文献类型：期刊文献

英文题名：End-To-End Autonomous Driving Decision Method Based on Memory Attention Convolutional Neural Networks

作者：Yu, Kanghong[1,2]; Zhang, Jun[2]; Liu, Yuansheng[2]

第一作者：Yu, Kanghong

机构：[1] Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing, 100101, China; [2] College of Robotics, Beijing Union University, Beijing, 100101, China

第一机构：北京联合大学北京市信息服务工程重点实验室

年份：2025

卷号：2449 CCIS

起止页码：192-205

外文期刊名：Communications in Computer and Information Science

收录：EI(收录号：20253719146888)

语种：英文

外文关键词：Autonomous vehicles - Complex networks - Convolution - Convolutional neural networks - Decision making - Deep neural networks - Deep reinforcement learning - Learning algorithms - Optimization

摘要：In complex scenarios, rapid and accurate decision-making is crucial for autonomous vehicles. Deep reinforcement learning has attracted extensive attention due to its exceptional decision-making capabilities. However, existing deep reinforcement learning algorithms suffer from slow network convergence and suboptimal decision outcomes due to the vast state space and inadequate feature extraction capabilities of the representation models. To address this issue, we have integrated the Convolutional Block Attention Module (CBAM) with Long Short-Term Memory (LSTM) networks to develop the Memory Attention Convolutional Neural Networks (MACNN). This network serves as a feature representation model, capable of extracting environmental information that includes historical features from sequential bird's-eye view inputs. We have integrated the MACNN with the Proximal Policy Optimization (PPO) network to form the MACNN-PPO algorithm. Simulation experiments conducted in the Carla simulator demonstrate that the MACNN-PPO algorithm achieves faster convergence speeds and higher convergence rewards during training. With the same number of training iterations, the MACNN-PPO maintains a higher driving speed and improves route completion rates in complex urban environments. ? The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.