## 1 Introduction  
With the rapid development of vehicle technology [1; 2; 3], considerable attention has been devoted to intelligent vehicles [4; 5; 6]. Various technologies have been investigated to satisfy the demanding requirements of commercialization. In particular, lane keeping systems (LKSs) and cruise control are two fundamental technologies for intelligent vehicles. A number of studies have been performed in these critical areas [7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22]. A nested proportional-integral-derivative (PID) algorithm with two closed loops was presented for lane keeping in [7]. A super-twisting sliding mode scheme was suggested in [8] to eliminate vehicle lateral tracking error and handle the chatting phenomenon. Three kinds of sliding mode control algorithms were suggested and compared with regard to the actuator saturation scenario in [9]. In [10], a control algorithm consisting of inner and outer loops in the control system was proposed for an LKS based on sliding mode control. In [11], the authors addressed the problem of integrated direct yaw moment control and front steering for an LKS with a transfer function. In [23], a proportional-type space-learning control scheme was constructed for vehicle path tracking, and a stability analysis of the proposed scheme was also performed. In [12], to achieve guaranteed lane keeping and speed tracking performance, a motion controller was developed based on fuzzy model predictive control. In [13], to consider nonlinear vehicle dynamics, a cruise control algorithm was proposed using nonlinear model predictive control. In [14], a self-learning cruise control scheme, in which the system parameters can be adjusted online, was developed for speed tracking. In [15], using a fractional linear feedforward prefilter, a cruise control method was designed considering an acceleration signal. In [16], a cruise controller was constructed considering energy consumption. In [17], the development of cruise control at a motor company was described. In [18], a cone feedback control strategy was extended to a cruise control application. In [19], a super-twisting sliding mode algorithm with adaptive gains was used in the design of a cruise controller. In [20], a cruise controller was proposed based on a fuzzy PID control method. In [21], a piecewise method was used in the development of a cruise control system. In [22], an online learning fuzzy scheme was considered when developing a cruise control system. In [24], an indirect field-oriented control algorithm was proposed for electric vehicles considering the motor uncertainty. Simulations and experiments have shown that the algorithms proposed in these studies are effective for lane keeping or cruise control on normal roads; thus, these investigations have been useful in the development of intelligent vehicles. Most of these studies have focused on normal roads with constant friction coefficients. However, due to changes in weather or the traffic environment, the friction coefficient may be reduced in real situations. For instance, water or ice on the road surface may significantly decrease the friction coefficient, causing the handling performance of a vehicle on a curved road to degrade. At high speeds, the lane keeping performance may not be maintained due to the limited adhesion on a curved road with a low friction coefficient; however, this concern has seldom been considered in previous studies on lane keeping and cruise control technologies. Thus, it is essential to develop an algorithm that can be used for online speed planning based on estimated friction coefficients. On a curved road with a high curvature and a low friction coefficient, such an algorithm can plan a lower speed to ensure safety. The aim of this paper is to design an integrated speed planning and friction coefficient estimation algorithm to address changing friction coefficients on curved roads to avoid potential accidents. Two key points should be considered in regard to such road conditions.