A deep learning-based semantic segmentation architecture for autonomous driving applications by Masood, Sharjeel, Ahmed, Fawad, Alsuhibany, Suliman A., Ghadi, Yazeed Yasin, Siyal, M. Y., Kumar, Harish, Khan, Khyber, Ahmad, Jawad

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A personalized behavior learning system for human-like longitudinal speed control of autonomous vehicles by Lu, Chao, Gong, Jianwei, Lv, Chen, Chen, Xin, Cao, Dongpu, Chen, Yimin

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RobustLoc: robust camera pose regression in challenging driving environments by Wang, Sijie, Kang, Qiyu, She, Rui, Tay, Wee Peng, Hartmannsgruber, Andreas, Navarro, Diego Navarro

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Spatio-temporal interaction aware and trajectory distribution aware graph convolution network for pedestrian multimodal trajectory prediction by Wang, Ruiping, Song, Xiao, Hu, Zhijian, Cui, Yong

“…Pedestrian trajectory prediction is a critical research area with numerous domains, e.g., blind navigation, autonomous driving systems, and service robots. There exist two challenges in this research field: spatio-temporal interaction modeling among pedestrians and the uncertainty of pedestrian trajectories. …”
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Real-time LiDAR point cloud compression using bi-directional prediction and range-adaptive floating-point coding by Zhao, Lili, Ma, Kai-Kuang, Lin, Xuhu, Wang, Wenyi, Chen, Jianwen

“…Due to the large amount of data involved in the three-dimensional (3D) LiDAR point clouds, point cloud compression (PCC) becomes indispensable to many real-time applications. In autonomous driving of connected vehicles for example, point clouds are constantly acquired along the time and subjected to be compressed. …”
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Adaptive resilient event-triggered control design of autonomous vehicles with an iterative single critic learning framework by Zhang, Kun, Su, Rong, Zhang, Huaguang, Tian, Yunlin

“…According to the kinematic equation of RWDA vehicles and desired trajectory, the tracking error system during autonomous driving process is firstly built, where the denial-of-service (DoS) attacking signals are injected in the networked communication and transmission. …”
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HypLiLoc: towards effective LiDAR pose regression with hyperbolic fusion by Wang, Sijie, Kang, Qiyu, She, Rui, Wang, Wei, Zhao, Kai, Song, Yang, Tay, Wee Peng

“…LiDAR relocalization plays a crucial role in many fields, including robotics, autonomous driving, and computer vision. LiDAR-based retrieval from a database typically incurs high computation storage costs and can lead to globally inaccurate pose estimations if the database is too sparse. …”
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PointDifformer: robust point cloud registration with neural diffusion and transformer by She, Rui, Kang, Qiyu, Wang, Sijie, Tay, Wee Peng, Zhao, Kai, Song, Yang, Geng, Tianyu, Xu, Yi, Navarro, Diego Navarro, Hartmannsgruber, Andreas

“…Point cloud registration is a fundamental technique in 3-D computer vision with applications in graphics, autonomous driving, and robotics. However, registration tasks under challenging conditions, under which noise or perturbations are prevalent, can be difficult. …”
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PatchAugNet: patch feature augmentation-based heterogeneous point cloud place recognition in large-scale street scenes by Zou, Xianghong, Li, Jianping, Wang, Yuan, Liang, Fuxun, Wu, Weitong, Wang, Haiping, Yang, Bisheng, Dong, Zhen

“…Point Cloud Place Recognition (PCPR) in street scenes is an essential task in the fields of autonomous driving, robot navigation, and urban map updating. …”
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