Zobrazeno 1 - 10
of 114
pro vyhledávání: '"Xu, Shaobing"'
Precise localization and mapping are critical for achieving autonomous navigation in self-driving vehicles. However, ego-motion estimation still faces significant challenges, particularly when GNSS failures occur or under extreme weather conditions (
Externí odkaz:
http://arxiv.org/abs/2411.07699
Autor:
Zhu, Zehang, Wang, Yuning, Ke, Tianqi, Han, Zeyu, Xu, Shaobing, Xu, Qing, Dolan, John M., Wang, Jianqiang
Safety is one of the most crucial challenges of autonomous driving vehicles, and one solution to guarantee safety is to employ an additional control revision module after the planning backbone. Control Barrier Function (CBF) has been widely used beca
Externí odkaz:
http://arxiv.org/abs/2409.14688
Autor:
Wang, Yuning, Ke, Zehong, Jiang, Yanbo, Li, Jinhao, Xu, Shaobing, Dolan, John M., Wang, Jianqiang
Autonomous driving decision-making is one of the critical modules towards intelligent transportation systems, and how to evaluate the driving performance comprehensively and precisely is a crucial challenge. A biased evaluation misleads and hinders d
Externí odkaz:
http://arxiv.org/abs/2409.14680
This paper presents an efficient algorithm, naming Centralized Searching and Decentralized Optimization (CSDO), to find feasible solution for large-scale Multi-Vehicle Trajectory Planning (MVTP) problem. Due to the intractable growth of non-convex co
Externí odkaz:
http://arxiv.org/abs/2405.20858
Autor:
Han, Zeyu, Jiang, Junkai, Ding, Xiaokang, Meng, Qingwen, Xu, Shaobing, He, Lei, Wang, Jianqiang
The 4D millimeter-wave (mmWave) radar, with its robustness in extreme environments, extensive detection range, and capabilities for measuring velocity and elevation, has demonstrated significant potential for enhancing the perception abilities of aut
Externí odkaz:
http://arxiv.org/abs/2405.05131
Scene understanding, defined as learning, extraction, and representation of interactions among traffic elements, is one of the critical challenges toward high-level autonomous driving (AD). Current scene understanding methods mainly focus on one conc
Externí odkaz:
http://arxiv.org/abs/2404.10263
LiDAR-based 3D occupancy prediction evolved rapidly alongside the emergence of large datasets. Nevertheless, the potential of existing diverse datasets remains underutilized as they kick in individually. Models trained on a specific dataset often suf
Externí odkaz:
http://arxiv.org/abs/2403.08512
Autor:
Jiang, Junkai, Hu, Zhenhua, Xie, Zihan, Hao, Changlong, Liu, Hongyu, Xu, Wenliang, Wang, Yuning, He, Lei, Xu, Shaobing, Wang, Jianqiang
Planning module is an essential component of intelligent vehicle study. In this paper, we address the risk-aware planning problem of UGVs through a global-local planning framework which seamlessly integrates risk assessment methods. In particular, a
Externí odkaz:
http://arxiv.org/abs/2402.02457
Autonomous vehicles (AV) are expected to reshape future transportation systems, and decision-making is one of the critical modules toward high-level automated driving. To overcome those complicated scenarios that rule-based methods could not cope wit
Externí odkaz:
http://arxiv.org/abs/2306.16784
Autor:
Han, Zeyu, Wang, Jiahao, Xu, Zikun, Yang, Shuocheng, He, Lei, Xu, Shaobing, Wang, Jianqiang, Li, Keqiang
The 4D millimeter-wave (mmWave) radar, proficient in measuring the range, azimuth, elevation, and velocity of targets, has attracted considerable interest within the autonomous driving community. This is attributed to its robustness in extreme enviro
Externí odkaz:
http://arxiv.org/abs/2306.04242