Zobrazeno 1 - 10
of 120
pro vyhledávání: '"ZHENG, Sifa"'
Autor:
Tan, Ashton Yu Xuan, Yang, Yingkai, Zhang, Xiaofei, Li, Bowen, Gao, Xiaorong, Zheng, Sifa, Wang, Jianqiang, Gu, Xinyu, Li, Jun, Zhao, Yang, Zhang, Yuxin, Stathaki, Tania
Enhancing the safety of autonomous vehicles is crucial, especially given recent accidents involving automated systems. As passengers in these vehicles, humans' sensory perception and decision-making can be integrated with autonomous systems to improv
Externí odkaz:
http://arxiv.org/abs/2408.16315
Generating safety-critical scenarios, which are essential yet difficult to collect at scale, offers an effective method to evaluate the robustness of autonomous vehicles (AVs). Existing methods focus on optimizing adversariality while preserving the
Externí odkaz:
http://arxiv.org/abs/2406.02983
The well-established modular autonomous driving system is decoupled into different standalone tasks, e.g. perception, prediction and planning, suffering from information loss and error accumulation across modules. In contrast, end-to-end paradigms un
Externí odkaz:
http://arxiv.org/abs/2405.19620
Autor:
He, Lei, Li, Leheng, Sun, Wenchao, Han, Zeyu, Liu, Yichen, Zheng, Sifa, Wang, Jianqiang, Li, Keqiang
Neural Radiance Field (NeRF) has garnered significant attention from both academia and industry due to its intrinsic advantages, particularly its implicit representation and novel view synthesis capabilities. With the rapid advancements in deep learn
Externí odkaz:
http://arxiv.org/abs/2404.13816
Trajectory prediction plays a vital role in the performance of autonomous driving systems, and prediction accuracy, such as average displacement error (ADE) or final displacement error (FDE), is widely used as a performance metric. However, a signifi
Externí odkaz:
http://arxiv.org/abs/2306.15136
Energy-function-based safety certificates can provide provable safety guarantees for the safe control tasks of complex robotic systems. However, all recent studies about learning-based energy function synthesis only consider the feasibility, which mi
Externí odkaz:
http://arxiv.org/abs/2209.11787
Choosing an appropriate parameter set for the designed controller is critical for the final performance but usually requires a tedious and careful tuning process, which implies a strong need for automatic tuning methods. However, among existing metho
Externí odkaz:
http://arxiv.org/abs/2209.04854
Autor:
WANG, Yao a, b, c, DING, Feng a, LI, Yong d, ZHANG, Yunhua e, ZHENG, Sifa b, ZHAO, Libin a, f, g, ⁎, HU, Ning a, c, f, ⁎
Publikováno v:
In Chinese Journal of Aeronautics January 2025 38(1)
Autor:
Cheng, Hao a, Jiang, Yanbo a, Zhang, Hailun a, Chen, Keyu a, Huang, Heye b, Xu, Shaobing a, Wang, Jianqiang a, Zheng, Sifa a, ⁎
Publikováno v:
In Transportation Research Part C February 2025 171
The recent advanced evolution-based zeroth-order optimization methods and the policy gradient-based first-order methods are two promising alternatives to solve reinforcement learning (RL) problems with complementary advantages. The former methods wor
Externí odkaz:
http://arxiv.org/abs/2201.12518