A Driving Warning Method based on YOLOV3 and Neural Network Unmanned driving warning is one of the important core issues of unmanned driving decision. This paper studies the method of prediction on the traffic warning information for unmanned driving warning, which is based on YOLOV3 and BP neural network. Firstly, weight training is carried out through selfmade data set. Secondly, the trained YOLOV3 model is used to classify the image and video data: car, bicycle, bus, motorbike, person. Finally, the YOLOV3 target detection information will be classified into 5 categories by the BP neural network: safe, stop, slow, left, right. The result of the simulated experiment shows that the model works well. Based on different data set, the accuracy of the second classification is up to 0.99.

Lane Departure Warning Mechanism of Limited False Alarm Rate using Extreme Learning Residual Network and xcfxb5greedy LSTM Neglecting the driver behavioral model in lanedeparturewarning systems has taken over as the primary reason for false warnings in humanxe2x80x93machine interfaces. We propose a machine learningbased mechanism to identify driversxe2x80x99 unintended lanedeparture behaviors, and simultaneously predict the possibility of driver proactive correction after slight departure. First, a deep residual network for driving state feature extraction is established by combining time series sensor data and three serial ReLU residual modules. Based on this feature network, online extreme learning machine is organized to identify a driverxe2x80x99s behavior intention, such as unconscious lanedeparture and intentional lanechanging. Once the system senses unconscious lanedeparture before crossing the outermost warning boundary, the xcfxb5greedy LSTM module in shadow mode is roused to verify the chances of driving the vehicle back to the original lane. Only those unconscious lanedepartures with no driversxe2x80x99 proactive correction behavior are transferred into the warning module, guaranteeing that the system has a limited false alarm rate. In addition, naturalistic driving data of twentyone drivers are collected to validate the system performance. Compared with the basic timetolinecrossing (TLC) method and the TLCDSPLS method, the proposed warning mechanism shows a largescale reduction of 12.9% on false alarm rate while maintaining the competitive accuracy rate of about 98.8%.