Towards the application of evidence accumulation models in the design of (semi-)autonomous driving systems - an attempt to overcome the sample size roadblock

D Bachmann*, L van Maanen*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

For the foreseeable future, automated vehicles (AVs) will coexist on the roads with human drivers. To avoid accidents, AVs will require knowledge on how human drivers typically make high-stakes and time-sensitive decisions (e.g., whether or not to brake). Providing such insights could be statistical models designed to explain human information processing and decision making. This paper attempts to address a roadblock that prevents one class of such "cognitive models", evidence accumulation models (EAMs), from being widely applied in the design of AV systems: their high demands for data. Specifically, we investigate whether Bayesian hierarchical modeling can be used to determine a person's characteristics, if we only have limited data about their behavior but extensive data on other (comparable) people's behaviors. Leveraging a simulation study and a reanalysis of experimental data, we find that most parameters of Decision Diffusion Models (a class of EAMs) – representing information processing components – can be adequately estimated with as few as 20 observations, if prior information regarding the decision-making processes of the population is incorporated. Subsequently, we discuss the implications of our findings for the modeling of traffic situations.
Original languageEnglish
Article number103220
Number of pages13
JournalInternational journal of human-computer studies
Volume185
DOIs
Publication statusPublished - May 2024

Keywords

  • Automated vehicles
  • Bayesian hierarchical modeling
  • Decision Diffusion models
  • Evidence accumulation models
  • Human-automated vehicle interaction
  • Sample size

Fingerprint

Dive into the research topics of 'Towards the application of evidence accumulation models in the design of (semi-)autonomous driving systems - an attempt to overcome the sample size roadblock'. Together they form a unique fingerprint.

Cite this