Posetal Games: Efficiency, Existence, and Refinement of Equilibria in Games With Prioritized Metrics

Zanardi, Alessandro; Zardini, Gioele; Srinivasan, Sirish; Bolognani, Saverio; Censi, Andrea; Dörfler, Florian

doi:10.1109/lra.2021.3135030

Cited by 8 publications

(4 citation statements)

References 26 publications

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“…1). We update the state (consisting of the color and shape) of each traffic light t in the input list T (see line [16][17][18][19] using the type conversions in Tab. I (d).…”

Section: Implementation Detailsmentioning

confidence: 99%

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Simplifying Sim-to-Real Transfer in Autonomous Driving: Coupling Autoware with the CommonRoad Motion Planning Framework

Würsching,

Mascetta,

Lin

et al. 2024

2024 IEEE Intelligent Vehicles Symposium (IV)

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Validating motion planning algorithms for autonomous vehicles on a real system is essential to improve their safety in the real world. Open-source initiatives, such as Autoware, provide a deployable software stack for real vehicles. However, such driving stacks have a high entry barrier, so that integrating new algorithms is tedious. Especially new research results are thus mostly evaluated only in simulation, e.g., within the CommonRoad benchmark suite. To address this problem, we present CR2AW, a publicly available interface between the CommonRoad framework and Autoware. CR2AW significantly simplifies the sim-to-real transfer of motion planning research, by allowing users to easily integrate their CommonRoad planning modules into Autoware. Our experiments both in simulation and on our research vehicle showcase the usefulness of CR2AW.

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“…1). We update the state (consisting of the color and shape) of each traffic light t in the input list T (see line [16][17][18][19] using the type conversions in Tab. I (d).…”

Section: Implementation Detailsmentioning

confidence: 99%

“…Finally, we check if the goal is reached (see line 16). If yes, we exit the planning loop and set the routing state to ARRIVED in line 18. We note that it is also possible to have multiple goals along a global route.…”

Section: Implementation Detailsmentioning

confidence: 99%

Simplifying Sim-to-Real Transfer in Autonomous Driving: Coupling Autoware with the CommonRoad Motion Planning Framework

Würsching,

Mascetta,

Lin

et al. 2024

2024 IEEE Intelligent Vehicles Symposium (IV)

View full text Add to dashboard Cite

show abstract

“…For instance, safe, ethical, and robust motion planning algorithms are benchmarked using CommonRoad scenarios in [21]- [24]. The authors in [25]- [27] utilize CommonRoad tools to demonstrate the game-theoretic aspects of autonomous vehicles. Furthermore, CommonRoad paves the way to use advanced algorithms to facilitate motion planning, such as reinforcement learning [28], [29] and geometric deep learning [30].…”

Section: A Related Workmentioning

confidence: 99%

Automatic Traffic Scenario Conversion from OpenSCENARIO to CommonRoad

Lin,

Ratzel,

Althoff

2023

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

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Scenarios are a crucial element for developing, testing, and verifying autonomous driving systems. However, open-source scenarios are often formulated using different terminologies. This limits their usage across different applications as many scenario representation formats are not directly compatible with each other. To address this problem, we present the first open-source converter from the OpenSCENARIO format to the CommonRoad format, which are two of the most popular scenario formats used in autonomous driving. Our converter employs a simulation tool to execute the dynamic elements defined by OpenSCENARIO. The converter is available at commonroad.in.tum.de and we demonstrate its usefulness by converting publicly available scenarios in the OpenSCENARIO format and evaluating them using CommonRoad tools.

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“…Many of the works in this field look at fast local solvers for relatively short prediction horizons, which exhibit good interactive behaviors when implemented in a receding horizon approach [4]- [9]. The hardness of these problems is inborn in the superlinear (almost exponential) explosion of number of states and joint actions for the game [10], [11]. The deeper the game graph and the more the players, the worse for the problem.…”

Section: Introductionmentioning

confidence: 99%

Factorization of Dynamic Games over Spatio-Temporal Resources

Zanardi

Bolognani

Censi

et al. 2022

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Dynamic games feature a state-space complexity that scales superlinearly with the number of players. This makes this class of games often intractable even for a handful of players. We introduce the factorization process of dynamic games as a transformation leveraging the independence of players at equilibrium to build a leaner game graph. When applicable, it yields fewer nodes, fewer players per game node, hence much faster solutions. While for the general case checking for independence of players requires to solve the game itself, we observe that for dynamic games in the robotic domain there exist exact heuristics based on the spatio-temporal occupancy of the individual players. We validate our findings in realistic autonomous driving scenarios showing that already for a 4player intersection we have a reduction of game nodes and solving time close to 99%.

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Posetal Games: Efficiency, Existence, and Refinement of Equilibria in Games With Prioritized Metrics

Cited by 8 publications

References 26 publications

Simplifying Sim-to-Real Transfer in Autonomous Driving: Coupling Autoware with the CommonRoad Motion Planning Framework

Simplifying Sim-to-Real Transfer in Autonomous Driving: Coupling Autoware with the CommonRoad Motion Planning Framework

Automatic Traffic Scenario Conversion from OpenSCENARIO to CommonRoad

Factorization of Dynamic Games over Spatio-Temporal Resources

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