN INGREDIENTSOur goal in this paper is to search for interesting corner cases for falsification of closed-loop control systems. By interesting corner case, we mean a pair of initial condition and external (disturbance) input signal that leads to a trajectory violating a given safety specification, together with a certificate that it is possible to satisfy the specification for this initial condition and external input; therefore arXiv:1807.09537v2 [cs.SY]

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Section: Adaptive Cruise Control Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using Control Synthesis to Generate Corner Cases: A Case Study on Autonomous Driving

Chou

Sahin

Yang

et al. 2018

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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.

736

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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“…In response the authors of [8] propose a receding horizon framework, but still rely on coarse grid-based abstractions. Others have sought to verify Adaptive Cruise Control Algorithms (ACC) which severely restrict scenarios in which the car may operate (no lane changes) [9]. Finally, some research which eschews discretization in favor of continuous linearized dynamics focuses on moving the verification task online [10].…”

Section: Contributionsmentioning

confidence: 99%

APEX: Autonomous Vehicle Plan Verification and Execution

O’Kelly

Abbas

Gao

et al. 2016

SAE Technical Paper Series

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Autonomous vehicles (AVs) have already driven millions of miles on public roads, but even the simplest scenarios have not been certified for safety. Current methodologies for the verification of AV's decision and control systems attempt to divorce the lower level, short-term trajectory planning and trajectory tracking functions from the behavioral rules-based framework that governs mid-term actions. Such analysis is typically predicated on the discretization of the state space and has several limitations. First, it requires that a conservative buffer be added around obstacles such that many feasible plans are classified as unsafe. Second, the discretized controllers modeled in this analysis require several refinement steps before being implementable on an actual AV, and typically do not allow the specification of comfort-related properties on the trajectories. In contrast, consumer-ready AVs use motion planning algorithms that generate smooth trajectories. While viable algorithms exist for the generation of smooth trajectories originating from a single state, analysis should consider that the AV faces state estimation errors and disturbances. Third, verification is restricted to a discretized state space with fixed-size cells; this assumption can artificially limit the set of available trajectories if the discretization is too coarse. Conversely, too fine of a discretization renders the problem intractable for automated analysis. This work presents a new verification tool, APEX, which investigates the combined action of a behavioral planner and state lattice-based motion planner to guarantee a safe vehicle trajectory is chosen. In APEX, decisions made at the behavioral layer can be traced through to the spatio-temporal evolution of the AV and verified. Thus, there is no need to create abstractions of the AV's controllers, and aggressive trajectories required for evasive maneuvers can be accurately investigated. AbstractAutonomous vehicles (AVs) have already driven millions of miles on public roads, but even the simplest scenarios have not been certified for safety. Current methodologies for the verification of AV's decision and control systems attempt to divorce the lower level, short-term trajectory planning and trajectory tracking functions from the behavioral rules-based framework that governs mid-term actions. Such analysis is typically predicated on the discretization of the state space and has several limitations. First, it requires that a conservative buffer be added around obstacles such that many feasible plans are classified as unsafe. Second, the discretized controllers modeled in this analysis require several refinement steps before being implementable on an actual AV, and typically do not allow the specification of comfort-related properties on the trajectories. Consumer-ready AVs use motion planning algorithms that generate smooth trajectories. While viable algorithms exist for the generation of smooth trajectories originating from a single state, analysis should consider that the AV faces...

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Correct-by-Construction Adaptive Cruise Control: Two Approaches

Cited by 152 publications

References 33 publications

Using Control Synthesis to Generate Corner Cases: A Case Study on Autonomous Driving

Using Control Synthesis to Generate Corner Cases: A Case Study on Autonomous Driving

Planning and Decision-Making for Autonomous Vehicles

APEX: Autonomous Vehicle Plan Verification and Execution

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