Towards Assume-Guarantee Profiles for Autonomous Vehicles

Phan-Minh, Tung; Cai, Karena X.; Murray, Richard M.

doi:10.1109/cdc40024.2019.9030068

Cited by 9 publications

(4 citation statements)

References 16 publications

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“…An interesting alternative is to generate rules that are consistent and complete by construction. Very recently, the authors of [149] addressed this problem using a distributed assume-guarantee structure. However, characterizing consistency and completeness of sets of ROTRs is an open and challenging problem.…”

Section: Discussion and Remaining Challengesmentioning

confidence: 99%

Formal methods to comply with rules of the road in autonomous driving: State of the art and grand challenges

et al. 2023

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Section: Discussion and Remaining Challengesmentioning

confidence: 99%

Formal methods to comply with rules of the road in autonomous driving: State of the art and grand challenges

et al. 2023

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“…a) Traffic-Rule-Informed Trajectory Planning: The use of formal methods allows autonomous vehicles to comply with high-level specifications and safely participate in traffic. These rules can be ensured, e.g., by reachability analysis [4], assume-guarantee contract formalisms [5], and partially by…”

Section: A Related Workmentioning

confidence: 99%

Rule-Compliant Trajectory Repairing using Satisfiability Modulo Theories

Lin

Althoff

2022

2022 IEEE Intelligent Vehicles Symposium (IV)

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Autonomous vehicles must comply with traffic rules. However, most motion planners do not explicitly consider all relevant traffic rules. Once traffic rule violations of an initially-planned trajectory are detected, there is often not enough time to replan the entire trajectory. To solve this problem, we propose to repair the initial trajectory by investigating the satisfiability modulo theories paradigm. This framework makes it efficient to reason whether and how the trajectory can be repaired and, at the same time, determine the part along the trajectory that can remain unchanged. Moreover, the robustness of traffic rule satisfaction is used to formulate a convex optimization problem for generating rule-compliant trajectories. We compare our approach with trajectory replanning and demonstrate its usefulness with traffic scenarios from the CommonRoad benchmark suite and recorded data. The evaluation result shows that rule-compliant trajectory repairing is computationally efficient and widely applicable.

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“…Future Work: A promising solution to guarantee safety at low δ is to utilize prior knowledge about human behavior; in particular, humans obey interaction rules (e.g. signaling intent) [54], which bound uncertainty in useful ways and can be encoded in assume-guarantee contracts [55]. A contract might encode that an agent cannot mislead others about its intention, assuming that others do not mislead it.…”

Section: Learned Uncertainty Distributions Become Highly Inaccurate At Low δ Undermining Any Claimed Guarantees Of Safetymentioning

confidence: 99%

Limits of Probabilistic Safety Guarantees when Considering Human Uncertainty

Cheng

Murray

Burdick

2021

2021 IEEE International Conference on Robotics and Automation (ICRA)

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When autonomous robots interact with humans, such as during autonomous driving, explicit safety guarantees are crucial in order to avoid potentially life-threatening accidents. Many data-driven methods have explored learning probabilistic bounds over human agents' trajectories (i.e. confidence tubes that contain trajectories with probability δ), which can then be used to guarantee safety with probability 1 − δ. However, almost all existing works consider δ ≥ 0.001. The purpose of this paper is to argue that (1) in safety-critical applications, it is necessary to provide safety guarantees with δ < 10 −8 , and (2) current learning-based methods are illequipped to compute accurate confidence bounds at such low δ. Using human driving data (from the highD dataset), as well as synthetically generated data, we show that current uncertainty models use inaccurate distributional assumptions to describe human behavior and/or require infeasible amounts of data to accurately learn confidence bounds for δ ≤ 10 −8 . These two issues result in unreliable confidence bounds, which can have dangerous implications if deployed on safety-critical systems.

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Towards Assume-Guarantee Profiles for Autonomous Vehicles

Cited by 9 publications

References 16 publications

Formal methods to comply with rules of the road in autonomous driving: State of the art and grand challenges

Formal methods to comply with rules of the road in autonomous driving: State of the art and grand challenges

Rule-Compliant Trajectory Repairing using Satisfiability Modulo Theories

Limits of Probabilistic Safety Guarantees when Considering Human Uncertainty

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