Anih, J. orcid.org/0000-0002-0938-1036, Kolekar, S., Dargahi, T. orcid.org/0000-0002-0908-6483 et al. (3 more authors) (Cover date: 2023) Deriving Environmental Risk Profiles for Autonomous Vehicles From Simulated Trips. IEEE Access, 11. pp. 38385-38398. ISSN 2169-3536
Abstract
The commercial adoption of Autonomous Vehicles (AVs) and the positive impact they are expected to have on traffic safety depends on appropriate insurance products due to the high potential losses. A significant proportion of these losses are expected to occur from the out-of-distribution risks which arise from situations outside the AV’s training experience. Traditional vehicle insurance products (for human-driven vehicles) rely on large data sets of drivers’ background and historical incidents. However, the lack of such datasets for AVs makes it imperative to exploit the ability to deploy AVs in simulated environments. In this paper, the data collected by deploying Autonomous Driving Systems (ADSs) in simulated environments is used to develop models to answer two questions: (1) how risky a road Section is for an AV to drive? and (2) how does the risk profile vary with different (SAE levels) of ADSs? A simulation pipeline was built on the CARLA (Car Learning to Act): an open-source simulator for autonomous driving research. The environment was specified using parameters such as weather, lighting, traffic density, traffic flow, no. of lanes, etc. A metric - risk factor was defined as a combination of harsh accelerations/braking, inverse Time to Collision, and inverse Time Headway to capture the crashes and near-crashes. To assess the difference between ADSs, two ADSs: OpenPilot (Level 2/3) and Pylot (Level 4) were implemented in the simulator. The results (from data and model predictions) show that the trends in the relation between the environment features and risk factor for an AV are similar to those observed for human drivers (e.g., risk increases with traffic flow). The models also showed that junctions were a risk hot-spot for both ADSs. The feature importance of the model revealed that the Level 2/3 ADS is more sensitive to no. of lanes and the Level 4 ADS is sensitive to traffic flow. Such differences in feature importance provide valuable insights into the risk characteristics of different ADSs. In the future, this base model will be extended to include other features (other than the environment), e.g., take over requests, and also address the deficiencies of the current simulation data in terms of insensitivity to weather and lighting.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | This item is protected by copyright. This is an open access article under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. |
Keywords: | Autonomous vehicle, insurance, risk modelling, simulation, traffic flow, traffic density |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mechanical Engineering (Leeds) > Institute of Engineering Thermofluids, Surfaces & Interfaces (iETSI) (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 07 Sep 2023 10:46 |
Last Modified: | 07 Sep 2023 10:46 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Identification Number: | 10.1109/access.2023.3261245 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:203146 |
Download
Filename: Deriving_Environmental_Risk_Profiles_for_Autonomous_Vehicles_From_Simulated_Trips.pdf
Licence: CC-BY 4.0