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SAFR-AV: Safety Analysis of Autonomous Vehicles Using Real World Data: An End-to-End Solution for Real World Data Driven Scenario-Based Testing for Pre-Certification of AV Stacks

Topics: Analytics for Intelligent Transportation; Autonomous Vehicles and Automated Driving; Big Data & Vehicle Analytics; Driver Behavior Analysis; Traffic and Vehicle Data Collection and Processing

Authors: Sagar Pathrudkar ; Saadhana Venkataraman ; Deepika Kanade ; Aswin Ajayan ; Palash Gupta ; Shehzaman Khatib ; Vijaya Indla and Saikat Mukherjee

Affiliation: Siemens Technology, India

Keyword(s): Autonomous Vehicles, Verification, Validation, Real2Sim, Scenario based Testing, Scenario Analysis, Scenario Variations, Large Scale AV Data Management.

Abstract: One of the major impediments in deployment of Autonomous Driving Systems (ADS) is their safety and reliability. The primary reason for the complexity of testing ADS is that it operates in an open world characterized by its non-deterministic, high-dimensional and non-stationary nature where the actions of other actors in the environment are uncontrollable from the ADS’s perspective. This leads to a state space explosion problem and one way of mitigating this problem is by concretizing the scope for the system under test (SUT) by testing for a set of behavioral competencies which an ADS must demonstrate. A popular approach to testing ADS is scenario-based testing where the ADS is presented with driving scenarios from real world (and synthetically generated) data and expected to meet defined safety criteria while navigating through the scenario. We present SAFR-AV, an end-to-end ADS testing platform to enable scenario-based ADS testing. Our work addresses key real-world challenges of bu ilding an efficient large scale data ingestion pipeline and search capability to identify scenarios of interest from real world data, creating digital twins of the real-world scenarios to enable Software-in-the-Loop (SIL) testing in ADS simulators and, identifying key scenario parameter distributions to enable optimization of scenario coverage. These along with other modules of SAFR-AV would allow the platform to provide ADS pre-certifications. (More)

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Paper citation in several formats:
Pathrudkar, S., Venkataraman, S., Kanade, D., Ajayan, A., Gupta, P., Khatib, S., Indla, V. and Mukherjee, S. (2023). SAFR-AV: Safety Analysis of Autonomous Vehicles Using Real World Data: An End-to-End Solution for Real World Data Driven Scenario-Based Testing for Pre-Certification of AV Stacks. In Proceedings of the 9th International Conference on Vehicle Technology and Intelligent Transport Systems - VEHITS; ISBN 978-989-758-652-1; ISSN 2184-495X, SciTePress, pages 232-239. DOI: 10.5220/0011838800003479

@conference{vehits23,
author={Sagar Pathrudkar and Saadhana Venkataraman and Deepika Kanade and Aswin Ajayan and Palash Gupta and Shehzaman Khatib and Vijaya Indla and Saikat Mukherjee},
title={SAFR-AV: Safety Analysis of Autonomous Vehicles Using Real World Data: An End-to-End Solution for Real World Data Driven Scenario-Based Testing for Pre-Certification of AV Stacks},
booktitle={Proceedings of the 9th International Conference on Vehicle Technology and Intelligent Transport Systems - VEHITS},
year={2023},
pages={232-239},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011838800003479},
isbn={978-989-758-652-1},
issn={2184-495X},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Vehicle Technology and Intelligent Transport Systems - VEHITS
TI - SAFR-AV: Safety Analysis of Autonomous Vehicles Using Real World Data: An End-to-End Solution for Real World Data Driven Scenario-Based Testing for Pre-Certification of AV Stacks
SN - 978-989-758-652-1
IS - 2184-495X
AU - Pathrudkar, S.
AU - Venkataraman, S.
AU - Kanade, D.
AU - Ajayan, A.
AU - Gupta, P.
AU - Khatib, S.
AU - Indla, V.
AU - Mukherjee, S.
PY - 2023
SP - 232
EP - 239
DO - 10.5220/0011838800003479
PB - SciTePress

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