Rule-Compliant Trajectory Repairing using Satisfiability Modulo Theories

Lin, Yuanfei; Althoff, Matthias

doi:10.1109/iv51971.2022.9827357

Cited by 10 publications

(7 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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: 92%

“…b) Model-free Robustness: The robustness of STL is typically nonconvex and nondifferentiable, e.g., in [6]. Therefore, it is generally difficult to deploy fast gradient-based optimization algorithms for online usage, such as optimizationbased trajectory planning for autonomous vehicles [13]. Many new extensions for STL robustness have been proposed to address this issue.…”

Section: A Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Model Predictive Robustness of Signal Temporal Logic Predicates

Lin¹,

Li²,

Althoff³

2022

Preprint

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: B Robustness-aware Trajectory Planningmentioning

confidence: 92%

Section: A Related Workmentioning

confidence: 99%

Model Predictive Robustness of Signal Temporal Logic Predicates

Lin¹,

Li²,

Althoff³

2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…2) Model-free Robustness: The robustness of STL is typically nonconvex and nondifferentiable, see, e.g., [7]. Therefore, it is generally difficult to deploy fast gradient-based optimization algorithms for online usage, such as optimizationbased trajectory planning for autonomous vehicles [14]. Many new extensions for STL robustness have been proposed to address this issue.…”

Section: A Related Workmentioning

confidence: 99%

Model Predictive Robustness of Signal Temporal Logic Predicates

Lin,

Li,

Althoff

2023

IEEE Robot. Autom. Lett.

Self Cite

View full text Add to dashboard Cite

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 precision. By incorporating our robustness definitions into a trajectory planner, autonomous vehicles obey traffic rules more robustly than human drivers in the dataset.Index Terms-Formal methods in robotics and automation, integrated planning and learning, signal temporal logic, model predictive robustness, Gaussian process regression.

show abstract

“…ASP and Ontologies/DL are used for verifying maps in [19], [24]. Moreover, SMT is used to repair trajectories considering logical constraints [26]. We can only provide a short and informal introduction to the different approaches.…”

Section: B Comparison With Other Approachesmentioning

confidence: 99%

Map Verification and Repairing Using Formalized Map Specifications

Maierhofer,

Ballnath,

Althoff

2023

2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)

Self Cite

View full text Add to dashboard Cite

Autonomous vehicles benefit from correct maps to participate in traffic safely, but often maps are not verified before their usage. We address this problem by providing an approach to verify and repair maps automatically based on a formalization of map specifications in higher-order logic. Unlike previous work, we provide a collection of map specifications. We can verify and repair all possible map parts, from geometric to semantic elements, e.g., adjacency relationships, lane types, road boundaries, traffic signs, and intersections. Due to the modular design of our approach, one can integrate additional logics. We compare ontologies, answer set programming, and satisfiability modulo theories with our higher-order logic verification algorithm. Our evaluation shows that our approach can efficiently verify and repair maps from several data sources and of different map sizes. We provide our tool as part of the CommonRoad Scenario Designer toolbox available at commonroad.in.tum.de.

show abstract

Rule-Compliant Trajectory Repairing using Satisfiability Modulo Theories

Cited by 10 publications

References 41 publications

Model Predictive Robustness of Signal Temporal Logic Predicates

Model Predictive Robustness of Signal Temporal Logic Predicates

Model Predictive Robustness of Signal Temporal Logic Predicates

Map Verification and Repairing Using Formalized Map Specifications

Contact Info

Product

Resources

About