# Research on Vehicle Trajectory Prediction and Warning Based on Mixed Neural Networks
###### Abstract  
When driving on roads, the most important issue for driving is safety. There are various vehicles, including cars, motorcycles, bicycles, and pedestrians, that increase the complexity of road conditions and the burden on drivers. In order to improve driving safety, a deep learning framework is applied to predict and announce the trajectory of a car. This research is divided into three parts. Lane line detection is adopted first. Secondly, car object detection is employed. Lastly, car trajectory prediction is a key part of our research. In addition, real images and videos in the driving recorder are used to simulate the real situation the driver sees from the driver's seat. Car detection is utilized to obtain the coordinates of the car in these images, reaching an accuracy of 0.91 and then predicting the future trajectory of the car, obtaining a loss of 0.00024 and costing 12 milliseconds. It can precisely mark the position of the car, accurately detect the lane line, and predict the future car's trajectory. Through the prediction and announcement of the car trajectory, we verified that our model can correctly predict the car trajectory and truly enhance the safety of driving. deep learning; object detection; trajectory prediction; lane line detection +
Footnote †: journal: Medical  
information by combining multiple models. The prediction results were as good as those in [6], but our proposed method could obtain more information about the road. In [7], the authors used social LSTM with a spatial attention model and trajectory attention model to predict the car trajectory. In [8], LSTM encoder-decoder neural network architecture was used. In [9], object detection was used to track an object. Our proposed method could reach 10.995 frames per second (FPS), which was better than the 3 FPS achieved in [9]. Without the complex structure in [7; 8], an efficient LSTM-based model was proposed and realized. The performance was as good as that in [7; 8; 9], but it took very little time to train and test our model. In addition, our proposed method contained multiple models to give more valuable information, which could detect quickly, warn the drivers, and improve driving safety. In this paper, our aim was to use multiple models to build a system that could predict and warn. A method combining object detection of a car, lane line detection, and trajectory prediction of the car was proposed. Object detection of a car is capable of detecting the car, which can help us distinguish the car on the road. YOLO (you only look once) [10; 11] has excellent results in object detection. Furthermore, YOLO object detection is easy to train to some extent. It is not only accurate but also takes very little time for detection. Lane line detection can find the lane line on the road. An improved Hough transform method [12], which uses wavelet, canny, and the Hough transform to detect lane lines, shows good correctness and real-time performance for lane line detection. In [13], a novel formulation was used and a structural loss was proposed, achieving state-of-the-art performance in terms of both speed and accuracy. We used a simple method based on OpenCV [14] for lane line detection. This technique assists the drivers to drive in the appropriate lane area, thereby reducing the chances of accidents. It can also detect the direction of the steering wheel to the left, the right, or straight ahead. The crucial part of this research is trajectory prediction of a car. We retained the idea of [6] and proposed a new prediction model based on an LSTM structure [5; 15]. If we could predict the possible trajectory of the car, we could preventively slow it down or take other suitable measures. At last, we combined all these techniques together. It was through the combination of multiple models that our system could detect cars and lane lines and predict the future trajectory of the car, showing good prediction and improving driving safety. Figure 1 shows the proposed framework of this research.