Predictive Monitoring of Traffic Rules

Gressenbuch, Luis; Althoff, Matthias

doi:10.1109/itsc48978.2021.9564432

Cited by 18 publications

(27 citation statements)

References 33 publications

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“…Our robustness measure can be easily integrated into the prediction of traffic rule violations [8] and trajectory repairing [13]. In this section, we demonstrate that the modelpredictive definition also facilitates the robustness awareness of trajectory planning using a sampling-based planner of [36].…”

Section: B Robustness-aware Trajectory Planningmentioning

confidence: 93%

“…a) Specification Formalization: The development of autonomous vehicles requires planning and control to fulfill formal specifications. Several publications formalize traffic rules for interstates [3], [8], intersections [9], and marines [10] in MTL. They use parameterizable Boolean predicates and functions in higher-order logic to specify basic elements of rule specifications.…”

Section: A Related Workmentioning

confidence: 99%

“…We evaluate the applicability and efficacy of the model predictive robustness using the highD dataset [42] and German interstate traffic rules from [3], [8]. Our simulation is based on CommonRoad [43], and GPyTorch [44] is used to model and solve the GP regression.…”

Section: Numerical Experimentsmentioning

confidence: 99%

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Model Predictive Robustness of Signal Temporal Logic Predicates

Lin¹,

Li²,

Althoff³

2022

Preprint

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The robustness of signal temporal logic not only assesses whether a signal adheres to a specification but also provides a measure of how much a formula is fulfilled or violated. The calculation of robustness is based on evaluating the robustness of underlying predicates. However, the robustness of predicates is usually defined in a model-free way, i.e., without including the system dynamics. Moreover, it is often nontrivial to define the robustness of complicated predicates precisely. To address these issues, we propose a notion of model predictive robustness, which provides a more systematic way of evaluating robustness compared to previous approaches by considering model-based predictions. In particular, we use Gaussian process regression to learn the robustness based on precomputed predictions so that robustness values can be efficiently computed online. We evaluate our approach for the use case of autonomous driving with predicates used in formalized traffic rules on a recorded dataset, which highlights the advantage of our approach compared to traditional approaches in terms of expressiveness. By incorporating our robustness definitions into a trajectory planner, autonomous vehicles obey traffic rules more robustly than human drivers in the dataset.

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Section: B Robustness-aware Trajectory Planningmentioning

confidence: 93%

Section: A Related Workmentioning

confidence: 99%

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Model Predictive Robustness of Signal Temporal Logic Predicates

Lin¹,

Li²,

Althoff³

2022

Preprint

Self Cite

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show abstract

“…In addition to generating scenarios, many works use linear temporal logic [4,8,10], or STL [22] to describe traffic rules or safety properties of autonomous driving systems. We also adopt linear temporal logic to describe traffic rules.…”

Section: Related Workmentioning

confidence: 99%

Simulation-based Validation for Autonomous Driving Systems

Li¹,

Sifakis²,

Wang³

et al. 2023

Preprint

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Simulation is essential to validate autonomous driving systems. However, a simple simulation, even for an extremely high number of simulated miles or hours, is not sufficient. We need well-founded criteria showing that simulation does indeed cover a large fraction of the relevant real-world situations. In addition, the validation must concern not only incidents, but also the detection of any type of potentially dangerous situation, such as traffic violations.We investigate a rigorous simulation and testing-based validation method for autonomous driving systems that integrates an existing industrial simulator and a formally defined testing environment. The environment includes a scenario generator that drives the simulation process and a monitor that checks at runtime the observed behavior of the system against a set of system properties to be validated. The validation method consists in extracting from the simulator a semantic model of the simulated system including a metric graph, which is a mathematical model of the environment in which the vehicles of the system evolve. The monitor can verify properties formalized in a first-order linear temporal logic and provide diagnostics explaining their non satisfaction. Instead of exploring the system behavior randomly as many simulators do, we propose a method to systematically generate sets of scenarios that cover potentially risky situations, especially for different types of junctions where specific traffic rules must be respected. We show that the systematic exploration of risky situations has uncovered many flaws in the real simulator that would have been very difficult to discover by a random exploration process.

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“…Primarily, notions of safety are often indirect and only implicitly found in datasets, as it is customary to show examples of optimal actions (what the robot should do) as opposed to giving examples of failures (what to avoid). In fact, defining explicit safety criteria in most real-world scenarios is a complex task and requires deep domain knowledge [3,4,5]. In addition, learning algorithms can struggle to directly infer safety from high-dimensional observations, as most robots do not operate with global ground truth state information.…”

Section: Introductionmentioning

confidence: 99%

ConBaT: Control Barrier Transformer for Safe Policy Learning

Meng¹,

Vemprala²,

Bonatti³

et al. 2023

Preprint

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Large-scale self-supervised models have recently revolutionized our ability to perform a variety of tasks within the vision and language domains. However, using such models for autonomous systems is challenging because of safety requirements: besides executing correct actions, an autonomous agent must also avoid the high cost and potentially fatal critical mistakes. Traditionally, self-supervised training mainly focuses on imitating previously observed behaviors, and the training demonstrations carry no notion of which behaviors should be explicitly avoided. In this work, we propose Control Barrier Transformer (ConBaT), an approach that learns safe behaviors from demonstrations in a self-supervised fashion. ConBaT is inspired by the concept of control barrier functions in control theory and uses a causal transformer that learns to predict safe robot actions autoregressively using a critic that requires minimal safety data labeling. During deployment, we employ a lightweight online optimization to find actions that ensure future states lie within the learned safe set. We apply our approach to different simulated control tasks and show that our method results in safer control policies compared to other classical and learning-based methods such as imitation learning, reinforcement learning, and model predictive control.

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Predictive Monitoring of Traffic Rules

Cited by 18 publications

References 33 publications

Model Predictive Robustness of Signal Temporal Logic Predicates

Model Predictive Robustness of Signal Temporal Logic Predicates

Simulation-based Validation for Autonomous Driving Systems

ConBaT: Control Barrier Transformer for Safe Policy Learning

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