Self-Driving Cars

Sharma, Shubhankar; Arora, Vatsala

doi:10.47760/ijcsmc.2021.v10i10.001

IJCSMC

2021

DOI: 10.47760/ijcsmc.2021.v10i10.001

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Self-Driving Cars

Shubhankar Sharma¹,

Vatsala Arora²

Abstract: Significant improvements in the last decade have greatly advanced self-driving car technology. These new capabilities will have profound global impacts that could markedly change society, not to mention the significant improvements they bring to the overall efficiency, convenience, and safety of our roadways and transportation systems. Addressing self-driving technology-related concerns is important, particularly given these broad potential impacts. In this paper, we are exploring the possibility of.

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Model vs system level testing of autonomous driving systems: a replication and extension study

2023

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Offline model-level testing of autonomous driving software is much cheaper, faster, and diversified than in-field, online system-level testing. Hence, researchers have compared empirically model-level vs system-level testing using driving simulators. They reported the general usefulness of simulators at reproducing the same conditions experienced in-field, but also some inadequacy of model-level testing at exposing failures that are observable only in online mode. In this work, we replicate the reference study on model vs system-level testing of autonomous vehicles while acknowledging several assumptions that we had reconsidered. These assumptions are related to several threats to validity affecting the original study that motivated additional analysis and the development of techniques to mitigate them. Moreover, we also extend the replicated study by evaluating the original findings when considering a physical, radio-controlled autonomous vehicle. Our results show that simulator-based testing of autonomous driving systems yields predictions that are close to the ones of real-world datasets when using neural-based translation to mitigate the reality gap induced by the simulation platform. On the other hand, model-level testing failures are in line with those experienced at the system level, both in simulated and physical environments, when considering the pre-failure site, similar-looking images, and accurate labels.

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Model vs system level testing of autonomous driving systems: a replication and extension study

2023

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TrustFSDV: Framework for Building and Maintaining Trust in Self-Driving Vehicles

Kuru

2022

IEEE Access

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The deployment of fully self-sufficient, self-driving vehicles (SDVs) to city roads in a diverse range of benefit-driven concepts is on the horizon where the higher levels of automation with automated driving systems (ADSs) are being installed on vehicles. The acceptance of SDVs as safetycritical systems to successfully substitute for human drivers requires a high level of trustworthiness in numerous socio-technical aspects. By the time SDVs are available to the general public with challenges and opportunities, the trustworthiness of SDVs in rapidly changing, partially observable, multiagent, stochastic, sequential, dynamic, continuous and unknown environments shall be ensured from the perspective of the stakeholders. This paper analyses the dynamics of trust in SDVs from the perspectives of all stakeholders to result in how the "trustworthiness" of SDVs can be ensured with mitigated risks and dangers (i.e., non-maleficence) and maximised benefits (i.e., beneficence) as a holistic view of all the dimensions and dynamics of SDVs. Besides, a framework, namely, TrustFSDV, that designates a transitional roadmap with delegated responsibilities and liabilities along with the desired performance indicators, is proposed for the stakeholders to pursue in ensuring a sufficient level of trust within the concepts of human-vehicle integration and society-vehicle harmonisation -inducing the advancement of SDVs and acceptance of them by the society -while the future is being revolutionised by SDVs. To conclude the key findings in this research, i) although instant trust can be gained through hard work, it can be lost readily without forward-thinking during the life-cycle of SDVs, ii) a high level of trustworthiness cannot only be achieved by manufacturers, it requires the strict collaboration of all the stakeholders with distributed responsibilities and liabilities while moulding the requirements of the stakeholders in a system under the supervision of related disciplines, iii) the manufacturers along with the press shall not ignore the real technical limitations of the SDV technology when communicating about benefits to the potential customers, and iv) drastic steps are needed to be taken concerning ethical and legal perspectives, which is perceived as the primary concern beyond technology to build and maintain a high level of trustworthiness.INDEX TERMS Autonomous vehicles, self-driving vehicles, autonomy in vehicles, trust in self-driving, vehicle teleportation.

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Self-Driving Cars

Cited by 2 publications

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Model vs system level testing of autonomous driving systems: a replication and extension study

Model vs system level testing of autonomous driving systems: a replication and extension study

TrustFSDV: Framework for Building and Maintaining Trust in Self-Driving Vehicles

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