论文标题
基于重新网和有效网络模型的车辆轨迹预测
The Vehicle Trajectory Prediction Based on ResNet and EfficientNet Model
论文作者
论文摘要
目前，应用自动驾驶技术的主要挑战是对车辆轨迹的准确预测。随着计算机技术的迅速发展和卷积深度神经网络的出现，预测结果的准确性得到了提高。但是，网络的深度，宽度和图像分辨率仍然是限制模型准确性和预测结果的重要原因。本文的主要创新是Resnet网络和有效的网络的组合，这不仅可以大大提高网络深度，而且还全面地改变了网络宽度和图像分辨率的选择，从而使模型性能更好，还可以尽可能地节省计算资源。实验结果还表明，我们提出的模型获得了最佳预测结果。具体而言，我们方法的损耗值分别少4个，而2.1比Resnet和效率网络方法少2.1。
At present, a major challenge for the application of automatic driving technology is the accurate prediction of vehicle trajectory. With the vigorous development of computer technology and the emergence of convolution depth neural network, the accuracy of prediction results has been improved. But, the depth, width of the network and image resolution are still important reasons that restrict the accuracy of the model and the prediction results. The main innovation of this paper is the combination of RESNET network and efficient net network, which not only greatly increases the network depth, but also comprehensively changes the choice of network width and image resolution, so as to make the model performance better, but also save computing resources as much as possible. The experimental results also show that our proposed model obtains the optimal prediction results. Specifically, the loss value of our method is separately 4 less and 2.1 less than that of resnet and efficientnet method.
