Research on obstacle avoidance optimization and path planning of autonomous vehicles based on attention mechanism combined with multimodal information decision-making thoughts of robots

Frontiers in Neurorobotics

Neuroscience

Nachuan Shen¹  Xuejin Wu²  Guangming Wang³ 

[1] Chinese Academy of Fiscal Science, Beijing, China;College of Transport and Communications, Shanghai Maritime University, Shanghai, China;School of Management, Wuhan University of Technology, Wuhan, China;School of Politics and Public Administration, Zhengzhou University, Zhengzhou, China;

关键词: attention mechanism;    end-to-end architecture;    autonomous driving;    path planning;    multimodal information decision-making for robots;    LSTM frontiers;   

DOI  :  10.3389/fnbot.2023.1269447

 received in 2023-07-30, accepted in 2023-08-28,  发布年份 2023

来源: Frontiers

【 摘 要 】

With the development of machine perception and multimodal information decision-making techniques, autonomous driving technology has become a crucial area of advancement in the transportation industry. The optimization of vehicle navigation, path planning, and obstacle avoidance tasks is of paramount importance. In this study, we explore the use of attention mechanisms in a end-to-end architecture for optimizing obstacle avoidance and path planning in autonomous driving vehicles. We position our research within the broader context of robotics, emphasizing the fusion of information and decision-making capabilities. The introduction of attention mechanisms enables vehicles to perceive the environment more accurately by focusing on important information and making informed decisions in complex scenarios. By inputting multimodal information, such as images and LiDAR data, into the attention mechanism module, the system can automatically learn and weigh crucial environmental features, thereby placing greater emphasis on key information during obstacle avoidance decisions. Additionally, we leverage the end-to-end architecture and draw from classical theories and algorithms in the field of robotics to enhance the perception and decision-making abilities of autonomous driving vehicles. Furthermore, we address the optimization of path planning using attention mechanisms. We transform the vehicle's navigation task into a sequential decision-making problem and employ LSTM (Long Short-Term Memory) models to handle dynamic navigation in varying environments. By applying attention mechanisms to weigh key points along the navigation path, the vehicle can flexibly select the optimal route and dynamically adjust it based on real-time conditions. Finally, we conducted extensive experimental evaluations and software experiments on the proposed end-to-end architecture on real road datasets. The method effectively avoids obstacles, adheres to traffic rules, and achieves stable, safe, and efficient autonomous driving in diverse road scenarios. This research provides an effective solution for optimizing obstacle avoidance and path planning in the field of autonomous driving. Moreover, it contributes to the advancement and practical applications of multimodal information fusion in navigation, localization, and human-robot interaction.

【 授权许可】

Unknown   
Copyright © 2023 Wu, Wang and Shen.