Title
PRISMA: A novel approach for deriving probabilistic surrogate safety measures for risk evaluation
Author
de Gelder, E. (TU Delft Delft Center for Systems and Control; TU Delft Team Bart De Schutter)
Adjenughwure, Kingsley (TNO)
Manders, Jeroen (TNO)
Snijders, Ron (TNO)
Paardekooper, Jan Pieter (TNO; Radboud Universiteit Nijmegen)
Op den Camp, Olaf (TNO)
Tejada, Arturo (Eindhoven University of Technology; TNO)
De Schutter, B.H.K. (TU Delft Delft Center for Systems and Control)
Department
Delft Center for Systems and Control
Date
2023
Abstract
Surrogate Safety Measures (SSMs) are used to express road safety in terms of the safety risk in traffic conflicts. Typically, SSMs rely on assumptions regarding the future evolution of traffic participant trajectories to generate a measure of risk, restricting their applicability to scenarios where these assumptions are valid. In response to this limitation, we present the novel Probabilistic RISk Measure derivAtion (PRISMA) method. The objective of the PRISMA method is to derive SSMs that can be used to calculate in real time the probability of a specific event (e.g., a crash). The PRISMA method adopts a data-driven approach to predict the possible future traffic participant trajectories, thereby reducing the reliance on specific assumptions regarding these trajectories. Since the PRISMA is not bound to specific assumptions, the PRISMA method offers the ability to derive multiple SSMs for various scenarios. The occurrence probability of the specified event is based on simulations and combined with a regression model, this enables our derived SSMs to make real-time risk estimations. To illustrate the PRISMA method, an SSM is derived for risk evaluation during longitudinal traffic interactions. Since there is no known method to objectively estimate risk from first principles, i.e., there is no known risk ground truth, it is very difficult, if not impossible, to objectively compare the relative merits of two SSMs. Instead, we provide a method for benchmarking our derived SSM with respect to expected risk trends. The application of the benchmarking illustrates that the SSM matches the expected risk trends. Whereas the derived SSM shows the potential of the PRISMA method, future work involves applying the approach for other types of traffic conflicts, such as lateral traffic conflicts or interactions with vulnerable road users.
Subject
Data driven
Driving risk
Risk Assessment
Surrogate safety measure
To reference this document use:
http://resolver.tudelft.nl/uuid:256ffbad-8a0a-4d54-98d6-17e28f91620f
DOI
https://doi.org/10.1016/j.aap.2023.107273
Embargo date
2024-03-08
ISSN
0001-4575
Source
Accident Analysis & Prevention, 192
Bibliographical note
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.
Part of collection
Institutional Repository
Document type
journal article
Rights
© 2023 E. de Gelder, Kingsley Adjenughwure, Jeroen Manders, Ron Snijders, Jan Pieter Paardekooper, Olaf Op den Camp, Arturo Tejada, B.H.K. De Schutter