Inicio  /  Applied Sciences  /  Vol: 14 Par: 4 (2024)  /  Artículo
ARTÍCULO
TITULO

Motion Planning for Autonomous Vehicles in Unanticipated Obstacle Scenarios at Intersections Based on Artificial Potential Field

Rui Mu    
Wenhao Yu    
Zhongxing Li    
Changjun Wang    
Guangming Zhao    
Wenhui Zhou and Mingyue Ma    

Resumen

This work designed a motion planning algorithm for autonomous vehicles in unanticipated obstacle scenarios. In standard driving scenarios, the proposed motion planning algorithm plans a trajectory that complies with intersection regulations, including lane-marking, recommended turning lane, traffic light, right-of-way, and no-parking rules. In unanticipated obstacle scenarios, after the necessity of obstacle avoidance is identified, the ego vehicle would break the rules temporarily to ensure the safety and mobility of autonomous vehicles.

 Artículos similares

       
 
Rongke Wei, Haodong Pei, Dongjie Wu, Changwen Zeng, Xin Ai and Huixian Duan    
The task of 3D reconstruction of urban targets holds pivotal importance for various applications, including autonomous driving, digital twin technology, and urban planning and development. The intricate nature of urban landscapes presents substantial cha... ver más
Revista: Applied Sciences

 
Beom-Joon Park and Hyun-Joon Chung    
The growing trend of onboard computational autonomy has increased the need for self-reliant rovers (SRRs) with high efficiency for unmanned rover activities. Mobility is directly associated with a successful execution mission, thus fault response for act... ver más
Revista: Aerospace

 
Yu Han, Xiaolei Ma, Bo Wang, Hongwang Zhang, Qiuxia Zhang and Gang Chen    
Nonlinear Model Predictive Control (NMPC) is an effective approach for motion planning in autonomous vehicles that need to satisfy multiple driving demands. Within the realm of planner design, current strategies inadequately address the issues related to... ver más
Revista: Applied Sciences

 
Xi Lyu, Yushan Sun, Lifeng Wang, Jiehui Tan and Liwen Zhang    
This study aims to solve the problems of sparse reward, single policy, and poor environmental adaptability in the local motion planning task of autonomous underwater vehicles (AUVs). We propose a two-layer deep deterministic policy gradient algorithm-bas... ver más

 
Shunan Hu, Shenpeng Tian, Jiansen Zhao and Ruiqi Shen    
In order to ensure the safe navigation of USVs (unmanned surface vessels) and real-time collision avoidance, this study conducts global and local path planning for USVs in a variable dynamic environment, while local path planning is proposed under the co... ver más