Formal methods for semi-autonomous driving

Seshia, Sanjit A.; Sadigh, Dorsa; Sastry, S. Shankar

doi:10.1145/2744769.2747927

Cited by 35 publications

(23 citation statements)

References 17 publications

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“…In this section, we provide CBFs for the LK model (9) and the ACC model (10), respectively. To this end, we translate the hard constraints of Section III-B into conditions that a CBF must satisfy so that, by the results of Section II, the trajectories of the closed-loop system will satisfy the safety portion of the specification.…”

Section: Control Barrier Functions For Lk and Accmentioning

confidence: 99%

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

Grizzle

Tabuada

et al. 2018

IEEE Trans. Automat. Sci. Eng.

151

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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Section: Control Barrier Functions For Lk and Accmentioning

confidence: 99%

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

Grizzle

Tabuada

et al. 2018

IEEE Trans. Automat. Sci. Eng.

151

115

View full text Add to dashboard Cite

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“…Seshia et al [17] discuss the need for formal methods for semi-autonomous driving. By requiring human driver intervention at certain moments, semi-autonomous cars introduce many conditional specification and verification problems.…”

Section: Motivation and Backgroundmentioning

confidence: 99%

A Formal Model to Facilitate Security Testing in Modern Automotive Systems

Santos

Simpson

Schoop

2018

Electron. Proc. Theor. Comput. Sci.

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Ensuring a car's internal systems are free from security vulnerabilities is of utmost importance, especially due to the relationship between security and other properties, such as safety and reliability. We provide the starting point for a model-based framework designed to support the security testing of modern cars. We use Communicating Sequential Processes (CSP) to create architectural models of the vehicle bus systems, as well as an initial set of attacks against these systems. While this contribution represents initial steps, we are mindful of the ultimate objective of generating test code to exercise the security of vehicle bus systems. We present the way forward from the models created and consider their potential integration with commercial engineering tools.

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“…It is unlikely that the designer will think of all potential scenarios to ensure complete coverage. Formal verification methods try to answer the rest of the questions by accounting for all the probabilities for a given scenario [12,13]. If accurate dynamical models are available to represent robotic skills of sensing and action, then formal verification can rely on a finite interaction model of the vehicle with a bounded model of the environment, that is based on known characteristics of traffic participants.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Verification Technique for Decision-Making of Self-Driving Vehicles

Al-Nuaimi

Wibowo

et al. 2021

JSAN

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The evolution of driving technology has recently progressed from active safety features and ADAS systems to fully sensor-guided autonomous driving. Bringing such a vehicle to market requires not only simulation and testing but formal verification to account for all possible traffic scenarios. A new verification approach, which combines the use of two well-known model checkers: model checker for multi-agent systems (MCMAS) and probabilistic model checker (PRISM), is presented for this purpose. The overall structure of our autonomous vehicle (AV) system consists of: (1) A perception system of sensors that feeds data into (2) a rational agent (RA) based on a belief–desire–intention (BDI) architecture, which uses a model of the environment and is connected to the RA for verification of decision-making, and (3) a feedback control systems for following a self-planned path. MCMAS is used to check the consistency and stability of the BDI agent logic during design-time. PRISM is used to provide the RA with the probability of success while it decides to take action during run-time operation. This allows the RA to select movements of the highest probability of success from several generated alternatives. This framework has been tested on a new AV software platform built using the robot operating system (ROS) and virtual reality (VR) Gazebo Simulator. It also includes a parking lot scenario to test the feasibility of this approach in a realistic environment. A practical implementation of the AV system was also carried out on the experimental testbed.

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Formal methods for semi-autonomous driving

Cited by 35 publications

References 17 publications

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

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

A Formal Model to Facilitate Security Testing in Modern Automotive Systems

Hybrid Verification Technique for Decision-Making of Self-Driving Vehicles

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