Autonomous driving has witnessed rapid advancement;however,ensuring safe and efficient driving in intricate scenarios remains a critical challenge.In particular,traffic roundabouts bring a set of challenges to autonom...Autonomous driving has witnessed rapid advancement;however,ensuring safe and efficient driving in intricate scenarios remains a critical challenge.In particular,traffic roundabouts bring a set of challenges to autonomous driving due to the unpredictable entry and exit of vehicles,susceptibility to traffic flow bottlenecks,and imperfect data in perceiving environmental information,rendering them a vital issue in the practical application of autonomous driving.To address the traffic challenges,this work focused on complex roundabouts with multi-lane and proposed a Perception EnhancedDeepDeterministic Policy Gradient(PE-DDPG)for AutonomousDriving in the Roundabouts.Specifically,themodel incorporates an enhanced variational autoencoder featuring an integrated spatial attention mechanism alongside the Deep Deterministic Policy Gradient framework,enhancing the vehicle’s capability to comprehend complex roundabout environments and make decisions.Furthermore,the PE-DDPG model combines a dynamic path optimization strategy for roundabout scenarios,effectively mitigating traffic bottlenecks and augmenting throughput efficiency.Extensive experiments were conducted with the collaborative simulation platform of CARLA and SUMO,and the experimental results show that the proposed PE-DDPG outperforms the baseline methods in terms of the convergence capacity of the training process,the smoothness of driving and the traffic efficiency with diverse traffic flow patterns and penetration rates of autonomous vehicles(AVs).Generally,the proposed PE-DDPGmodel could be employed for autonomous driving in complex scenarios with imperfect data.展开更多
Deep deterministic policy gradient(DDPG)has been proved to be effective in optimizing particle swarm optimization(PSO),but whether DDPG can optimize multi-objective discrete particle swarm optimization(MODPSO)remains ...Deep deterministic policy gradient(DDPG)has been proved to be effective in optimizing particle swarm optimization(PSO),but whether DDPG can optimize multi-objective discrete particle swarm optimization(MODPSO)remains to be determined.The present work aims to probe into this topic.Experiments showed that the DDPG can not only quickly improve the convergence speed of MODPSO,but also overcome the problem of local optimal solution that MODPSO may suffer.The research findings are of great significance for the theoretical research and application of MODPSO.展开更多
Device-to-device(D2D)communications underlying cellular networks enabled by unmanned aerial vehicles(UAV)have been regarded as promising techniques for next-generation communications.To mitigate the strong interferenc...Device-to-device(D2D)communications underlying cellular networks enabled by unmanned aerial vehicles(UAV)have been regarded as promising techniques for next-generation communications.To mitigate the strong interference caused by the line-of-sight(LoS)airto-ground channels,we deploy a reconfigurable intelligent surface(RIS)to rebuild the wireless channels.A joint optimization problem of the transmit power of UAV,the transmit power of D2D users and the RIS phase configuration are investigated to maximize the achievable rate of D2D users while satisfying the quality of service(QoS)requirement of cellular users.Due to the high channel dynamics and the coupling among cellular users,the RIS,and the D2D users,it is challenging to find a proper solution.Thus,a RIS softmax deep double deterministic(RIS-SD3)policy gradient method is proposed,which can smooth the optimization space as well as reduce the number of local optimizations.Specifically,the SD3 algorithm maximizes the reward of the agent by training the agent to maximize the value function after the softmax operator is introduced.Simulation results show that the proposed RIS-SD3 algorithm can significantly improve the rate of the D2D users while controlling the interference to the cellular user.Moreover,the proposed RIS-SD3 algorithm has better robustness than the twin delayed deep deterministic(TD3)policy gradient algorithm in a dynamic environment.展开更多
自动驾驶车辆决策系统直接影响车辆综合行驶性能,是实现自动驾驶技术需要解决的关键难题之一。基于深度强化学习算法DDPG(deep deterministic policy gradient),针对此问题提出了一种端到端驾驶行为决策模型。首先,结合驾驶员模型选取...自动驾驶车辆决策系统直接影响车辆综合行驶性能,是实现自动驾驶技术需要解决的关键难题之一。基于深度强化学习算法DDPG(deep deterministic policy gradient),针对此问题提出了一种端到端驾驶行为决策模型。首先,结合驾驶员模型选取自车、道路、干扰车辆等共64维度状态空间信息作为输入数据集对决策模型进行训练,决策模型输出合理的驾驶行为以及控制量,为解决训练测试中的奖励和控制量突变问题,改进DDPG决策模型对决策控制效果进行优化,并在TORCS(the open racing car simulator)平台进行仿真实验验证。结果表明:所提出的决策模型可以根据车辆和环境实时状态信息输出合理的驾驶行为以及控制量,与DDPG模型相比,改进的模型具有更好的控制精度,且车辆横向速度显著减小,车辆舒适性以及车辆稳定性明显改善。展开更多
基于智能手机的室内定位在研究和工业领域都引起了相当大的关注。然而在复杂的定位环境中,定位的准确性和鲁棒性仍然是具有挑战性的问题。考虑到行人航位推算(PDR,pedestrian dead reckoning)算法被广泛配备在最近的智能手机上,提出了...基于智能手机的室内定位在研究和工业领域都引起了相当大的关注。然而在复杂的定位环境中,定位的准确性和鲁棒性仍然是具有挑战性的问题。考虑到行人航位推算(PDR,pedestrian dead reckoning)算法被广泛配备在最近的智能手机上,提出了一种基于双延迟深度确定性策略梯度(TD3,twin delayed deep deterministic policy gradient)的室内定位融合方法,该方法集成了Wi-Fi信息和PDR数据,将PDR的定位过程建模为马尔可夫过程并引入了智能体的连续动作空间。最后,与3个最先进的深度Q网络(DQN,deep Q network)室内定位方法进行实验。实验结果表明,该方法能够显著减少定位误差,提高定位准确性。展开更多
基金supported in part by the projects of the National Natural Science Foundation of China(62376059,41971340)Fujian Provincial Department of Science and Technology(2023XQ008,2023I0024,2021Y4019),Fujian Provincial Department of Finance(GY-Z230007,GYZ23012)Fujian Key Laboratory of Automotive Electronics and Electric Drive(KF-19-22001).
