Autonomous driving is a promising technology to reduce traffic accidents and improve driving efficiency. In this work, a deep reinforcement learning (DRL)-enabled decision-making policy is constructed for autonomous vehicles to address the overtaking behaviors on the highway. First, a highway driving environment is founded, wherein the ego vehicle aims to pass through the surrounding vehicles with an efficient and safe maneuver. A hierarchical control framework is presented to control these vehicles, which indicates the upper-level manages the driving decisions, and the lower-level cares about the supervision of vehicle speed and acceleration. Then, the particular DRL method named dueling deep Qnetwork (DDQN) algorithm is applied to derive the highway decision-making strategy. The exhaustive calculative procedures of deep Q-network and DDQN algorithms are discussed and compared. Finally, a series of estimation simulation experiments are conducted to evaluate the effectiveness of the proposed highway decision-making policy. The advantages of the proposed framework in convergence rate and control performance are illuminated. Simulation results reveal that the DDQN-based overtaking policy could accomplish highway driving tasks efficiently and safely. INDEX TERMS Autonomous driving, decision-making, deep reinforcement learning, dueling deep Qnetwork, deep Q-learning, overtaking policy.

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In this paper, a dual-task convolutional neural network based on the combination of the U-Net and a diffraction propagation model is proposed for the design of phase holograms to suppress speckle noise of the reconstructed images. By introducing a Fresnel transmission layer, based on angular spectrum diffraction theory, as the diffraction propagation model and incorporating it into U-Net as the output layer, the proposed neural network model can describe the actual physical process of holographic imaging, and the distributions of both the light amplitude and phase can be generated. Afterwards, by respectively using the Pearson correlation coefficient (PCC) as the loss function to modulate the distribution of the amplitude, and a proposed target-weighted standard deviation (TWSD) as the loss function to limit the randomness and arbitrariness of the reconstructed phase distribution, the dual tasks of the amplitude reconstruction and phase smoothing are jointly solved, and thus the phase hologram that can produce high quality image without speckle is obtained. Both simulations and optical experiments are carried out to confirm the feasibility and effectiveness of the proposed method. Furthermore, the depth of field (DOF) of the image using the proposed method is much larger than that of using the traditional Gerchberg-Saxton (GS) algorithm due to the smoothness of the reconstructed phase distribution, which is also verified in the experiments. This study provides a new phase hologram design approach and shows the potential of neural networks in the field of the holographic imaging and more.

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