## 1 Introduction  
In recent years, many society and livelihood issues, such as traffic safety, congestion, pollution, and energy consumption are accompanied with the continuous increase of car ownership and traffic flow. According to statistics, more than 94% of traffic accidents are related to the driver's behaviors [1], and the driver's distracted behaviors will affect his operating behaviors, thereby inducing a traffic accident. Additionally, the driver's perceptions of danger greatly affect the driver's driving behaviors. Many scholars carry out research from multiple aspects, such as supervision on fatigue driving [2; 3], prediction of traffic flow [4] and so on, in order to reduce the occurrence of traffic accidents. Meanwhile intelligent driving systems are also being widely used. The current detection of road target information in intelligent driving systems mainly uses the method of combining the target detection algorithm that is based on deep learning and hardware, such as radar. Firstly, all of the road target information is identified by the vision sensor while using the target detection algorithm, and then static or dynamic objects can be identified, detected, and tracked by using radar or infrared technology, thereby taking corresponding measures. At present, the target detection algorithms that are based on deep learning are mainly divided into two categories, one of which is based on the region proposal framework, and the other is based on regression/classification framework [5]. The target detection algorithm that is based on the regional proposal framework is based on the R-CNN target detection model that was proposed by [PERSON] and others in 2014 [6], and uses the idea of this model to propose a large number of better target detection models. The target detection algorithm that is based on the regional proposal framework uses the idea of R-CNN proposed by [PERSON] et al. in 2014, and many scholars have proposed a series of better target detection models. The most representative of them is the Faster R-CNN model that was proposed by [PERSON] et al. [7]. This model greatly improving the detection speed of model. However, limited by the calculation of a large number of candidate regions, there is still a large gap between the model and the real-time detection. For the slow speed, problems, such as R-CNN and Faster R-CNN, the target detection algorithm that is based on the regression/classification framework directly implements the mapping from image pixels to bounding box coordinates and object class probability. This greatly improves the detection speed of the algorithm, but it is slightly inferior in detection accuracy. Its representatives are YOLO and SSD. In 2016, [PERSON] et al. [8] proposed Yolo, which follows the design concept of end-to-end training and real-time detection. Soon after, [PERSON] et al. designed Darknet-19 based on Yolo and proposed Yolov2 [9] while using batch normalization [10]. In 2018, [PERSON] drew ideas from ResNet [11] and Feature Pyramid Networks (FPN) [12] algorithm, and proposed Yolov3 [13], thus greatly improving the detection accuracy. Much of the target detection in the field of intelligent driving is based on Yolo. [PERSON] et al. [14] realized a real-time human-car detector by improving Yolo. [PERSON] et al. [15] proposed a real-time vehicle detection method that was based on Yolov2, which can complete vehicle detection and realize vehicle classification in real time. When compared with Yolo, the Single Shot MultiBox Detector (SSD) that was proposed by [PERSON] et al. [16] has three differences. The first is that SSD uses the Convolutional Neural Network (CNN) to detect directly, instead of detecting after the fully connected layer, like Yolo. Secondly, SSD extracts feature graphs of different scales for detection. The large scale feature graph (the earlier feature graph) can be used to detect small objects, while the small scale feature graph (the later feature graph) is used to detect large objects.