A Formally Verified Motion Planner for Autonomous Vehicles

Rizaldi, Albert; Immler, Fabian; Schürmann, Bastian; Althoff, Matthias

doi:10.1007/978-3-030-01090-4_5

Cited by 33 publications

(21 citation statements)

References 35 publications

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“…The framework can be integrated into existing motion planning architectures and enables the fail-safe operation of self-driving vehicles. In [10], the authors use reachability analysis to compute the reachable sets of each motion primitive and subsequently to define the satisfaction relation of motion primitives with formulae in linear temporal logic.…”

Section: Related Work a Aircraft Guidance Navigation And Controlmentioning

confidence: 99%

Geometric Verification of Vector Fields for Autonomous Aircraft Navigation

Miraglia

Hook

2021

IEEE Access

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New generations of small advanced aircraft may soon transform how people and freight are transported. Thus, ensuring safe and reliable operation of these systems, in all plausible scenarios, is of utmost priority. In this paper, we address this problem by proposing a method to verify the safety of an autonomous aircraft controller following a navigational policy. The policy is encoded using discrete vector fields that are meant to drive the vehicle to a goal state and away from obstacles or restricted airspace. The solution presented in this paper provides the ability to verify the policy, catching unsafe scenarios which may be missed by random Monte Carlo methods or general reachability analysis. After illustrating the main theoretical principles, a possible practical implementation is described and compared with analysis based on Monte Carlo simulations. The comparison shows an insightful example where Monte Carlo simulations fail to detect several corner cases that are uncovered by the proposed method. In the conclusion of the paper, we provide insights about possible future research to implement the proposed solution obtaining higher accuracy and efficiency. INDEX TERMS Navigation, Unmanned aerial vehicles, Autonomous agents, Reachability analysis VOLUME 4, 2016 This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2021.3062662, IEEE Access Giovanni Miraglia et al.: Geometric Verification of Vector Fields for Autonomous Aircraft Navigation Giovanni Miraglia et al.: Geometric Verification of Vector Fields for Autonomous Aircraft Navigation LOYD R. HOOK (Member, IEEE) has worked in aircraft autonomous systems research and implementation for the past 15 years beginning at NASA Dryden/Armstrong flight research center and continuing at his current position as Assistant Professor at the University of Tulsa. Loyd is currently the director of the TU Vehicle Autonomy and Intelligence Lab (TU VAIL) where his research focus is the development of safety assured autonomous vehicle systems. Loyd has MS and Ph.D. degrees from the University of Oklahoma in Electrical and Computer Engineering from 2006 and 2011 respectively.

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Section: Related Work a Aircraft Guidance Navigation And Controlmentioning

confidence: 99%

Geometric Verification of Vector Fields for Autonomous Aircraft Navigation

Miraglia

Hook

2021

IEEE Access

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“…desirable to make statements about worstcase input-output relations of (controlled) vehicle dynamics models, e.g. by applying reachability analysis [18,19]. When applied with proper system knowledge, such approaches can yield valuable formal proof whether safety constraints can be adhered to.…”

Section: A Relevance For Automated Drivingmentioning

confidence: 99%

Sensitivity Analysis for Vehicle Dynamics Models -- An Approach to Model Quality Assessment for Automated Vehicles

Nolte¹,

Schubert²,

Reisch³

et al. 2020

Preprint

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Model-based approaches have become increasingly popular in the domain of automated driving. This includes runtime algorithms, such as Model Predictive Control, as well as formal and simulative approaches for the verification of automated vehicle functions. With this trend, the quality of models becomes crucial for automated vehicle safety. Established tools from model theory which can be applied to assure model quality are uncertainty and sensitivity analysis [1].In this paper, we conduct sensitivity analyses for a single and double track vehicle dynamics model to gain insights about the models' behavior under different operating conditions. We compare the models, point out the most important findings regarding the obtained parameters sensitivities, and provide examples of possible applications of the gained insights.

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“…Recent work such as [10][11][12] investigated verifying reachand-avoid tasks 1 or routing tasks in linear temporal logic (LTL). LTL is a formal language that combines boolean and temporal operators.…”

Section: Related Workmentioning

confidence: 99%

From Specifications to Behavior: Maneuver Verification in a Semantic State Space

Esterle

Aravantinos

Knoll

2019

2019 IEEE Intelligent Vehicles Symposium (IV)

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To realize a market entry of autonomous vehicles in the foreseeable future, the behavior planning system will need to abide by the same rules that humans follow. Product liability cannot be enforced without a proper solution to the approval trap. In this paper, we define a semantic abstraction of the continuous space and formalize traffic rules in linear temporal logic (LTL). Sequences in the semantic state space represent maneuvers a high-level planner could choose to execute. We check these maneuvers against the formalized traffic rules using runtime verification. By using the standard model checker NuSMV, we demonstrate the effectiveness of our approach and provide runtime properties for the maneuver verification. We show that high-level behavior can be verified in a semantic state space to fulfill a set of formalized rules, which could serve as a step towards safety of the intended functionality.• an LTL representation corresponding to the partitioning method of [9], arXiv:1905.00708v1 [cs.RO]

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A Formally Verified Motion Planner for Autonomous Vehicles

Cited by 33 publications

References 35 publications

Geometric Verification of Vector Fields for Autonomous Aircraft Navigation

Geometric Verification of Vector Fields for Autonomous Aircraft Navigation

Sensitivity Analysis for Vehicle Dynamics Models -- An Approach to Model Quality Assessment for Automated Vehicles

From Specifications to Behavior: Maneuver Verification in a Semantic State Space

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