ViSTA: a Framework for Virtual Scenario-based Testing of Autonomous Vehicles

• URL: http://arxiv.org/abs/2109.02529v2
• Date: Tue, 7 Sep 2021 04:46:01 GMT
• Title: ViSTA: a Framework for Virtual Scenario-based Testing of Autonomous Vehicles
• Authors: Andrea Piazzoni, Jim Cherian, Mohamed Azhar, Jing Yew Yap, James Lee Wei Shung, Roshan Vijay
• Abstract summary: We present ViSTA, a framework for Virtual Scenario-based Testing of Autonomous Vehicles (AV) We describe a comprehensive test case generation approach that facilitates the design of special-purpose scenarios with meaningful parameters. We describe how to automate the execution of test cases, and analyze the performance of the AV under these test cases.
• Score: 2.20200533591633
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we present ViSTA, a framework for Virtual Scenario-based Testing of Autonomous Vehicles (AV), developed as part of the 2021 IEEE Autonomous Test Driving AI Test Challenge. Scenario-based virtual testing aims to construct specific challenges posed for the AV to overcome, albeit in virtual test environments that may not necessarily resemble the real world. This approach is aimed at identifying specific issues that arise safety concerns before an actual deployment of the AV on the road. In this paper, we describe a comprehensive test case generation approach that facilitates the design of special-purpose scenarios with meaningful parameters to form test cases, both in automated and manual ways, leveraging the strength and weaknesses of either. Furthermore, we describe how to automate the execution of test cases, and analyze the performance of the AV under these test cases.

Related papers

• AutoSurvey: Large Language Models Can Automatically Write Surveys [77.0458309675818]
  This paper introduces AutoSurvey, a speedy and well-organized methodology for automating the creation of comprehensive literature surveys. Traditional survey paper creation faces challenges due to the vast volume and complexity of information. Our contributions include a comprehensive solution to the survey problem, a reliable evaluation method, and experimental validation demonstrating AutoSurvey's effectiveness.
  arXiv  Detail & Related papers  (2024-06-10T12:56:06Z)
• A Requirements-Driven Platform for Validating Field Operations of Small Uncrewed Aerial Vehicles [48.67061953896227]
  DroneReqValidator (DRV) allows sUAS developers to define the operating context, configure multi-sUAS mission requirements, specify safety properties, and deploy their own custom sUAS applications in a high-fidelity 3D environment. The DRV Monitoring system collects runtime data from sUAS and the environment, analyzes compliance with safety properties, and captures violations.
  arXiv  Detail & Related papers  (2023-07-01T02:03:49Z)
• Identifying and Explaining Safety-critical Scenarios for Autonomous Vehicles via Key Features [5.634825161148484]
  This paper uses Instance Space Analysis (ISA) to identify the significant features of test scenarios that affect their ability to reveal the unsafe behaviour of AVs. ISA identifies the features that best differentiate safety-critical scenarios from normal driving and visualises the impact of these features on test scenario outcomes (safe/unsafe) in 2D. To test the predictive ability of the identified features, we train five Machine Learning classifiers to classify test scenarios as safe or unsafe.
  arXiv  Detail & Related papers  (2022-12-15T00:52:47Z)
• Learning to Generalize across Domains on Single Test Samples [126.9447368941314]
  We learn to generalize across domains on single test samples. We formulate the adaptation to the single test sample as a variational Bayesian inference problem. Our model achieves at least comparable -- and often better -- performance than state-of-the-art methods on multiple benchmarks for domain generalization.
  arXiv  Detail & Related papers  (2022-02-16T13:21:04Z)
• ADAPQUEST: A Software for Web-Based Adaptive Questionnaires based on Bayesian Networks [70.79136608657296]
  ADAPQUEST is a software tool written in Java for the development of adaptive questionnaires based on Bayesian networks. It embeds dedicated elicitation strategies to simplify the elicitation of the questionnaire parameters. An application of this tool for the diagnosis of mental disorders is also discussed.
  arXiv  Detail & Related papers  (2021-12-29T09:50:44Z)
• A Survey on Scenario-Based Testing for Automated Driving Systems in High-Fidelity Simulation [26.10081199009559]
  Testing the system on the road is the closest to real-world and desirable approach, but it is incredibly costly. 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.
  arXiv  Detail & Related papers  (2021-12-02T03:41:33Z)
• Addressing the IEEE AV Test Challenge with Scenic and VerifAI [10.221093591444731]
  This paper summarizes our formal approach to testing autonomous vehicles (AVs) in simulation for the IEEE AV Test Challenge. We demonstrate a systematic testing framework leveraging our previous work on formally-driven simulation for intelligent cyber-physical systems.
  arXiv  Detail & Related papers  (2021-08-20T04:51:27Z)
• Generating and Characterizing Scenarios for Safety Testing of Autonomous Vehicles [86.9067793493874]
  We propose efficient mechanisms to characterize and generate testing scenarios using a state-of-the-art driving simulator. We use our method to characterize real driving data from the Next Generation Simulation (NGSIM) project. We rank the scenarios by defining metrics based on the complexity of avoiding accidents and provide insights into how the AV could have minimized the probability of incurring an accident.
  arXiv  Detail & Related papers  (2021-03-12T17:00:23Z)
• AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehicles [76.46575807165729]
  We propose AdvSim, an adversarial framework to generate safety-critical scenarios for any LiDAR-based autonomy system. By simulating directly from sensor data, we obtain adversarial scenarios that are safety-critical for the full autonomy stack.
  arXiv  Detail & Related papers  (2021-01-16T23:23:12Z)
• Towards Automated Safety Coverage and Testing for Autonomous Vehicles with Reinforcement Learning [0.3683202928838613]
  Validation puts the autonomous vehicle system to the test in scenarios or situations that the system would likely encounter in everyday driving. We propose using reinforcement learning (RL) to generate failure examples and unexpected traffic situations for the AV software implementation.
  arXiv  Detail & Related papers  (2020-05-22T19:00:38Z)
• Formal Scenario-Based Testing of Autonomous Vehicles: From Simulation to the Real World [8.498542964344987]
  We present a new approach to automated scenario-based testing of the safety of autonomous vehicles. Our approach is based on formal methods, combining formal specification of scenarios and safety properties.
  arXiv  Detail & Related papers  (2020-03-17T14:17:52Z)

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.