Safety Performance Boundary Identification of Highly Automated Vehicles

Safety Performance Boundary Identification of Highly Automated Vehicles: A Surrogate Model-Based Gradient Descent Searching Approach

Wang, Y. (TU Delft Transport and Planning; Tongji University)
Yu, Rongjie (Tongji University)
Qiu, Shuhan (Tongji University)
SUN, J. (Tongji University)
Farah, H. (TU Delft Transport and Planning)

Transport and Planning

2022

Highly automated vehicles (HAVs) have been introduced to the transportation system for the purpose of providing safer mobility. Considering the expected long co-existence period of HAVs and human-driven vehicles (HDVs), the safety operation of HAVs interacting with HDVs needs to be verified. To achieve this, HAVs' Operational Design Domain (ODD) needs to be identified under the scenario-based testing framework. In this study, a novel testing framework aiming at identifying the Safety performance boundary (SPB) is proposed, which assures the coverage of safety-critical scenarios and compatible with the black-box feature of HAV control algorithm. A surrogate model was utilized to approximate the safety performance of HAV, and a gradient descent searching algorithm was employed to accelerate the search for SPB. For empirical analyses, a three-vehicle following scenario was adopted and the Intelligent Driver Model (IDM) was tested as a case study. The results show that only 4% of the total scenarios are required to establish a reliable surrogate model. And the gradient descent algorithm was able to establish the SPB by identifying 97.42% of collision scenarios and only false alarming 0.29% of non-collision scenarios. Furthermore, the concept of safety tolerance was proposed to measure the possibilities of boundary scenarios dropping in safety performance. The applications of helping to construct ODD and compare different control algorithms were discussed. It shows that the IDM performs better than the Wiedemann 99 (W99) model with larger ODD.

Adaptation models
Aerospace electronics
gradient descent
Highly automated vehicle
Life estimation
operational design domain
Roads
Safety
safety performance boundary
Sampling methods
surrogate model
Testing

http://resolver.tudelft.nl/uuid:acd2ba81-df63-4e03-a4b9-538c0ee69ca3

https://doi.org/10.1109/TITS.2022.3191088

2023-07-01

1524-9050

IEEE Transactions on Intelligent Transportation Systems, 23 (12), 23809-23820

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2022 Y. Wang, Rongjie Yu, Shuhan Qiu, J. SUN, H. Farah