## 1 Introduction  
Autonomous electric vehicles are widely discussed in the vehicle industry owing to their integrated intelligence systems and effective energy-saving technology. When an electric vehicle applies regenerative braking, the motor acts as a generator that stores kinetic energy in the energy storage device or battery [1]. In the driving state, the motor uses energy from this battery. When applying the brake or during vehicle deceleration, the motor rotates backward and regenerates energy. This is known as a regenerative braking system. The energy flow in regenerative braking systems is depicted in Figure 1. High energy savings are achieved in electric or hybrid vehicles by effectively extracting the energy through the motor when applying the brake [2]. The difference between normal braking and regenerative braking is shown in Figure 2, where the left image represents regenerative braking and the right image shows a discharging process, as seen in [3]. The speed management process and braking conditions are key parameters in vehicle energy efficiency. In traditional vehicles, the driver manually controls the vehicle's speed. It is indeed challenging to maximize the efficiency of a regenerative braking system because road conditions can vary significantly and cannot be easily predicted. Traditional methods for maximizing efficiency typically rely on accurate traffic information to determine the most efficient way to use regenerative braking. However, this can be difficult to obtain in real-time, especially if the vehicle is traveling on unfamiliar roads or in areas with limited traffic data. One way to overcome this challenge is to use sensors and various technologies to continuously monitor the road conditions and adjust the braking strategy. This can help to optimize the efficiency of the regenerative braking system and improve the overall performance of the vehicle. The machine learning method can appropriately mimic human driving because it can train the characteristics of human driving [4]. Many researchers have conducted studies to predict a vehicle's acceleration and velocity by using artificial neural network methods. [PERSON] et al. [5] applied genetic algorithms to evaluate the driving distance and state of charge (SOC) by using dynamic programming to generate an SOC curve. [PERSON] et al. [6] developed a prediction algorithm by combining neural network and Markov chain methods according to energy conditions in plug-in hybrid electric vehicles. [PERSON] et al. [7] applied the support vector method (SVM) and exponential prediction model to predict vehicle speed. The above researchers evaluated several parameters, such as road speed and traffic information, but failed to design a comfortable system. [PERSON] and [PERSON] [8] measured riding comfort based on the vibrations transmitted to a passenger according to the road and vehicle conditions. In the vehicle driving state, the acceleration acting on the passenger was analyzed by varying the values of the influencing factors. [PERSON] et al. [9] presented a method to improve riding comfort during braking, in which the electromechanical brake was applied in a longitudinal acceleration direction to reduce jerk (the derivative of acceleration). In addition, they designed a controller to lower discomfort and improve riding comfort. [PERSON] et al. [10] researched the improvement of a passenger's comfort in autonomous vehicles, proposed measuring the degree of discomfort according to the speed and road surface conditions, and evaluated different speed control strategies. The ride comfort felt by passengers is key to systemic vehicle development because it is an important indicator that considerably influences vehicle performance. Many studies on vehicle braking and avoiding extreme situations during driving require the maximum performance of the vehicle [11]. The ride comfort and vehicle jerk have a complex relationship and exhibit nonlinear characteristics when compared to a biomechanically measured human response [12].