Effects of Visual Latency on Vehicle Driving Behavior

2016 ◽  
Vol 14 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Björn Blissing ◽  
Fredrik Bruzelius ◽  
Olle Eriksson
Keyword(s):  
Driving Behavior ◽  
Visual Latency

Driving Behavior Survey--Polish Version

2013 ◽  
Author(s):  
A. Blachnio ◽  
A. Przepiorka ◽  
M. Sullman ◽  
J. Taylor
Keyword(s):  
Driving Behavior ◽  
Polish Version

A Graph Based Security Description Method of Driving Behavior Characteristics

CICTP 2020 ◽  
2020 ◽  
Author(s):  
Hang Qi ◽  
Xiao-Hua Zhao ◽  
Yi-Ping Wu ◽  
Chang Liu
Keyword(s):  
Driving Behavior ◽  
Behavior Characteristics

STUDI KETRAMPILAN BERKENDARA TERHADAP PERILAKU AMAN BERKENDARA PADA TARUNA STIMART “AMNI” SEMARANG

2018 ◽  
Author(s):  
Andar Sri Sumantri
Keyword(s):  
Driving Behavior ◽  
Driving Competency

<p><em>Safety driving is one of the issues that always get serious attention is every country. Based on a survey done by ADB-ASEAN on Andrew Ruspali 2014, Indonesia is number 9 out of to 10 in handling the victim’s survival which considered to be very little. According to a research by Agus Aji Samekto (2009) stated that most accident victims in Semarang is 15-21 years old whom generally students and the vehicle involved were motorcycles. The purpose of this research was to know the influence of driving attitude to safety driving behavior. The object of this research was the student of STIMART “AMNI” Semarang.</em></p><p><em></em><strong><em>Keywords :</em><em> Safety Driving Competency, Safety Driving Behavior, Students of STIMART “AMNI” Semarang</em></strong></p><p><em><br /></em></p>Keselamatan berkendara merupakan salah satu masalah yang selalu mendapatkan perhatian serius di setiap negara. Kinerja keselamatan lalu lintas jalan di Indonesia dari survei yang dilakukan ADB-ASEAN dalam Andrew Ruspanah tahun 2014, berada pada peringkat ke-9 dari 10 negara. Ini menunjukkan bahwa penanganan masalah keselamatan akibat kecelakaan lalu lintas jalan di Indonesia belum banyak dilakukan. Penelitian Agus Aji Samekto (2009) menyebutkan bahwa jumlah terbesar korban kecelakaan lalu lintas di Kota Semarang dan didominasi oleh kelompok usia 15-21 tahun, pada umumnya adalah pelajar atau mahasiswa. Dimana jumlah kendaraan terbesar yang terlibat dalam kecelakaan lalu lintas adalah sepeda motor.  Keselamatan berkendara merupakan salah satu masalah yang selalu mendapatkan perhatian serius di setiap negara. Tujuan penelitian ini adalah untuk mengetahui pengaruh sikap berkendara terhadap perilaku aman berkendara. Pada penelitian ini objek yang diambil adalah semua taruna-taruni STIMART “AMNI” Semarang yang aktif terdaftar.<p><strong>Kata kunci :   <em>Perilaku Aman Berkendara, Keterampilan Berkendara</em></strong></p>

Cloning Safe Driving Behavior for Self-Driving Cars using Convolutional Neural Networks

2019 ◽  
Vol 12 (2) ◽  
pp. 120-127 ◽  
Author(s):  
Wael Farag
Keyword(s):  
Gradient Descent ◽  
Autonomous Driving ◽  
Driving Behavior ◽  
Training Data ◽  
Stochastic Gradient Descent ◽  
Data Set ◽  
Safe Driving ◽  
Processing Pipeline ◽  
Self Driving Cars ◽  
And Training

Background: In this paper, a Convolutional Neural Network (CNN) to learn safe driving behavior and smooth steering manoeuvring, is proposed as an empowerment of autonomous driving technologies. The training data is collected from a front-facing camera and the steering commands issued by an experienced driver driving in traffic as well as urban roads. Methods: This data is then used to train the proposed CNN to facilitate what it is called “Behavioral Cloning”. The proposed Behavior Cloning CNN is named as “BCNet”, and its deep seventeen-layer architecture has been selected after extensive trials. The BCNet got trained using Adam’s optimization algorithm as a variant of the Stochastic Gradient Descent (SGD) technique. Results: The paper goes through the development and training process in details and shows the image processing pipeline harnessed in the development. Conclusion: The proposed approach proved successful in cloning the driving behavior embedded in the training data set after extensive simulations.

scholarly journals Concept of an Ontology for Automated Vehicle Behavior in the Context of Human-Centered Research on Automated Driving Styles

Information ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 21
Author(s):  
Johannes Ossig ◽  
Stephanie Cramer ◽  
Klaus Bengler
Keyword(s):  
Driving Behavior ◽  
Automation System ◽  
Automated Driving ◽  
Driving Style ◽  
Driving Experience ◽  
Automated Vehicle ◽  
Future Design ◽  
Vehicle System ◽  
The Future ◽  
Conceptual Distinction

In the human-centered research on automated driving, it is common practice to describe the vehicle behavior by means of terms and definitions related to non-automated driving. However, some of these definitions are not suitable for this purpose. This paper presents an ontology for automated vehicle behavior which takes into account a large number of existing definitions and previous studies. This ontology is characterized by an applicability for various levels of automated driving and a clear conceptual distinction between characteristics of vehicle occupants, the automation system, and the conventional characteristics of a vehicle. In this context, the terms ‘driveability’, ‘driving behavior’, ‘driving experience’, and especially ‘driving style’, which are commonly associated with non-automated driving, play an important role. In order to clarify the relationships between these terms, the ontology is integrated into a driver-vehicle system. Finally, the ontology developed here is used to derive recommendations for the future design of automated driving styles and in general for further human-centered research on automated driving.

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