Soft-Body Modeling: A Scalable and Efficient Formulation for Control-Oriented Simulation of Electric Vehicles

Dalboni, Matteo; Soldati, Alessandro

doi:10.1109/itec.2019.8790473

Cited by 1 publication

(1 citation statement)

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“…Soft-body simulations improve over rigid-body simulations and can simulate a wide range of simulation cases in addition to primitive body motions and rotations. As stated by Dalboni and Soldati (Dalboni and Soldati 2019), soft-body simulations can simulate body deformations, anisotropic mass distributions, and inertia, which are essential in many CPS domains. For SDCs, soft-body simulations are a better fit for simulating safety-critical driving scenarios ) and, like rigid-body simulations, they can be coupled with powerful rendering engines to achieve photo-realism (e.g., BeamNG GmbH (2022)).…”

Section: Cps Simulation Technologiesmentioning

confidence: 99%

Machine learning-based test selection for simulation-based testing of self-driving cars software

Khatiri

Birchler

Bosshard³

et al. 2023

Empir Software Eng

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Simulation platforms facilitate the development of emerging Cyber-Physical Systems (CPS) like self-driving cars (SDC) because they are more efficient and less dangerous than field operational test cases. Despite this, thoroughly testing SDCs in simulated environments remains challenging because SDCs must be tested in a sheer amount of long-running test cases. Past results on software testing optimization have shown that not all the test cases contribute equally to establishing confidence in test subjects’ quality and reliability, and the execution of “safe and uninformative” test cases can be skipped to reduce testing effort. However, this problem is only partially addressed in the context of SDC simulation platforms. In this paper, we investigate test selection strategies to increase the cost-effectiveness of simulation-based testing in the context of SDCs. We propose an approach called SDC-Scissor (SDC coS t-effeC tI ve teS t S electOR) that leverages Machine Learning (ML) strategies to identify and skip test cases that are unlikely to detect faults in SDCs before executing them. Our evaluation shows that SDC-Scissor outperforms the baselines. With the Logistic model, we achieve an accuracy of 70%, a precision of 65%, and a recall of 80% in selecting tests leading to a fault and improved testing cost-effectiveness. Specifically, SDC-Scissor avoided the execution of 50% of unnecessary tests as well as outperformed two baseline strategies. Complementary to existing work, we also integrated SDC-Scissor into the context of an industrial organization in the automotive domain to demonstrate how it can be used in industrial settings.

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Section: Cps Simulation Technologiesmentioning

confidence: 99%