Results 61 to 70 of about 86,486 (162)

A study on identifying representative trips for mobility service design

IET Intelligent Transport Systems
Recently, with growing interest in urban mobility patterns, the demand for collecting and analysing origin‐destination (OD) data is increasing. Due to the large scale and dimensionality of OD data, there are two issues in analysing the data: big‐data ...
Jeongyun Kim, Sehyun Tak, Jinwon Yoon, Hwasoo Yeo   +3 more
doaj   +1 more source

A Taxonomic Odyssey: Evolution, Criticisms, and Future Directions of Driving Automation Taxonomies – The Case of SAE J3016

Transportation Research Interdisciplinary Perspectives
While the Society of Automotive Engineers (SAE) International’s classification system (J3016) has provided a framework for categorising sustained driving automation systems, concerns have arisen about its clarity and ability to incorporate emerging ...
Soyeon Kim, Fjollë Novakazi, Elisabeth Shi, Ilse M. Harms, Oscar Oviedo-Trespalacios   +4 more
doaj   +1 more source

A Method of Selecting Automated Driving Service Sections Considering Spatial Influence Factors

Applied Sciences
Currently, the level of automated driving technology is level 3 service in Korea, and the level 4 service has been launched overseas and is reaching the stage of commercialization.
Tagyoung Kim, Kyeong-pyo Kang
doaj   +1 more source

Comfort in Automated Driving: Analysis of Driving Style Preference in Automated Driving

, 2017
Over the last years, driving automation has increasingly moved into focus in human factors research. A large body of research focusses on situations in which the human driver needs to regain control. However, little research has so far been conducted on how SAE level 3+ automated driving should be designed with focus on occupant comfort.
openaire   +1 more source

Automated Decision-making in Automated Driving

, 2020
In an attempt to increase road safety, car manufacturers turn their attention to the interior of the vehicle. If the driver falls asleep, or is intoxicated, this will be picked up by sensors and cameras inside the vehicle. If it is deemed unsafe for the driver to continue the trip, the vehicle will pull over and bring itself to a stop so as to prevent ...
Vellinga, N. E., Mulder, Trix
openaire   +1 more source

Gradient-Based Time-Extended Potential Field Method for Real-Time Path Planning in Infrastructure-Based Cooperative Driving Systems

Sensors
This study proposes a real-time path generation method called the Gradient-based Time-extended Potential Field (GT-PF) for cooperative autonomous driving environments.
Jakyung Ko, Inchul Yang
doaj   +1 more source

Gradient-Based Time-Sequential Potential Field Method for Path Planning in Infrastructure-Based Cooperative Driving Automation

Sensors
This study proposes a GTS-PF (Gradient-based Time-Sequential Potential Field)-based path generation method for real-time reference-path planning at infrastructure-based RSUs in cooperative driving automation environments.
Jakyung Ko, Inchul Yang
doaj   +1 more source

Impact analysis of cooperative perception on the performance of automated driving in unsignalized roundabouts. [PDF]

Front Robot AI, 2023
Zainudin H, Koufos K, Lee G, Jiang L, Dianati M.   +4 more
europepmc   +1 more source

A dataset on the physiological state and behavior of drivers in conditionally automated driving. [PDF]

Data Brief, 2023
Meteier Q, Capallera M, de Salis E, Angelini L, Carrino S, Widmer M, Abou Khaled O, Mugellini E, Sonderegger A.   +8 more
europepmc   +1 more source

A review on AI Safety in highly automated driving. [PDF]

Front Artif Intell, 2022
Wäschle M, Thaler F, Berres A, Pölzlbauer F, Albers A.   +4 more
europepmc   +1 more source