School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China.
Department of Transport and Planning, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Stevinweg 1, 2628 CN Delft, The Netherlands.
Sensors (Basel). 2022 Jun 29;22(13):4935. doi: 10.3390/s22134935.
In the autonomous driving process, the decision-making system is mainly used to provide macro-control instructions based on the information captured by the sensing system. Learning-based algorithms have apparent advantages in information processing and understanding for an increasingly complex driving environment. To incorporate the interactive information between agents in the environment into the decision-making process, this paper proposes a generalized single-vehicle-based graph neural network reinforcement learning algorithm (SGRL algorithm). The SGRL algorithm introduces graph convolution into the traditional deep neural network (DQN) algorithm, adopts the training method for a single agent, designs a more explicit incentive reward function, and significantly improves the dimension of the action space. The SGRL algorithm is compared with the traditional DQN algorithm (NGRL) and the multi-agent training algorithm (MGRL) in the highway ramp scenario. Results show that the SGRL algorithm has outstanding advantages in network convergence, decision-making effect, and training efficiency.
在自动驾驶过程中，决策系统主要用于根据传感系统获取的信息提供宏观控制指令。基于学习的算法在处理和理解日益复杂的驾驶环境信息方面具有明显的优势。为了将环境中代理之间的交互信息纳入决策过程，本文提出了一种广义的基于单个车辆的图神经网络强化学习算法（SGRL 算法）。SGRL 算法将图卷积引入传统的深度神经网络（DQN）算法中，采用单代理的训练方法，设计了更明确的激励奖励函数，并显著提高了动作空间的维度。将 SGRL 算法与传统的 DQN 算法（NGRL）和多代理训练算法（MGRL）在高速公路匝道场景中进行了比较。结果表明，SGRL 算法在网络收敛性、决策效果和训练效率方面具有突出的优势。
