Case Studies for Computing Density of Reachable States for Safe Autonomous Motion Planning

Meng, Yue; Qiu, Zeng; Waez, Md Tawhid Bin; Fan, Chuchu

doi:10.1007/978-3-031-06773-0_13

Cited by 6 publications

(3 citation statements)

References 48 publications

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“…For example, THW, RLA, and BTN [16] were proposed as an online driving risk indicator, RSS [2] was intended as a safe control action supervisor that can be added to any driving decision-making module, FSM [23] was defined to justify if a particular driving scene would evolve to an unpreventable collision, to name a few. Some of the other works involve formal verification methods [63], [64] such as those using set-based approaches [65], [66] to determine reachable states under different kinds of input assumptions (e.g., uncertainties, steady-state, instantaneous control action, etc.). These primarily involve behavioral models akin to the metrics considered in this work, and they are inherently aimed to complement the decision-making process by acting as supervisory controllers.…”

Section: A Other Related Literaturementioning

confidence: 99%

A Diversity Analysis of Safety Metrics Comparing Vehicle Performance in the Lead-Vehicle Interaction Regime

Singh

Weng

Rao

et al. 2023

IEEE Trans. Intell. Transport. Syst.

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Vehicle performance metrics analyze data sets consisting of subject vehicle's interactions with other road users in a nominal driving environment and provide certain performance measures as outputs. To the best of the authors' knowledge, the vehicle safety performance metrics research dates back to at least 1967. To date, there still does not exist a community-wide accepted metric or a set of metrics for vehicle safety performance assessment and justification. This issue gets further amplified with the evolving interest in Advanced Driver Assistance Systems and Automated Driving Systems. In this paper, the authors seek to perform a unified study that facilitates an improved community-wide understanding of vehicle performance metrics using the lead-vehicle interaction operational design domain as a common means of performance comparison. In particular, the authors study the diversity (including constructive formulation discrepancies and empirical performance differences) among 33 base metrics with up to 51 metric variants (with different choices of hyper-parameters) in the existing literature, published between 1967 and 2022. Two data sets are adopted for the empirical performance diversity analysis, including vehicle trajectories from normal highway driving environment and relatively high-risk incidents with collisions and near-miss cases. The analysis further implies that (i) the conceptual acceptance of a safety metric proposal can be problematic if the assumptions, conditions, and types of outcome assurance are not justified properly, and (ii) the empirical performance justification of an acceptable metric can also be problematic as a dominant consensus is not observed among metrics empirically.

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Section: A Other Related Literaturementioning

confidence: 99%

A Diversity Analysis of Safety Metrics Comparing Vehicle Performance in the Lead-Vehicle Interaction Regime

Singh

Weng

Rao

et al. 2023

IEEE Trans. Intell. Transport. Syst.

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“…for sets to be tractably encoded and operated on unlocks new approaches for a variety of downstream tasks, such as prediction with set-valued uncertainties (Mahjourian et al, 2022), reachable set computation (Meng et al, 2022), safetyconstrained trajectory optimization (Michaux et al, 2023), bin packing (Pan et al, 2023), object pile manipulation (Wang et al, 2023), and swept volume approximation in robotics (Chiang et al, 2021).…”

Section: Case Study: Transporting Algebras Of Setsmentioning

confidence: 99%

LQR Control with Sparse Adversarial Disturbances

Pfrommer

Sojoudi

2022

2022 IEEE 61st Conference on Decision and Control (CDC)

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Machine learning often aims to produce latent embeddings of inputs which lie in a larger, abstract mathematical space. For example, in the field of 3D modeling, subsets of Euclidean space can be embedded as vectors using implicit neural representations. Such subsets also have a natural algebraic structure including operations (e.g., union) and corresponding laws (e.g., associativity). How can we learn to "union" two sets using only their latent embeddings while respecting associativity? We propose a general procedure for parameterizing latent space operations that are provably consistent with the laws on the input space. This is achieved by learning a bijection from the latent space to a carefully designed mirrored algebra which is constructed on Euclidean space in accordance with desired laws. We evaluate these structural transport nets for a range of mirrored algebras against baselines that operate directly on the latent space. Our experiments provide strong evidence that respecting the underlying algebraic structure of the input space is key for learning accurate and self-consistent operations.

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“…Reachable sets are particularly interesting in applications where they support decision-making, see e.g. [15,21], where some controls model external disturbances that need to be taken into account, see e.g. [17,20,23], or where the reachable set models a spatial object to be controlled, see e.g.…”

mentioning

confidence: 99%

Towards optimal space-time discretization for reachable sets of nonlinear control systems

Rieger,

Wawryk

2024

JCD

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Case Studies for Computing Density of Reachable States for Safe Autonomous Motion Planning

Cited by 6 publications

References 48 publications

A Diversity Analysis of Safety Metrics Comparing Vehicle Performance in the Lead-Vehicle Interaction Regime

A Diversity Analysis of Safety Metrics Comparing Vehicle Performance in the Lead-Vehicle Interaction Regime

LQR Control with Sparse Adversarial Disturbances

Towards optimal space-time discretization for reachable sets of nonlinear control systems

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