Adaptive cruise control: Experimental validation of advanced controllers on scale-model cars

Mehra, Aakar; Ma, Wen-Loong; Berg, Forrest; Tabuada, Paulo; Grizzle, Jessy W.; Ames, Aaron D.

doi:10.1109/acc.2015.7170931

Cited by 50 publications

(43 citation statements)

References 21 publications

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“…However, the computation of the finite abstraction is expensive, and the size of the final graph (on which the controller synthesis is based) is exponential in the length of the LTL formula and in the dimension of the system. The ACC problem can also be addressed using control barrier functions (Mehra et al, 2015;Ames et al, 2014). These functions are used to penalize the violation of the constraints that arise from ACC specifications.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Cruise Control with Safety Guarantees for Autonomous Vehicles

Magdici

Althoff

2017

IFAC-PapersOnLine

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This paper addresses the problem of following a vehicle with varying acceleration in a comfortable and safe manner. Our architecture consists of a nominal controller (here: model predictive control) and a safety controller. Although model predictive control attempts to keep a safe distance, it cannot formally guarantee it, due to the assumptions on the behavior of the leading vehicle. We address this problem by holding a formally verified safety controller available. Our novel mechanism gradually engages the safety maneuver since most critical situations resolve quickly. The overall approach is evaluated against real traffic data. The results show good position and velocity tracking performance, while safety and comfort are guaranteed.

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Section: Related Workmentioning

confidence: 99%

Adaptive Cruise Control with Safety Guarantees for Autonomous Vehicles

Magdici

Althoff

2017

IFAC-PapersOnLine

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“…Other approaches such as theorem proving [14], [15], [26], verification [3], [10], [28], and synthesis [5], [17], [30] also address the safety of autonomous vehicles. The authors of [14], [15], [26] all demonstrate the use of theorem proving for AV safety.…”

Section: Related Workmentioning

confidence: 99%

“…In contrast this work builds a larger library of scenarios with multiple dynamic agents, adds realistic road geometries, introduces a flexible scenario description language, and integrates specificationguide testing with exhaustive reachability analysis. Finally, other works [5], [17], [30] address the synthesis of AV controllers from formal specifications. This orthogonal attack largely addresses design at the behavior level only, and when composed with the complete system often must still be verified.…”

Section: Related Workmentioning

confidence: 99%

Computer-aided design for safe autonomous vehicles

O’Kelly

Abbas

Mangharam

2017

2017 Resilience Week (RWS)

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This paper details the design of an autonomous vehicle CAD toolchain, which captures formal descriptions of driving scenarios in order to develop a safety case for an autonomous vehicle (AV). Rather than focus on a particular component of the AV, like adaptive cruise control, the toolchain models the end-to-end dynamics of the AV in a formal way suitable for testing and verification. First, a domain-specific language capable of describing the scenarios that occur in the day-to-day operation of an AV is defined. The language allows the description and composition of traffic participants, and the specification of formal correctness requirements. A scenario described in this language is an executable that can be processed by a specification-guided automated test generator (bug hunting), and by an exhaustive reachability tool. The toolchain allows the user to exploit and integrate the strengths of both testing and reachability, in a way not possible when each is run alone. Finally, given a particular execution of the scenario that violates the requirements, a visualization tool can display this counter-example and generate labeled sensor data. The effectiveness of the approach is demonstrated on five autonomous driving scenarios drawn from a collection of 36 scenarios that account for over 95% of accidents nationwide. These case studies demonstrate robustness-guided verification heuristics to reduce analysis time, counterexample visualization for identifying controller bugs in both the discrete decision logic and low-level analog (continuous) dynamics, and identification of modeling errors that lead to unrealistic environment behavior. Abstract-This paper details the design of an autonomous vehicle CAD toolchain, which captures formal descriptions of driving scenarios in order to develop a safety case for an autonomous vehicle (AV). Rather than focus on a particular component of the AV, like adaptive cruise control, the toolchain models the end-to-end dynamics of the AV in a formal way suitable for testing and verification. First, a domain-specific language capable of describing the scenarios that occur in the day-to-day operation of an AV is defined. The language allows the description and composition of traffic participants, and the specification of formal correctness requirements. A scenario described in this language is an executable that can be processed by a specification-guided automated test generator (bug hunting), and by an exhaustive reachability tool. The toolchain allows the user to exploit and integrate the strengths of both testing and reachability, in a way not possible when each is run alone. Finally, given a particular execution of the scenario that violates the requirements, a visualization tool can display this counter-example and generate labeled sensor data. The effectiveness of the approach is demonstrated on five autonomous driving scenarios drawn from a collection of 36 scenarios that account for over 95% of accidents nationwide. These case studies demonstrate robustness-guided verif...

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“…The proposed method generalizes the well-known pointwise minnorm control method for designing a control law using control Lyapunov functions via an optimization problem [13]. It has been successfully implemented in the cruise control of autonomous vehicle as reported in [10]. Another direct approach is pursued in [14], [16] which is based on the direct merging of control Lyapunov function and control Barrier function.…”

Section: Introductionmentioning

confidence: 99%

On the new notion of input-to-state safety

Romdlony

Jayawardhana

2016

2016 IEEE 55th Conference on Decision and Control (CDC)

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Abstract-In this paper, we study robustness analysis of systems' safety with respect to external input (or disturbance) signals. To this end, we introduce a new notion of input-to-state safety (ISSf) which allows us to quantify the systems' safety robustness, in the same way as the application of input-tostate stability (ISS) notion for analyzing robustness of systems' stability. In particular, ISSf prescribes the relationship between the evolution of state distance to the unsafe set with the initial conditions and the bounded external input signals. Finally, we discuss how to combine this notion with ISS for analyzing the robustness of both systems' stability and safety.

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Adaptive cruise control: Experimental validation of advanced controllers on scale-model cars

Cited by 50 publications

References 21 publications

Adaptive Cruise Control with Safety Guarantees for Autonomous Vehicles

Adaptive Cruise Control with Safety Guarantees for Autonomous Vehicles

Computer-aided design for safe autonomous vehicles

On the new notion of input-to-state safety

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