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E. Papadimitriou

119 records found

Frequently Used Vehicle Controls While Driving

A Real-World Driving Study Assessing Internal Human–Machine Interface Task Frequencies and Influencing Factors

Human–Machine Interfaces (HMIs) in passenger cars have become more complex over the years, with touch screens replacing physical buttons and with layered menu-structures. This can lead to distractions. The purpose of this study is to investigate how often vehicle controls are use ...
Autonomous vessels are becoming paramount to ocean transportation, while they also face complex risks in dynamic marine environments. Machine learning plays a crucial role in enhancing maritime safety by leveraging its data analysis and predictive capabilities. However, there has ...
As automated vehicles (AVs) become increasingly popular, the question arises as to how cyclists will interact with such vehicles. This study investigated (1) whether cyclists spontaneously notice if a vehicle is driverless, (2) how well they perform a driver-detection task when e ...

Enhancing collision avoidance in mixed waterborne transport

Human-mimic navigation and decision-making by autonomous vessels

Collision avoidance in maritime navigation, particularly between autonomous and conventional vessels, involves iterative and dynamic processes. Traditional path planning models often neglect the behaviours of surrounding vessels, while path predictive models tend to ignore ship i ...
Automated Driving Systems (ADS) are aimed to improve traffic efficiency and safety, however these systems are not yet capable of handling all driving tasks in all types of road conditions. The role of a human driver remains crucial in taking over control, if an ADS fails or reach ...

Unfolding the dynamics of driving behavior

A machine learning analysis from Germany and Belgium

The i-DREAMS project focuses on establishing a framework known as the ‘Safety Tolerance Zone (STZ)’ to ensure drivers operate within safe boundaries. This study compares Long-Short-Term-Memory Networks and shallow Neural Networks to assess participants’ safety levels during i-DRE ...
Maritime Autonomous Surface Ships (MASS) have gained much attention as a safer and more efficient mode of transportation and a potential solution to reduce the workload of seafarers. Despite the highly sophisticated autonomous systems that enable MASS to make independent decision ...
Existing models for correlating global mortality rates with underlying country-specific factors overlook the variations in the effects of these factors on mortality across different countries. These may arise from social, cultural, and political complexities which are usually not ...
This study investigates the enhancement of Maritime Autonomous Surface Ships (MASS) navigation and path-planning through the integration of ontology-based knowledge maps (KM) with the Dynamic Window Approach (DWA), a fusion termed KM-DWA. The ontology-based KM model is important ...

Data Handling

Good Practices in the Context of Naturalistic Driving Studies

Naturalistic driving studies (NDS) have recently gained attention as a way of instrumenting vehicles in an unobtrusive way and collecting driving data over long periods of time. Aiming at eventually modeling driving behavior, NDS are often a part of larger scale studies. These st ...
Recent research in transport safety focuses on the processing of large amounts of available data by means of intelligent systems, in order to decrease the number of accidents for transportation users. Several Machine Learning (ML) and Artificial Intelligence (AI) applications hav ...
This study reviews the Artificial Intelligence and Machine Learning approaches developed thus far for driver profile and driving pattern recognition, representing a set of macroscopic and microscopic behaviors respectively, to enhance the understanding of human factors in road sa ...
Driving pattern recognition has been applied for the purposes of driving styles identification and harsh driving events detection. However, the evolution of driving behavior around and especially before such events has not been investigated at a microscopic level. The objective o ...

Exploring the Influence of Signal Countdown Timers on Driver Behavior

An Analysis of Pedestrian–Vehicle Conflicts at Signalized Intersections

Although signal countdown timers (SCTs) are likely to enhance efficiency at signalized intersections, there is little research on how they affect road users’ behavior. The present study explores factors associated with driver behavior through two approaches to examine how SCTs in ...
Driver anticipation plays a crucial role in crashes along horizontal curves. Anticipation is related to road predictability and can be influenced by roadway geometric design. Therefore, it is essential to understand which geometric design elements can influence anticipation and c ...
The safety of maritime autonomous surface ships (MASS) in mixed waterborne transport system (MWTS) depends on effective situational awareness (SA) distribution among MASS, manned ships, and various stakeholders, such as Vessel Traffic Service (VTS), Remote Control Center (RCC) an ...
Driver behavior analytics is an important concept that plays a significant role in the understanding of road crashes. This paper investigates the optimal number of driver profiles to understand the most important characteristics that differentiate drivers and extract useful insig ...
Sleepiness is a common human factor among truck drivers resulting from sleep loss or time of day and causing impairment in vigilance, attention, and driving performance. While driver sleepiness may be associated with increased risk on the road, sleepy drivers may drive more cauti ...