Zobrazeno 1 - 10
of 12 967
pro vyhledávání: '"AUTONOMOUS DRIVING"'
Autor:
Li, Youwei, Qu, Jian
Publikováno v:
Data Technologies and Applications, 2024, Vol. 58, Issue 5, pp. 693-717.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/DTA-08-2022-0307
Publikováno v:
Alexandria Engineering Journal, Vol 111, Iss , Pp 530-539 (2025)
Recent autonomous driving systems heavily rely on 3D point cloud data collected from multiple sensors for environmental awareness and decision-making. However, it is unrealistic to expect the autonomous driving system to recognize all road environmen
Externí odkaz:
https://doaj.org/article/8998ecbef982478ba162bf5a4ff440d7
Publikováno v:
IET Intelligent Transport Systems, Vol 18, Iss S1, Pp 2793-2813 (2024)
Abstract Autonomous driving is an exciting research field that has received growing attention in recent years. One of the most challenging and safety‐critical driving situations is highway on‐ramp merging. Most decision‐making strategies that p
Externí odkaz:
https://doaj.org/article/3a72015c557643eaa93b155f8135256a
Publikováno v:
IET Intelligent Transport Systems, Vol 18, Iss S1, Pp 2921-2938 (2024)
Abstract The deployment of autonomous vehicles (AVs) in complex urban environments faces numerous challenges, especially at intersections where they coexist with human‐driven vehicles (HVs), resulting in increased safety risks. In response, this st
Externí odkaz:
https://doaj.org/article/b939404ecd9f4ce49c8895e74b86394d
Publikováno v:
IET Intelligent Transport Systems, Vol 18, Iss 12, Pp 2552-2564 (2024)
Abstract Bird's‐Eye‐View (BEV) map is a powerful and detailed scene representation for intelligent vehicles that provides both the location and semantic information about nearby objects from a top‐down perspective. BEV map generation is a compl
Externí odkaz:
https://doaj.org/article/31a2922ea7f1497c8ecdf97478aa953d
Publikováno v:
Acta Electrotechnica et Informatica, Vol 24, Iss 4, Pp 27-34 (2024)
We explore the use of radiance fields (RFs) to reconstruct photorealistic 3D urban scenes, creating digital twins (DTs) for autonomous driving (AD) by leveraging Nerfacto and Splatfacto models integrated with the CARLA simulator. Our research demonst
Externí odkaz:
https://doaj.org/article/3d2022a02b3f452cbbe3bfad13a12589
Publikováno v:
Alexandria Engineering Journal, Vol 109, Iss , Pp 497-507 (2024)
This study explores a more effective obstacle avoidance method for autonomous driving based on the monocular vision system of YOLOv5. The study utilizes the YOLOv5 model to detect obstacles and road signs in the environment in real-time, including ve
Externí odkaz:
https://doaj.org/article/596b663c8fa1450982d7f397298c8976
Publikováno v:
Shanghai Jiaotong Daxue xuebao, Vol 58, Iss 11, Pp 1826-1834 (2024)
To solve the problem of inadequate perception of autonomous driving in occlusion and over-the-horizon scenarios, a vehicle-road collaborative perception method based on a dual-stream feature extraction network is proposed to enhance the 3D object det
Externí odkaz:
https://doaj.org/article/ab21a9cc610441699fec16a08ddfb088
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-16 (2024)
Abstract The continuous advancement of autonomous driving technology imposes higher demands on the accuracy of target detection in complex environments, particularly when traffic targets are occluded. Existing algorithms still face significant challe
Externí odkaz:
https://doaj.org/article/5b88c25ebbeb4176bd2b9b965300e346
Publikováno v:
Jisuanji kexue yu tansuo, Vol 18, Iss 11, Pp 3051-3064 (2024)
Addressing the issue of inter-class similarity is a challenging task in the research of autonomous driving scene classification, which primarily focuses on learning the distinctive features of targets in real-world complex traffic scenarios with high
Externí odkaz:
https://doaj.org/article/d88c3d311e11452399e52d01fc2f78d5