Hardware-in-the-Loop Autonomous Driving Simulation Without Real-Time Constraints

Brogle, Craig; Zhang, Chao; Lim, Kai Li; Bräunl, Thomas

doi:10.1109/tiv.2019.2919457

Cited by 27 publications

(14 citation statements)

References 8 publications

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“…Software-in-loop, on the other hand, tests the system in a simulated environment. Software-in-loop with physical hardware is referred to as hardware-in-loop [36], [37]. In an academic research environment, software-in-loop is the most popular one since it is more accessible, flexible, and scalable for scenario creation and simulation.…”

Section: Ads Testingmentioning

confidence: 99%

A Survey on Scenario-Based Testing for Automated Driving Systems in High-Fidelity Simulation

Zhong¹,

Tang²,

Zhou³

et al. 2021

Preprint

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Automated Driving Systems (ADSS) have seen rapid progress in recent years. To ensure the safety and reliability of these systems, extensive testings are being conducted before their future mass deployment. Testing the system on the road is the closest to real-world and desirable approach, but it is incredibly costly. Also, it is infeasible to cover rare corner cases using such real-world testing. Thus, a popular alternative is to evaluate an ADS's performance in some well-designed challenging scenarios, a.k.a. scenario-based testing. High-fidelity simulators have been widely used in this setting to maximize flexibility and convenience in testing what-if scenarios. Although many works have been proposed offering diverse frameworks/methods for testing specific systems, the comparisons and connections among these works are still missing. To bridge this gap, in this work, we provide a generic formulation of scenario-based testing in high-fidelity simulation and conduct a literature review on the existing works. We further compare them and present the open challenges as well as potential future research directions.

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Section: Ads Testingmentioning

confidence: 99%

A Survey on Scenario-Based Testing for Automated Driving Systems in High-Fidelity Simulation

Zhong¹,

Tang²,

Zhou³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…For AI systems that are deployed on embedded or otherwise boxed hardware, it is important to understand system behavior when they are run on the hardware used in real-world scenarios. Hardware-in-the-loop simulations can help developers understand system performance when it is actually run on the chips, sensors, and actuators in a simulated environment, which is particularly helpful for latency-and power-critical systems like autonomous driving systems [58,62]. Taking real-world simulations one step further, one can also construct controlled real-world environments for fully integrated AI systems to roam around in (e.g., test tracks for self-driving cars with road signs and dummy obstacles), further providing more realistic measurements and assurance of performance before releasing such systems to actual users.…”

Section: Development By Simulationmentioning

confidence: 99%

Trustworthy AI: From Principles to Practices

Li¹,

Pan²,

Liu³

et al. 2021

Preprint

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Fast developing artificial intelligence (AI) technology has enabled various applied systems deployed in the real world, impacting people's everyday lives. However, many current AI systems were found vulnerable to imperceptible attacks, biased against underrepresented groups, lacking in user privacy protection, etc., which not only degrades user experience but erodes the society's trust in all AI systems. In this review, we strive to provide AI practitioners a comprehensive guide towards building trustworthy AI systems. We first introduce the theoretical framework of important aspects of AI trustworthiness, including robustness, generalization, explainability, transparency, reproducibility, fairness, privacy preservation, alignment with human values, and accountability. We then survey leading approaches in these aspects in the industry. To unify the current fragmented approaches towards trustworthy AI, we propose a systematic approach that considers the entire lifecycle of AI systems, ranging from data acquisition to model development, to development and deployment, finally to continuous monitoring and governance. In this framework, we offer concrete action items to practitioners and societal stakeholders (e.g., researchers and regulators) to improve AI trustworthiness. Finally, we identify key opportunities and challenges in the future development of trustworthy AI systems, where we identify the need for paradigm shift towards comprehensive trustworthy AI systems.

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“…For instance, HiL has been employed to emulate vehicular ECUs, improve plant models, facilitate rapid‐prototyping, and perform standardised tests [80, 81]. Research on the use of HiL for assisted and autonomous driving has also been presented [82–84]. An analysis of mechanical–electrical traction shift in HEVs is exhibited in [85].…”

Section: Future Research and Trends On Vehicular Edsmentioning

confidence: 99%

Development of a computer platform for visualisation and simulation of vehicular DC distribution systems

Dominguez

Mantilla-Pérez

Gimenez³

et al. 2020

IET Electrical Systems in Transportation

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Hardware-in-the-Loop Autonomous Driving Simulation Without Real-Time Constraints

Cited by 27 publications

References 8 publications

A Survey on Scenario-Based Testing for Automated Driving Systems in High-Fidelity Simulation

A Survey on Scenario-Based Testing for Automated Driving Systems in High-Fidelity Simulation

Trustworthy AI: From Principles to Practices

Development of a computer platform for visualisation and simulation of vehicular DC distribution systems

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