To address the problems that rule-based motion planning algorithms under constraints need pre-definition of rules and temporal features are not considered in deep learning-based methods, a motion planning model based on deep cascading neural networks was proposed. In this model, the two classical deep learning models, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network, were combined to build a novel cascaded neural network, the spatial and temporal features of the input images were extracted respectively, and the nonlinear relationship between the input sequential images and the output motion parameters were fit to achieve the end-to-end planning from the input sequential images to the output motion parameters. In experiments, the data of simulated environment were used for training and testing. Results show that the Root Mean Squared Error (RMSE) of the proposed model in four scenes including country road, freeway, tunnel and mountain road is less than 0.017, and the stability of the prediction results of the proposed model is better than that of the algorithm without using cascading neural network by an order of magnitude. Experimental results show that the proposed model can effectively learn human driving behaviors, eliminate the effect of cumulative errors and adapt to different scenes of a variety of road conditions with good robustness.

To deal with the difficulty and low performance of emergency crowd evacuation in public spaces, a crowd evacuation method using robots based on the social force model was proposed. A new human-robot social force model based on the original social force model was first developed, where the human-robot interaction from robots to pedestrians was added to the original social force model. And then, a new method using robot based on the human-robot social force model was presented to evacuate the crowd. After joining the crowd evacuation scenes, the robots can influence the motion of the surrounding pedestrians and reduce the pressure among the pedestrians by moving in the crowd, thus increasing the crowd motion speed and improving the efficiency of crowd evacuation. Two classical scenarios, including a group of crowd escaping from a closed environment and two groups of crowd moving to each other by crossing, were designed and simulated to test the proposed method, and the crowd evacuation method without robots was used for comparison. The experimental results demonstrate that the proposed method based on human-robot social force model can obviously increase the crowd motion speed and improve the efficiency of crowd evacuation.