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<title>2.05. 2024 Volume 21, Issue No. 7.</title>
<link href="http://hdl.handle.net/20.500.14044/33865" rel="alternate"/>
<subtitle/>
<id>http://hdl.handle.net/20.500.14044/33865</id>
<updated>2026-07-21T07:26:21Z</updated>
<dc:date>2026-07-21T07:26:21Z</dc:date>
<entry>
<title>Investigating the Energetics of Electric Vehicles, based on Real Measurements</title>
<link href="http://hdl.handle.net/20.500.14044/33120" rel="alternate"/>
<author>
<name>Tollner, Dávid</name>
</author>
<author>
<name>Zöldy, Máté</name>
</author>
<id>http://hdl.handle.net/20.500.14044/33120</id>
<updated>2025-09-18T08:35:11Z</updated>
<published>2024-01-01T00:00:00Z</published>
<summary type="text">Investigating the Energetics of Electric Vehicles, based on Real Measurements
Tollner, Dávid; Zöldy, Máté
Electric vehicles can offer a real alternative for mobility in the 21 st Century, but&#13;
the extent of their long-term wear and degradation is unknown. Obviously, both traffic and&#13;
users have to adapt to the new types of drivetrains, as these cars can be driven optimally in&#13;
a completely different style. A new measurement and evaluation methodology has been&#13;
developed, and various tests have been defined to investigate the key questions of electric&#13;
cars. We showed that energy consumption can be reduced by more than 1/3 at optimum&#13;
ambient temperatures, compared to 0 °C. We examined the correlation between speed and&#13;
energy consumption on highway, and found that reducing the average speed from 105 km/h&#13;
to 85 km/h can increase the range by up to 40%. Finally, we calculated that at 50,000 km of&#13;
mileage, the battery only degrades by 11%.
</summary>
<dc:date>2024-01-01T00:00:00Z</dc:date>
</entry>
<entry>
<title>Investigating Safety Effects of PKI Authentication, in Automotive Systems</title>
<link href="http://hdl.handle.net/20.500.14044/33119" rel="alternate"/>
<author>
<name>Pethő, Zsombor</name>
</author>
<author>
<name>Kazár, Tamás Márton</name>
</author>
<author>
<name>Kraudy, Roland</name>
</author>
<author>
<name>Szalay, Zsolt</name>
</author>
<author>
<name>Török, Árpád</name>
</author>
<id>http://hdl.handle.net/20.500.14044/33119</id>
<updated>2025-09-18T08:34:50Z</updated>
<published>2024-01-01T00:00:00Z</published>
<summary type="text">Investigating Safety Effects of PKI Authentication, in Automotive Systems
Pethő, Zsombor; Kazár, Tamás Márton; Kraudy, Roland; Szalay, Zsolt; Török, Árpád
Our study analyses the safety effects of Public Key Infrastructure (PKI), based&#13;
authentication mated, related to certain wireless communication based automotive functions.&#13;
The first part of the article focuses on quantifying the safety effect of quality of service (QoS)&#13;
parameters in the case of wireless communication based automotive functions. Based on this&#13;
concept, the paper discusses two scenarios: in the first case, there is no authentication&#13;
process applied during the communication, and in the second case, the communication is&#13;
secured by PKI authentication. This concept allows us to evaluate the safety effect of the&#13;
security overhead caused by the additional computation demand related to the authentication&#13;
process. Considering the results of our research, it becomes possible to define the&#13;
requirements and expected conditions, regarding the operational circumstances
</summary>
<dc:date>2024-01-01T00:00:00Z</dc:date>
</entry>
<entry>
<title>Neural Network-based Multi-Class Traffic-Sign Classification with the German Traffic Sign Recognition Benchmark</title>
<link href="http://hdl.handle.net/20.500.14044/33116" rel="alternate"/>
<author>
<name>Ferencz, Csanád</name>
</author>
<author>
<name>Zöldy, Máté</name>
</author>
<id>http://hdl.handle.net/20.500.14044/33116</id>
<updated>2025-09-18T08:34:33Z</updated>
<published>2024-01-01T00:00:00Z</published>
<summary type="text">Neural Network-based Multi-Class Traffic-Sign Classification with the German Traffic Sign Recognition Benchmark
Ferencz, Csanád; Zöldy, Máté
Traffic-sign detection has an essential role in the field of computer vision, having&#13;
many real-world applications more and more object recognition and classification task is&#13;
being solved by using Convolutional Neural Networks (CNNs or ConvNets), especially in the&#13;
field of intelligent transportation. In the present article, we offer an implementation chosen&#13;
from several CNN-based traffic-sign recognition and classification algorithm architectures,&#13;
using a ConvNet classifying 43 different types of road traffic signs in the TensorFlow&#13;
framework, as part of the German Traffic Sign Recognition Benchmark (GTSRB)&#13;
competition. A Deep ConvNet was trained end-to-end, aiming to improve the prediction&#13;
performance of a DCNN-based autonomous driving system equipped with a front-facing&#13;
digital camera, with as input a sequence of images, as output directly the prediction results.&#13;
The results obtained on held-out data demonstrated the high accuracy of the model, matching&#13;
the state-of-the-art multi-class recognition and classification accuracies, as well as related&#13;
human-level recognition performances.
</summary>
<dc:date>2024-01-01T00:00:00Z</dc:date>
</entry>
<entry>
<title>Trends in Cognitive Mobility in 2022</title>
<link href="http://hdl.handle.net/20.500.14044/33114" rel="alternate"/>
<author>
<name>Zöldy, Máté</name>
</author>
<author>
<name>Baranyi, Péter</name>
</author>
<author>
<name>Török, Ádám</name>
</author>
<id>http://hdl.handle.net/20.500.14044/33114</id>
<updated>2025-09-18T08:33:25Z</updated>
<published>2024-01-01T00:00:00Z</published>
<summary type="text">Trends in Cognitive Mobility in 2022
Zöldy, Máté; Baranyi, Péter; Török, Ádám
Cognitive mobility framework has developed in the year 2022. The first&#13;
international scientific congress (1st IEEE Cognitive Mobility at Bosch Budapest&#13;
Innovation Center – CogMob Conference) was organised to create a space for sharing&#13;
thoughts and starting conversations about the topic. This paper aims to summarise and&#13;
evaluate the main tendencies of cognitive mobility. After reviewing the presented papers,&#13;
there were evaluated according to essential elements of cognitive mobility, and statistical&#13;
analysis was carried out to evaluate the papers. In our work, the five core topics of&#13;
cognitive mobility in 2022 were defined and evaluated according to the essential elements.&#13;
The similarity measure and proximity index-based evaluation of the abstracts and the&#13;
keywords show that sustainability-related topics, energy sources, and their utilisation area&#13;
are the most frequented. The tendency is expected to continue, but the international military&#13;
situation could influence the weight of the topics.
</summary>
<dc:date>2024-01-01T00:00:00Z</dc:date>
</entry>
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