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pro vyhledávání: '"Yu, Hongkai"'
Cooperative perception systems play a vital role in enhancing the safety and efficiency of vehicular autonomy. Although recent studies have highlighted the efficacy of vehicle-to-everything (V2X) communication techniques in autonomous driving, a sign
Externí odkaz:
http://arxiv.org/abs/2409.10699
Autor:
Li, Jinlong, Li, Baolu, Tu, Zhengzhong, Liu, Xinyu, Guo, Qing, Juefei-Xu, Felix, Xu, Runsheng, Yu, Hongkai
Vision-centric perception systems for autonomous driving have gained considerable attention recently due to their cost-effectiveness and scalability, especially compared to LiDAR-based systems. However, these systems often struggle in low-light condi
Externí odkaz:
http://arxiv.org/abs/2404.04804
Autor:
Li, Baolu, Li, Jinlong, Liu, Xinyu, Xu, Runsheng, Tu, Zhengzhong, Guo, Jiacheng, Li, Xiaopeng, Yu, Hongkai
Current LiDAR-based Vehicle-to-Everything (V2X) multi-agent perception systems have shown the significant success on 3D object detection. While these models perform well in the trained clean weather, they struggle in unseen adverse weather conditions
Externí odkaz:
http://arxiv.org/abs/2403.11371
Autor:
Sun, Huiming, Guo, Jiacheng, Meng, Zibo, Zhang, Tianyun, Fang, Jianwu, Lin, Yuewei, Yu, Hongkai
Vehicle detection in Unmanned Aerial Vehicle (UAV) captured images has wide applications in aerial photography and remote sensing. There are many public benchmark datasets proposed for the vehicle detection and tracking in UAV images. Recent studies
Externí odkaz:
http://arxiv.org/abs/2403.05422
Autor:
Fang, Jianwu, Li, Lei-lei, Zhou, Junfei, Xiao, Junbin, Yu, Hongkai, Lv, Chen, Xue, Jianru, Chua, Tat-Seng
We present MM-AU, a novel dataset for Multi-Modal Accident video Understanding. MM-AU contains 11,727 in-the-wild ego-view accident videos, each with temporally aligned text descriptions. We annotate over 2.23 million object boxes and 58,650 pairs of
Externí odkaz:
http://arxiv.org/abs/2403.00436
In autonomous driving, predicting the behavior (turning left, stopping, etc.) of target vehicles is crucial for the self-driving vehicle to make safe decisions and avoid accidents. Existing deep learning-based methods have shown excellent and accurat
Externí odkaz:
http://arxiv.org/abs/2402.08423
The diverse agents in multi-agent perception systems may be from different companies. Each company might use the identical classic neural network architecture based encoder for feature extraction. However, the data source to train the various agents
Externí odkaz:
http://arxiv.org/abs/2402.04273
The multi-agent perception system collects visual data from sensors located on various agents and leverages their relative poses determined by GPS signals to effectively fuse information, mitigating the limitations of single-agent sensing, such as oc
Externí odkaz:
http://arxiv.org/abs/2401.17499
Vehicle Re-identification (Re-ID) has been broadly studied in the last decade; however, the different camera view angle leading to confused discrimination in the feature subspace for the vehicles of various poses, is still challenging for the Vehicle
Externí odkaz:
http://arxiv.org/abs/2311.16278
Recently, self-supervised monocular depth estimation has gained popularity with numerous applications in autonomous driving and robotics. However, existing solutions primarily seek to estimate depth from immediate visual features, and struggle to rec
Externí odkaz:
http://arxiv.org/abs/2309.00526