Coverage based testing for V&V and Safety Assurance of Self-driving Autonomous Vehicles: A Systematic Literature Review

• URL: http://arxiv.org/abs/2103.04364v1
• Date: Sun, 7 Mar 2021 14:23:04 GMT
• Title: Coverage based testing for V&V and Safety Assurance of Self-driving Autonomous Vehicles: A Systematic Literature Review
• Authors: Zaid Tahir, Rob Alexander
• Abstract summary: Self-driving Autonomous Vehicles (SAVs) are gaining more interest each passing day by the industry as well as the general public. One of the major hurdles in the way of SAVs making it to the public roads is the lack of confidence of public in the safety aspect of SAVs. Researchers around the world have used coverage-based testing for Verification and Validation (V&V) and safety assurance of SAVs.
• Score: 1.1602089225841632
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Self-driving Autonomous Vehicles (SAVs) are gaining more interest each passing day by the industry as well as the general public. Tech and automobile companies are investing huge amounts of capital in research and development of SAVs to make sure they have a head start in the SAV market in the future. One of the major hurdles in the way of SAVs making it to the public roads is the lack of confidence of public in the safety aspect of SAVs. In order to assure safety and provide confidence to the public in the safety of SAVs, researchers around the world have used coverage-based testing for Verification and Validation (V&V) and safety assurance of SAVs. The objective of this paper is to investigate the coverage criteria proposed and coverage maximizing techniques used by researchers in the last decade up till now, to assure safety of SAVs. We conduct a Systematic Literature Review (SLR) for this investigation in our paper. We present a classification of existing research based on the coverage criteria used. Several research gaps and research directions are also provided in this SLR to enable further research in this domain. This paper provides a body of knowledge in the domain of safety assurance of SAVs. We believe the results of this SLR will be helpful in the progression of V&V and safety assurance of SAVs.

Related papers

• Achieving the Safety and Security of the End-to-End AV Pipeline [9.714684348295707]
  This paper provides a thorough description of the current state of autonomous vehicle (AV) safety and security research. We provide sections for the primary research questions that concern this research area, including AV surveillance, sensor system reliability, security of the AV stack, algorithmic robustness, and safe environment interaction. At the conclusion of each section, we propose future research questions that still lack conclusive answers.
  arXiv  Detail & Related papers  (2024-09-05T20:14:22Z)
• Safetywashing: Do AI Safety Benchmarks Actually Measure Safety Progress? [59.96471873997733]
  We propose an empirical foundation for developing more meaningful safety metrics and define AI safety in a machine learning research context. We aim to provide a more rigorous framework for AI safety research, advancing the science of safety evaluations and clarifying the path towards measurable progress.
  arXiv  Detail & Related papers  (2024-07-31T17:59:24Z)
• Work-in-Progress: Crash Course: Can (Under Attack) Autonomous Driving Beat Human Drivers? [60.51287814584477]
  This paper evaluates the inherent risks in autonomous driving by examining the current landscape of AVs. We develop specific claims highlighting the delicate balance between the advantages of AVs and potential security challenges in real-world scenarios.
  arXiv  Detail & Related papers  (2024-05-14T09:42:21Z)
• A Safe Harbor for AI Evaluation and Red Teaming [124.89885800509505]
  Some researchers fear that conducting such research or releasing their findings will result in account suspensions or legal reprisal. We propose that major AI developers commit to providing a legal and technical safe harbor. We believe these commitments are a necessary step towards more inclusive and unimpeded community efforts to tackle the risks of generative AI.
  arXiv  Detail & Related papers  (2024-03-07T20:55:08Z)
• A novel framework for adaptive stress testing of autonomous vehicles in highways [3.2112502548606825]
  We propose a novel framework to explore corner cases that can result in safety concerns in a highway traffic scenario. We develop a new reward function for DRL to guide the AST in identifying crash scenarios based on the collision probability estimate. The proposed framework is further integrated with a new driving model enabling us to create more realistic traffic scenarios.
  arXiv  Detail & Related papers  (2024-02-19T04:02:40Z)
• A Counterfactual Safety Margin Perspective on the Scoring of Autonomous Vehicles' Riskiness [52.27309191283943]
  This paper presents a data-driven framework for assessing the risk of different AVs' behaviors. We propose the notion of counterfactual safety margin, which represents the minimum deviation from nominal behavior that could cause a collision.
  arXiv  Detail & Related papers  (2023-08-02T09:48:08Z)
• Safety of autonomous vehicles: A survey on Model-based vs. AI-based approaches [1.370633147306388]
  It is proposed to review research on relevant methods and concepts defining an overall control architecture for AVs. It is intended through this reviewing process to highlight researches that use either model-based methods or AI-based approaches. This paper ends with discussions on the methods used to guarantee the safety of AVs namely: safety verification techniques and the standardization/generalization of safety frameworks.
  arXiv  Detail & Related papers  (2023-05-29T08:05:32Z)
• Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys [71.28049144033773]
  We propose a Survey of Surveys (SoS) for total technologies of autonomous driving (AD) and intelligent vehicles (IVs) This article is the first SoS with milestones in AD and IVs, which constitutes our complete research work together with two other technical surveys.
  arXiv  Detail & Related papers  (2023-03-30T08:31:22Z)
• Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements [76.80453043969209]
  This survey presents a framework for safety research pertaining to large models. We begin by introducing safety issues of wide concern, then delve into safety evaluation methods for large models. We explore the strategies for enhancing large model safety from training to deployment.
  arXiv  Detail & Related papers  (2023-02-18T09:32:55Z)
• Fast or Accurate? Governing Conflicting Goals in Highly Autonomous Vehicles [3.3605894204326994]
  We argue that understanding the fundamental engineering trade-off between accuracy and speed in AVs is critical for policymakers to regulate the uncertainty and risk inherent in AV systems. This will shift the balance of power from manufacturers to the public by facilitating effective regulation, reducing barriers to tort recovery, and ensuring that public values like safety and accountability are appropriately balanced.
  arXiv  Detail & Related papers  (2022-08-03T13:24:25Z)
• Assessing Safety-Critical Systems from Operational Testing: A Study on Autonomous Vehicles [3.629865579485447]
  Demonstrating high reliability and safety for safety-critical systems (SCSs) remains a hard problem. We use Autonomous Vehicles (AVs) as a current example to revisit the problem of demonstrating high reliability.
  arXiv  Detail & Related papers  (2020-08-19T19:50:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.