Towards the application of evidence accumulation models in the design of (semi-)autonomous driving systems – an attempt to overcome the sample size roadblock
Bibliographic Data
| ID | 21642492 |
|---|---|
| Authors | Dominik Bachmann (0000-0001-5781-6009, Utrecht University, corresponding author), Leendert van Maanen (0000-0001-9120-1075, Utrecht University) |
| Year | 2024 |
| Volume | 185 |
| Pages | 103220 |
| Publication date | 2024-05-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Human-Computer Studies (JOURNAL) |
| Journal identifiers | ISSN: 1071-5819 • E-ISSN: 1095-9300 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.ijhcs.2024.103220 |
| OpenAlex | W4390739014 |
| Language | EN |
| References cited | 39 |
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
Bayesian probability · Data science · Information processing · Machine learning · Operations research · Population · Computer Science · Engineering · Human-Automation Interaction and Safety · Psychology · Safety Warnings and Signage · Traffic and Road Safety · Artificial Intelligence
| Citation velocity | historical |
|---|---|
| Highly cited | No |