Prediction of the future location of vehicles and other mobile targets is instrumental in intelligent transportation system applications. In fact, networking schemes and protocols based on machine learning can benefit from the results of such accurate trajectory predictions. This is because routing decisions always need to be made for the future scenario due to the inevitable latency caused by processing and propagation of the routing request and response. Thus, to predict the highprecision trajectory beyond the state-of-the-art, we propose a Generative Adversarial Network-based VEhicle trajEctory Prediction method, GAN-VEEP, for urban roads. The proposed method consists of three components, 1) vehicle coordinate transformation for data set preparation, 2) neural network prediction model trained by GAN, and 3) vehicle turning model to adjust the prediction process. The vehicle coordinate transformation model is introduced to deal with the complex spatial dependence in the urban road topology. Then, the neural network prediction model learns from the behavior of vehicle drivers. Finally, the vehicle turning model can refine the driving path based on the driver's psychology. Compared with its counterparts, the experimental results show that GAN-VEEP exhibits higher effectiveness in terms of the Average Accuracy, Mean Absolute Error, and Root Mean Squared Error.

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In this work, a disturbance observer is designed to obtain the total disturbance including the unknown part, and the error system of the disturbance observer is proved to be finite-time convergence. Based on the total disturbance, a sliding mode controller with exponential reaching rule is proposed for the speed control system of a marine diesel engine. The performance of the proposed controller and the disturbance observer is tested by the simulations on a marine diesel engine model. It shows that the proposed disturbance observer can achieve satisfactory results, and both the control performance and the robustness of the DOSMC are superior to the PID controller.