Quelltext der Seite ASYDE: An Argumentation-based System for classifYing Driving bEhaviors

{{Veranstaltung
|Titel EN=ASYDE: An Argumentation-based System for classifYing Driving bEhaviors
|Beschreibung EN=Abstract: 
Examining the influence of human behavior on road safety is a matter of considerable interest. As
studies in this field have demonstrated a correlation between driver behavior and road safety over
the past decades, there is a compelling need to develop a software system capable of impartially
identifying the typical driving behavior of motorists. The objective of this research is to create one
or more algorithms capable of recognizing and classifying a driver's typical driving behavior by
integrating various data sources. Since different data sources may present conflicting information
due to noise errors or failures, our goal is to resolve such ambiguities by employing artificial
intelligence paradigms. We are currently exploring the potential use of a well-known AI framework,
namely Dung’s Abstract Argumentation Framework (AAF). Argumentation Frameworks, indeed,
provide a valuable tool for analyzing and assessing conflicting pieces of information, enabling us to
draw more accurate and dependable conclusions. Our argumentation-based system provides the
user with a driving certificate describing their driving behavior. In the certificate, we outline, for
each predefined class C of driving behavior (e.g., calm driving, normal driving, aggressive driving),
the minimum and maximum percentage of time points where the driving behavior falls in C. The
minimum percentage represents the time points where driver behavior has been classified in C
without uncertainty, whereas the maximum percentage encodes the time points where some other
classes in addition to C have been identified, allowing for multiple interpretations.
The experimental evaluation baked up the need for intervals, as for several time points ambiguity in
identifying classes occurred, due to different measures collected by different sensors leading to
conflicting information. Therefore, assigning a single class is not always possible; instead, multiple
possible interpretations need to be considered.

The talk will take place in a hybrid fashion, physically in the APB room 2026, and online through the link:
https://bbb.tu-dresden.de/b/pio-zwt-smp-aus
|Veranstaltungsart=Vortrag
|Start=2024/02/01 11:00:00
|Ende=2024/02/01 12:00:00
|Raum=APB room 2026
|Vortragender=Giuseppina Monterosso
|Event series=Research Seminar Logic and AI
|In News anzeigen=1
}}