Falsifying Motion Plans of Autonomous Vehicles With Abstractly Specified Traffic Scenarios

Klischat, Moritz; Althoff, Matthias

doi:10.1109/tiv.2022.3191179

Cited by 6 publications

(6 citation statements)

References 67 publications

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“…Third, obstacles in a parking scenario complicate the planning problem. Recall that an on-road trajectory planner is typically developed in the Frenet frame (also known as a curvilinear coordinate system or a road-aligned frame) [14], [15], which makes all the surrounding obstacles located to the left or right side of the ego vehicle. By contrast, parking planners commonly work in the Cartesian frame, wherein obstacles in a cluttered environment render multiple homotopy classes for the parking planner to choose [16], [17].…”

Section: Motivationsmentioning

confidence: 99%

Online Competition of Trajectory Planning for Automated Parking: Benchmarks, Achievements, Learned Lessons, and Future Perspectives

Fan

Ouyang

et al. 2023

IEEE Trans. Intell. Veh.

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Automated parking is a typical function in a self-driving car. The trajectory planning module directly reflects the intelligence level of an automated parking system. Although many competitions have been launched for autonomous driving, most of them focused on on-road driving scenarios. However, driving on a structured road greatly differs from parking in an unstructured environment. In addition, previous competitions typically competed on the overall driving performance instead of the trajectory planning performance. A trajectory planning competition of automated parking (TPCAP) has been recently organized. This event competed on parking-oriented planners without involving other modules, such as localization, perception, or tracking control. This study reports the TPCAP benchmarks, achievements, experiences, and future perspectives.

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Section: Motivationsmentioning

confidence: 99%

Online Competition of Trajectory Planning for Automated Parking: Benchmarks, Achievements, Learned Lessons, and Future Perspectives

Fan

Ouyang

et al. 2023

IEEE Trans. Intell. Veh.

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“…a) Criticality Measures for Advanced Driver Assistant Systems and Autonomous Driving: Criticality measures are primarily developed to objectively determine the behavioral safety and threat level of autonomous driving systems. For example, criticality measures are used to validate the safety of autonomous vehicles through various methods, including generating safety-critical scenarios [7]- [11], falsifying the system under test [12], and formally verifying system properties [1]. For motion planning applications, criticality measures help to find traffic conflicts, repair unsafe trajectories, and provide fail-safe solutions [13]- [15].…”

Section: A Related Workmentioning

confidence: 99%

CommonRoad-CriMe: A Toolbox for Criticality Measures of Autonomous Vehicles

Lin,

Althoff

2023

2023 IEEE Intelligent Vehicles Symposium (IV)

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Criticality measures are essential for autonomous vehicles to capture the complexity of the surrounding environment, trigger emergency maneuvers, and verify safety. However, there is currently no publicly available toolbox that allows researchers to use or evaluate a large number of criticality measures on arbitrary traffic scenarios. To address this issue, we present CommonRoad-CriMe, an open-source toolbox for measuring the criticality of autonomous vehicles in a unified framework. Our toolbox covers a wide range of state-of-theart criticality measures and provides visualized information to facilitate debugging and showcasing. Numerical experiments demonstrate how our toolbox facilitates the comparison of different criticality measures and the analysis of traffic conflicts. Our toolbox is available at commonroad.in.tum.de.

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“…We use a recursive, one-step forward consistency definition. This is computed backwards in time, starting at the final time step □ f with [28]:…”

Section: A Concept For Computing the Reachable Setsmentioning

confidence: 99%

“…7.2]. A more detailed description of forward consistency computation is given in [28]. The resulting reachable sets ↶ R i k are the input to the QP optimization trajectory synthesis.…”

Section: Backward Path Of the Reachability Analysismentioning

confidence: 99%

Synthesizing Traffic Scenarios from Formal Specifications Using Reachability Analysis

Finkeldei,

Althoff

2023

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

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Scenario-based testing is a promising approach for the verification of automated vehicles (AVs). In this paper, we present a novel approach that combines reachability analysis and numerical optimization to derive concrete scenarios from formal specifications. This promises to address multiple deficiencies of previous approaches: Improvement of computation times, handling of nonlinear specifications (e.g., traffic rules), and incorporation of criticality metrics. Our evaluation shows that the computation time increases linearly with the number of agents and the time horizon, compared to a typically exponential increase for methods without reachability analysis.

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Falsifying Motion Plans of Autonomous Vehicles With Abstractly Specified Traffic Scenarios

Cited by 6 publications

References 67 publications

Online Competition of Trajectory Planning for Automated Parking: Benchmarks, Achievements, Learned Lessons, and Future Perspectives

Online Competition of Trajectory Planning for Automated Parking: Benchmarks, Achievements, Learned Lessons, and Future Perspectives

CommonRoad-CriMe: A Toolbox for Criticality Measures of Autonomous Vehicles

Synthesizing Traffic Scenarios from Formal Specifications Using Reachability Analysis

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