CoPEM: cooperative perception error models for autonomous driving by Piazzoni, Andrea, Cherian, Jim, Vijay, Roshan, Chau, Lap-Pui, Dauwels, Justin

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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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Robust and low complexity obstacle detection and tracking by Wu, Meiqing, Zhou, Chengju, Srikanthan, Thambipillai

“…Obstacle detection and tracking is essential module for autonomous driving. Vision based obstacle detection and tracking faces huge challenges due to factors like cluttered background, partial occlusion, inconsistent illumination, etc. …”
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SpSequenceNet : semantic segmentation network on 4D point clouds by Shi, Hanyu, Lin, Guosheng, Wang, Hao, Hung, Tzu-Yi, Wang, Zhenhua

“…Point clouds are useful in many applications like autonomous driving and robotics as they provide natural 3D information of the surrounding environments. …”
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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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Safe decision-making for lane-change of autonomous vehicles via human demonstration-aided reinforcement learning by Wu, Jingda, Huang, Wenhui, de Boer, Niels, Mo, Yanghui, He, Xiangkun, Lv, Chen

“…Decision-making is critical for lane change in autonomous driving. Reinforcement learning (RL) algorithms aim to identify the values of behaviors in various situations and thus they become a promising pathway to address the decision- making problem. …”
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