# Vehicle Acceleration Prediction Based on Machine Learning Models and Driving Behavior Analysis
## 3 Personalized Driving Behavior Analysis Model  
### Data Description  
All of the driving data we used were obtained from the SPMD database. This database recorded car-following data from 2842 equipment vehicles in the United States from 2012 to 2014. Overall, the SPMD data collection consisted of the surrounding environment (63 vehicles), light vehicle drivers (2836 drivers), heavy vehicle fleets (3 vehicles), and a transport vehicle fleet (3 vehicles). The light vehicle drivers were from the University of Michigan and its neighboring communities, and held valid driver's licenses. The on-board devices include real-time data collection systems and Mobieye that continuously observe driving behavior over a period of time. The mobieye is responsible for recording road base information (e.g., the lane width, lane curvature, and number of lanes) and information about the surrounding vehicles (e.g., relative speed, relative distance). The information of the target vehicle is stored in a CAN-bus signal (e.g., speed, steering angle, pedal position). The data were collected at a frequency of 10 Hz. In addition, the driver cannot see the on-board devices while driving normally, which can avoid unnecessary interference to a certain extent [22].  
We selected 30 drivers with similar driving scenes from the SPMD database and extracted their car-following trajectory data. This study did not consider data which were outliers, or missing frames greater than 1 s. For the missing durations of the follow-along fragment less than 1 s, this study used linear interpolation to make up for the missing duration [23]. In addition, we extracted the following conditions for car-following segments in the original data: (1) the lead vehicle and the subject vehicle are in the same lane; (2) the relative distance between two vehicles is less than 120 m and greater than 5 m; (3) the speed of the subject vehicle is greater than 18 km/h; (4) no overtaking event occurs; (5) the duration of a car-following segment is greater than 50 s [24].  
A single variable, such as acceleration, does not characterize driving behavior well. Thus, environmental variables need to be included in order to help us analyze driving behavior. However, if we have too many variables, it will increase the calculation difficulty of the personalized driving behavior analysis model [25]. This study selects three characteristic variables from the SPMD, including the acceleration of subject vehicle \(a_{x}\), the relative distance \(\Delta d=x_{2}-x_{1}\) and the relative speed \(\Delta v=v_{2}-v_{1}\) between the subject vehicle and the lead vehicle [24].  
This paper studied the driving behavior of drivers in a stable car-following scene. The driving environment is described by two variables, relative distance and relative speed, and the lead vehicles are all sedans. There are indeed some external environment variables that we did not consider, such as weather and traffic conditions, etc., because this information is not available in the SPMD dataset. These can be explored further when more complete car-following data information is available.