Zobrazeno 1 - 10
of 30
pro vyhledávání: '"Halilaj, Lavdim"'
Autor:
Zhou, Hongkuan, Halilaj, Lavdim, Monka, Sebastian, Schmid, Stefan, Zhu, Yuqicheng, Xiong, Bo, Staab, Steffen
Despite the remarkable success of deep neural networks (DNNs) in computer vision, they fail to remain high-performing when facing distribution shifts between training and testing data. In this paper, we propose Knowledge-Guided Visual representation
Externí odkaz:
http://arxiv.org/abs/2410.15981
Accurate trajectory prediction is crucial for ensuring safe and efficient autonomous driving. However, most existing methods overlook complex interactions between traffic participants that often govern their future trajectories. In this paper, we pro
Externí odkaz:
http://arxiv.org/abs/2405.03809
Trajectory prediction in autonomous driving relies on accurate representation of all relevant contexts of the driving scene, including traffic participants, road topology, traffic signs, as well as their semantic relations to each other. Despite incr
Externí odkaz:
http://arxiv.org/abs/2404.19379
Autor:
Mlodzian, Leon, Sun, Zhigang, Berkemeyer, Hendrik, Monka, Sebastian, Wang, Zixu, Dietze, Stefan, Halilaj, Lavdim, Luettin, Juergen
Trajectory prediction in traffic scenes involves accurately forecasting the behaviour of surrounding vehicles. To achieve this objective it is crucial to consider contextual information, including the driving path of vehicles, road topology, lane div
Externí odkaz:
http://arxiv.org/abs/2312.09676
Autor:
Zipfl, Maximilian, Hertlein, Felix, Rettinger, Achim, Thoma, Steffen, Halilaj, Lavdim, Luettin, Juergen, Schmid, Stefan, Henson, Cory
Representing relevant information of a traffic scene and understanding its environment is crucial for the success of autonomous driving. Modeling the surrounding of an autonomous car using semantic relations, i.e., how different traffic participants
Externí odkaz:
http://arxiv.org/abs/2212.02503
Current deep learning methods for object recognition are purely data-driven and require a large number of training samples to achieve good results. Due to their sole dependence on image data, these methods tend to fail when confronted with new enviro
Externí odkaz:
http://arxiv.org/abs/2210.11233
Automated driving is one of the most active research areas in computer science. Deep learning methods have made remarkable breakthroughs in machine learning in general and in automated driving (AD)in particular. However, there are still unsolved prob
Externí odkaz:
http://arxiv.org/abs/2210.08119
Recent approaches of computer vision utilize deep learning methods as they perform quite well if training and testing domains follow the same underlying data distribution. However, it has been shown that minor variations in the images that occur when
Externí odkaz:
http://arxiv.org/abs/2201.11794
Traditional computer vision approaches, based on neural networks (NN), are typically trained on a large amount of image data. By minimizing the cross-entropy loss between a prediction and a given class label, the NN and its visual embedding space are
Externí odkaz:
http://arxiv.org/abs/2102.08747
Collaborative vocabulary development in the context of data integration is the process of finding consensus between the experts of the different systems and domains. The complexity of this process is increased with the number of involved people, the
Externí odkaz:
http://arxiv.org/abs/1601.02433