# Adaptive Cruise Control for Intelligent City Bus Based on Vehicle Mass and Road Slope Estimation
## 1 Introduction  
Intelligent Vehicles (IV) and Intelligent Transportation System (ITS) have been hot topics during recent years [1]. They integrate information technology, sensor technology, and automatic control technology, and provide large potential to reduce pollutant emission [2, 3]. As a pivotal component of ITS, Advanced Driver Assistance System (ADAS) which contains Adaptive Cruise Control (ACC) plays an important role in modern passenger cars and commercial vehicles [4, 5, 6, 7]. ACC can adjust the speed by throttle/braking control to achieve vehicle following [8], and includes three modules: perception, decision, and control [9]. The perception module can get the states of vehicle by wheel speed sensor and radar; the decision modules calculate the desired acceleration according to the control strategy; the control modules operate the braking/driving system to control the longitudinal motion of the vehicle. There have been numerous studies on ACC. Safe distance model is a popular way to describe the vehicle collision risk [10]. Compared with Constant Time Headway (CTH), which can only be applied in specific occasions, the desired following distance of Variable Time Headway (VTH) varies with relative speed and has better adaptability [11]. Since VTH does not consider the acceleration of the preceding vehicle, there is more room for further improvement. [PERSON] et al. [12] developed a sliding mode controller which performed better in response to variable speed compared with the classic controller. [PERSON] et al. [13] established the desired safe distance and designed an inner loop controller to maintain following distance with consideration to the speed. ACC is a multi-objective optimization process and apart from the vehicle dynamics and driving safety, the driving comfort and energy consumption should also be optimized. [PERSON] et al. proposed a novel distance controlstrategy to solve the multi-objective coordinate control problem by using objects decoupling control; the proposed control system is beneficial to real-time performance [14; 15]. For multi-objective optimization, MPC is widely used for its effectiveness and optimal performance [16; 17]. However, heavy computation burden is a problem for numerical optimization [18]. [PERSON] et al. presented a fast online computation method to reduce computational burden of the receding horizon optimization in MPC controllers [19]. [PERSON] et al. proposed a moving back (MB) method by fixing some free variables to constant and improved the computational efficiency [20]. Hierarchical control strategy is a popular concept to deal with the relationship between the controller and actuators in the ACC [21; 22; 23]. The upper-level controller is designed to balance tracking capability, driving safety, and energy consumption. The lower-level controller realizes the vehicle following by adjusting the driving and braking system. Real-time road condition and vehicle status are essential information for ACC; due to limitations of sensors, some road information cannot be obtained directly [24]. In ACC control strategies, vehicle mass and road slope are usually considered as constants which will cause the inaccurate calculation of vehicle status. Therefore, estimations of vehicle mass and road slope are essential [25]. Recursive Least Squares (RLS), Kalman Filter (KF), and Slide Mode Observer (SMO) are the main methods used to estimate vehicle mass and road slope. RLS is vulnerable to noise and it will get the biased parameter estimation [26; 27]. In order to solve the nonlinearity of the model, Kalman Filter-based for nonlinear filtering algorithms is applied and implemented on a real-time ECU [28]. In recent years, Machine Learning (ML) has been widely used in the field of intelligent driving. [PERSON] [29] et al. proposed a ML-based approach to estimate trailer mass.