文摘Autonomous driving has witnessed rapid advancement;however,ensuring safe and efficient driving in intricate scenarios remains a critical challenge.In particular,traffic roundabouts bring a set of challenges to autonomous driving due to the unpredictable entry and exit of vehicles,susceptibility to traffic flow bottlenecks,and imperfect data in perceiving environmental information,rendering them a vital issue in the practical application of autonomous driving.To address the traffic challenges,this work focused on complex roundabouts with multi-lane and proposed a Perception EnhancedDeepDeterministic Policy Gradient(PE-DDPG)for AutonomousDriving in the Roundabouts.Specifically,themodel incorporates an enhanced variational autoencoder featuring an integrated spatial attention mechanism alongside the Deep Deterministic Policy Gradient framework,enhancing the vehicle’s capability to comprehend complex roundabout environments and make decisions.Furthermore,the PE-DDPG model combines a dynamic path optimization strategy for roundabout scenarios,effectively mitigating traffic bottlenecks and augmenting throughput efficiency.Extensive experiments were conducted with the collaborative simulation platform of CARLA and SUMO,and the experimental results show that the proposed PE-DDPG outperforms the baseline methods in terms of the convergence capacity of the training process,the smoothness of driving and the traffic efficiency with diverse traffic flow patterns and penetration rates of autonomous vehicles(AVs).Generally,the proposed PE-DDPGmodel could be employed for autonomous driving in complex scenarios with imperfect data.
文摘Deep deterministic policy gradient(DDPG)has been proved to be effective in optimizing particle swarm optimization(PSO),but whether DDPG can optimize multi-objective discrete particle swarm optimization(MODPSO)remains to be determined.The present work aims to probe into this topic.Experiments showed that the DDPG can not only quickly improve the convergence speed of MODPSO,but also overcome the problem of local optimal solution that MODPSO may suffer.The research findings are of great significance for the theoretical research and application of MODPSO.
基金supported by the National Natural Science Foundation of China under Grant Nos.62201462 and 62271412.
文摘Device-to-device(D2D)communications underlying cellular networks enabled by unmanned aerial vehicles(UAV)have been regarded as promising techniques for next-generation communications.To mitigate the strong interference caused by the line-of-sight(LoS)airto-ground channels,we deploy a reconfigurable intelligent surface(RIS)to rebuild the wireless channels.A joint optimization problem of the transmit power of UAV,the transmit power of D2D users and the RIS phase configuration are investigated to maximize the achievable rate of D2D users while satisfying the quality of service(QoS)requirement of cellular users.Due to the high channel dynamics and the coupling among cellular users,the RIS,and the D2D users,it is challenging to find a proper solution.Thus,a RIS softmax deep double deterministic(RIS-SD3)policy gradient method is proposed,which can smooth the optimization space as well as reduce the number of local optimizations.Specifically,the SD3 algorithm maximizes the reward of the agent by training the agent to maximize the value function after the softmax operator is introduced.Simulation results show that the proposed RIS-SD3 algorithm can significantly improve the rate of the D2D users while controlling the interference to the cellular user.Moreover,the proposed RIS-SD3 algorithm has better robustness than the twin delayed deep deterministic(TD3)policy gradient algorithm in a dynamic environment.
文摘自动驾驶车辆决策系统直接影响车辆综合行驶性能,是实现自动驾驶技术需要解决的关键难题之一。基于深度强化学习算法DDPG(deep deterministic policy gradient),针对此问题提出了一种端到端驾驶行为决策模型。首先,结合驾驶员模型选取自车、道路、干扰车辆等共64维度状态空间信息作为输入数据集对决策模型进行训练,决策模型输出合理的驾驶行为以及控制量,为解决训练测试中的奖励和控制量突变问题,改进DDPG决策模型对决策控制效果进行优化,并在TORCS(the open racing car simulator)平台进行仿真实验验证。结果表明:所提出的决策模型可以根据车辆和环境实时状态信息输出合理的驾驶行为以及控制量,与DDPG模型相比,改进的模型具有更好的控制精度,且车辆横向速度显著减小,车辆舒适性以及车辆稳定性明显改善。
文摘基于智能手机的室内定位在研究和工业领域都引起了相当大的关注。然而在复杂的定位环境中,定位的准确性和鲁棒性仍然是具有挑战性的问题。考虑到行人航位推算(PDR,pedestrian dead reckoning)算法被广泛配备在最近的智能手机上,提出了一种基于双延迟深度确定性策略梯度(TD3,twin delayed deep deterministic policy gradient)的室内定位融合方法,该方法集成了Wi-Fi信息和PDR数据,将PDR的定位过程建模为马尔可夫过程并引入了智能体的连续动作空间。最后,与3个最先进的深度Q网络(DQN,deep Q network)室内定位方法进行实验。实验结果表明,该方法能够显著减少定位误差,提高定位准确性。
