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Vehicle platooning gains its popularity in improving traffic capacity, safety and fuel saving. The key requirements of an effective platooning strategy include keeping a safe intervehicle space, ensuring string stability and satisfying vehicular constraints. To meet these requirements, this paper proposes a distributed min-max model predictive control (MPC). One technical contribution is that the proposed MPC can guarantee input-to-state predecessor-follower string stability, in the presence of vehicle-to-vehicle communication delays and realistic constraints. Another technical contribution is the development of a new concept of input-to-state stability margin for analyzing the platooning system that is nonlinear under MPC. The proposed MPC is applicable to both homogeneous and heterogeneous platoons because only the point-mass vehicle model is needed. The proposed MPC also has reduced communication burden because each vehicle in the platoon only transmits its current acceleration to the adjacent follower. The design efficacy is verified by simulating a platoon composed of five vehicles under different uncertainties and communication delays.

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Essential decision-making tasks such as power management in future vehicles will benefit from the development of artificial intelligence technology for safe and energy-efficient operations. To develop the technique of using neural network and deep learning in energy management of the plug-in hybrid vehicle and evaluate its advantage, this article proposes a new adaptive learning network that incorporates a deep deterministic policy gradient (DDPG) network with an adaptive neuro-fuzzy inference system (ANFIS) network. First, the ANFIS network is built using a new global K-fold fuzzy learning (GKFL) method for real-time implementation of the offline dynamic programming result. Then, the DDPG network is developed to regulate the input of the ANFIS network with the real-world reinforcement signal. The ANFIS and DDPG networks are integrated to maximize the control utility (CU), which is a function of the vehicle's energy efficiency and the battery state-of-charge. Experimental studies are conducted to testify the performance and robustness of the DDPG-ANFIS network. It has shown that the studied vehicle with the DDPG-ANFIS network achieves 8% higher CU than using the MATLAB ANFIS toolbox on the studied vehicle. In five simulated real-world driving conditions, the DDPG-ANFIS network increased the maximum mean CU value by 138% over the ANFIS-only network and 5% over the DDPG-only network.

SA-YOLOv3: an efficient and accurate object detector using self-attention mechanism for autonomous driving. IEEE Transactions on Intelligent Transportation Systems, 23(5), pp. 4099-4110.

Citation: YANG, J. ... et al, 2016. Quality assessment metric of stereo images considering cyclopean integration and visual saliency.Information Sciences, 373, Additional Information:• This paper was accepted for publication in the journal Informa- AbstractIn recent years, there has been great progress in the wider use of threedimensional (3D) technologies. With increasing sources of 3D content, a useful tool is needed to evaluate the perceived quality of the 3D videos/images. This paper puts forward a framework to evaluate the quality of stereoscopic images contaminated by possible symmetric or asymmetric distortions. Human visual system (HVS)studies reveal that binocular combination models and visual saliency are the two key factors for the stereoscopic image quality assessment (SIQA) metric. Therefore inspired by such findings in HVS, this paper proposes a novel saliency map in SIQA metric for the cyclopean image called "cyclopean saliency", which avoids complex calculations and produces good results in detecting saliency regions. Moreover, experimental results show that our metric significantly outperforms conventional 2D quality metrics and yields higher correlations with human subjective judg- * Corresponding author ment than the state-of-art SIQA metrics. 3D saliency performance is also compared with "cyclopean saliency" in SIQA. It is noticed that the proposed metric is applicable to both symmetric and asymmetric distortions. It can thus be concluded that the proposed SIQA metric can provide an effective evaluation tool to assess stereoscopic image quality.

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