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A new modeling approach for predicting vehicle-based safety threats

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journal contribution
posted on 2022-12-05, 13:26 authored by Nicolette Formosa, Mohammed Quddus, Stephen Ison, Andrew TimmisAndrew Timmis
Existing autonomous driving systems of intelligent vehicles such as advanced driver assistant systems (ADAS) assess and quantify the level of potential safety threats. However, they may not be able to plan the best response to unexpected dangerous situations and do not have the ability to cope with uncertainties since not all vehicles can always keep a safe gap from preceding vehicles and drive at a desired velocity. Previous research has not taken such uncertainties into account, it is, therefore, necessary to develop models which are not restricted by the predefined movement patterns of a vehicle. Existing systems are based on a model that estimates the threat level based only on one factor Time-To-Collision (TTC). This approach is limited since it cannot handle all scenarios and ignores all uncertainties. To overcome these limitations, this paper utilised deep learning to develop a range of models that rely on a group of factors to reliably estimate the threat level and predict conflicts under uncertainty using the concept of looming '. Comparative analyses were undertaken by incorporating new varying input factors to each model (e.g., surrogate safety measures, vehicle kinematics, macroscopic traffic data). Real-world experiments demonstrated that adding new factors increases the reliability and sensitivity of the models. Results also indicated that the models that consider looming provide low false alarm rate extending their applications for a wider spectrum of traffic scenarios. This is paramount for ADAS as uncertainties are inherent in the deployment of connected and autonomous vehicles in a mixed traffic stream.

History

School

  • Architecture, Building and Civil Engineering

Published in

IEEE Transactions on Intelligent Transportation Systems

Volume

23

Issue

10

Pages

18175 - 18185

Publisher

IEEE

Version

  • AM (Accepted Manuscript)

Rights holder

© IEEE

Publisher statement

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Acceptance date

2022-02-22

Publication date

2022-03-14

Copyright date

2022

ISSN

1524-9050

eISSN

1558-0016

Language

  • en

Depositor

Dr Andrew Timmis. Deposit date: 28 November 2022