This study raises a new electric wheel with active control of suspension for in-wheel motor. Proceeding from the reality, we made a simulation analysis on these four models: a sample car with no motor, a motor with fixed joint, and active/inactive control suspension for in-wheel motor. During the analysis, less-than-10mm relative vertical displacement was used as a prerequisite, reducing the car's acceleration, the suspension flexivety, and the wheel's load as basic conditions, reducing the wheel's vertical acceleration as the primary target, LQR control of active suspension as the motive. According to the analysis, by optimizing the parameter of inactive control suspension for in-wheel motor, we can solve the vertical problems caused by the electric wheel's mass, but the vertical impact to in-wheel motor is still heavy. However, using active control suspension for in-wheel motor can not only rise the smoothness and safety of the car, but also reduce 64% of the vertical impact comparing to inactive control suspension. This improves the active control suspension for in-wheel motor can effectively resolve the electric cars' vertical problems both theoretically and practically.

A human-like driving model can help to improve the acceptance and safety of automated driving systems (ADS). To improve the performance of human-like driving and interaction with conventional vehicles of ADS, the speed behavior of left-turn vehicles at the signalized intersection was studied using natural driving data. In this study, 374 valid data points of left-turn snippets at signalized intersections were extracted and three phases were introduced based on the reaction behavior of braking, stopping, and accelerating in the left-turn process. Firstly, a one-way ANOVA was used to study the influence of traffic density, traffic light state, intersection type, and left-turn waiting area on the reaction position of each phase and the spatial distribution of the speed. The traffic light state and traffic density were the main significant effects. Furthermore, to analyze the spatial distribution of acceleration, a method of frequency contour was conducted. The butterfly-shaped frequency contour suggested that “the closer to the stop line, the higher the variation of acceleration”. Finally, the driving parameters at each phase were further analyzed. The main results indicate the following: (1) The red traffic light will lead to a larger variation of acceleration, a larger maximum deceleration, a larger starting acceleration, and a larger maximum acceleration. (2) On the condition of dense traffic density, more stops and the duration of the stop–go phase may cause the time pressure, and the driver tends to choose a greater maximum acceleration. (3) The red traffic light leads to a further reaction distance of all three phases, whilst increased traffic density only increases the reaction distance of the stop. (4) Both the dense traffic density and red traffic light lead to an earlier reaction time. The findings can provide a basis for the design of human-like driving of left-turn driving assistance systems and improve the interaction with left-turn conventional vehicles.