Improving the learning of self-driving vehicles based on real driving behavior using deep neural network techniques

Zaghari Nayereh; Fathy Mahmood; Jameii Seyed Mahdi; Sabokrou Mohammad; Shahverdy Mohammad

摘要

Considering the significant advancements in autonomous vehicle technology, research in this field is of interest to researchers. To drive vehicles autonomously, controlling steer angle, gas hatch, and brakes needs to be learned. The behavioral cloning method is used to imitate humans' driving behavior. We created a dataset of driving in different routes and conditions, and using the designed model, the output used for controlling the vehicle is obtained. In this paper, the learning of self-driving vehicles based on real driving behavior using deep neural network techniques (LSV-DNN) is proposed. We designed a convolutional network which uses the real driving data obtained through the vehicle's camera and computer. The response of the driver during driving is recorded in different situations, and by converting the real driver's driving video to images and transferring the data to an Excel file, obstacle detection is carried out with the best accuracy and speed using the Yolo algorithm version 3. This way, the network learns the response of the driver to obstacles in different locations and the network is trained with the Yolo algorithm version 3 and the output of obstacle detection. Then, it outputs the steer angle and amount of brake, gas, and vehicle acceleration. This study focuses on designing a convolutional network using behavioral cloning and motion planning of autonomous vehicle using a deep learning framework. Neural networks are effective systems for finding relationships between data, modeling, and predict new data or classify data. As a result Neural networks with input real data predict steer angle and speed for autonomous driving. The LSV-DNN is evaluated here via extensive simulations carried out in Python and TensorFlow environment. We evaluated the network error using the loss function. The results confirmed that our scheme is capable of exhibiting high prediction accuracy (exceeding 92.93%). In addition, our proposed scheme has high speed (more than 64.41%), low FPR (less than 6.89%), and low FNR (less than 3.95%), in comparison with the other approaches currently being employed. By comparing other methods which were conducted on the simulator's data, we obtained good performance results for the designed network on the data from KITTI benchmark, the data collected using a private vehicle, and the data we collected.

机译：考虑到自动车辆技术的重要进步，研究人员的研究是兴趣的。为了自主驱动车辆，需要学习控制转向角度，气体舱口和制动器。行为克隆方法用于模仿人类的驾驶行为。我们在不同的路线和条件下创建了一个驱动的数据集，并使用设计的模型，获得用于控制车辆的输出。本文提出了基于使用深神经网络技术（LSV-DNN）的基于实际驾驶行为的自动驾驶车辆的学习。我们设计了一种卷积网络，它使用通过车辆的相机和计算机获得的真实驾驶数据。驱动期间驱动器的响应被记录在不同的情况下，并且通过将真实驾驶员的驱动视频转换为图像并将数据传送到Excel文件，使用Yolo算法3的最佳精度和速度来执行障碍物检测。这样，网络将驾驶员对不同位置的障碍物的响应学习，并且网络用Yolo算法版本3和障碍物检测的输出训练。然后，它输出转向角度和制动，气体和车辆加速度。本研究侧重于使用深层学习框架使用自主车辆的行为克隆和运动规划来设计卷积网络。神经网络是用于查找数据，建模和预测新数据或分类数据之间的关系的有效系统。结果，具有输入实际数据的神经网络预测自动驾驶的转向角度和速度。这里通过Python和Tensorflow环境中进行的广泛模拟评估LSV-DNN。我们使用损耗函数评估网络错误。结果证实，我们的方案能够表现出高预测精度（超过92.93％）。此外，与目前正在采用的其他方法相比，我们所提出的方案具有高速（超过64.41％），低FPR（小于6.89％），低FPR（小于3.95％）。通过比较在模拟器数据上进行的其他方法，我们为来自Kitti基准测试的数据的设计网络获得了良好的性能结果，使用私人车辆收集的数据以及我们收集的数据。

Improving the learning of self-driving vehicles based on real driving behavior using deep neural network techniques

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