Zobrazeno 1 - 10
of 50
pro vyhledávání: '"Yurtsever, Ekim"'
Autor:
Liu, Mingyu, Yurtsever, Ekim, Brede, Marc, Meng, Jun, Zimmer, Walter, Zhou, Xingcheng, Zagar, Bare Luka, Cui, Yuning, Knoll, Alois
Accurate and effective 3D object detection is critical for ensuring the driving safety of autonomous vehicles. Recently, state-of-the-art two-stage 3D object detectors have exhibited promising performance. However, these methods refine proposals indi
Externí odkaz:
http://arxiv.org/abs/2405.06782
Autor:
Liu, Mingyu, Yurtsever, Ekim, Fossaert, Jonathan, Zhou, Xingcheng, Zimmer, Walter, Cui, Yuning, Zagar, Bare Luka, Knoll, Alois C.
Autonomous driving has rapidly developed and shown promising performance due to recent advances in hardware and deep learning techniques. High-quality datasets are fundamental for developing reliable autonomous driving algorithms. Previous dataset su
Externí odkaz:
http://arxiv.org/abs/2401.01454
Autor:
Zhou, Xingcheng, Liu, Mingyu, Yurtsever, Ekim, Zagar, Bare Luka, Zimmer, Walter, Cao, Hu, Knoll, Alois C.
The applications of Vision-Language Models (VLMs) in the field of Autonomous Driving (AD) have attracted widespread attention due to their outstanding performance and the ability to leverage Large Language Models (LLMs). By incorporating language dat
Externí odkaz:
http://arxiv.org/abs/2310.14414
Deformable linear objects are vastly represented in our everyday lives. It is often challenging even for humans to visually understand them, as the same object can be entangled so that it appears completely different. Examples of deformable linear ob
Externí odkaz:
http://arxiv.org/abs/2310.08904
Recent research in pedestrian simulation often aims to develop realistic behaviors in various situations, but it is challenging for existing algorithms to generate behaviors that identify weaknesses in automated vehicles' performance in extreme and u
Externí odkaz:
http://arxiv.org/abs/2306.07525
Autor:
Yurtsever, Ekim, Erçelik, Emeç, Liu, Mingyu, Yang, Zhijie, Zhang, Hanzhen, Topçam, Pınar, Listl, Maximilian, Çaylı, Yılmaz Kaan, Knoll, Alois
State-of-the-art lidar-based 3D object detection methods rely on supervised learning and large labeled datasets. However, annotating lidar data is resource-consuming, and depending only on supervised learning limits the applicability of trained model
Externí odkaz:
http://arxiv.org/abs/2205.00705
Challenges related to automated driving are no longer focused on just the construction of such automated vehicles (AVs), but in assuring the safety of their operation. Recent advances in Level 3 and Level 4 autonomous driving have motivated more exte
Externí odkaz:
http://arxiv.org/abs/2202.02818
Avoiding unseen or partially occluded vulnerable road users (VRUs) is a major challenge for fully autonomous driving in urban scenes. However, occlusion-aware risk assessment systems have not been widely studied. Here, we propose a pedestrian emergen
Externí odkaz:
http://arxiv.org/abs/2107.02326
3D object detection is a core component of automated driving systems. State-of-the-art methods fuse RGB imagery and LiDAR point cloud data frame-by-frame for 3D bounding box regression. However, frame-by-frame 3D object detection suffers from noise,
Externí odkaz:
http://arxiv.org/abs/2104.12106
Predicting vulnerable road user behavior is an essential prerequisite for deploying Automated Driving Systems (ADS) in the real-world. Pedestrian crossing intention should be recognized in real-time, especially for urban driving. Recent works have sh
Externí odkaz:
http://arxiv.org/abs/2104.05485