# Integrated Adaptive Cruise Control with Weight Coefficient Self-Tuning Strategy
## 1 Introduction  
The adaptive cruise control (ACC) system is the commercial implementation of the advanced driver assistance system (ADAS) that can reduce drivers' workload (e.g., taking over the longitudinal control task), and improve vehicle safety as well as traffic flow [1; 2]. The designs of current commercial ACC systems mainly focus on tracking a desired speed or maintaining a desired safe distance from preceding vehicles in the same lane. Additionally, various studies have recently begun to explore additional objectives such as fuel economy, longitudinal ride comfort, lateral stability, and vehicular physical limitations [2; 3; 4; 5; 6]. As these mentioned objectives usually conflict with each other [5], coordinating them simultaneously still presents a lot of challenges for designers, especially under complicated and volatile traffic scenarios. [PERSON] et al. [7] pointed out that fuel consumption was much higher under conditions of large acceleration/deceleration, lane changing, as well as cut-in/cut-off. On this basis, [PERSON] et al. [8] suggested reducing fuel consumption while decreasing tracking capability. With respect to the longitudinal ride comfort, the vehicle longitudinal acceleration and its derivative (jerk) are typically restrained [9]. To improve lateral stability, researchers typically employ a direct yaw-moment control (DYC) system [10; 11]. Importantly, an emergency braking, road surface disturbance or a sudden strong side wind might result in vehicle deflection during the longitudinal car-following [11],and furthermore ACC and DYC are reversely interactive with each other under some traffic scenarios [12]. [PERSON] et al. [12] therefore present a curving ACC system that is coordinated with a DYC system and gives consideration to both the longitudinal car-following performance and lateral stability on curved roads. The developed control system provides significant benefits in weakening the impact of DYC on the longitudinal car-following performance while also improving the lateral stability. What is more, in order to achieve the multiple objectives mentioned above, it is natural to introduce a model predictive control (MPC) framework, whose advantages in ACC controllers have recently been proven [2; 5; 6]. However, the traditional model predictive control algorithm with a constant weight matrix will probably not be able to improve the overall performance in transient scenarios [13]. [PERSON] et al. [13] pointed out that the traditional model predictive control algorithm with a constant weight matrix cannot meet the improvement requirement in fuel economy and ride comfort, and accordingly proposed a real-time weight tuning strategy to solve time-varying multi-objective control problems, where the weight matrix can be adjusted with respect to different operating conditions. In addition, when the weight coefficient for each sub-objective is constant, it is easy to cause the distribution of solutions to be more concentrated, and accordingly it will be difficult to achieve the global optimum [14]. For instance, when the current traffic scenario is emergent and severe, the longitudinal tracking capability should be most important to avoid a rear-end collision, which indicates that the importance (namely weight coefficient) of such a sub-objective in the MPC cost function should be paid more attention to. Similarly, when the tire-road friction coefficient is small, or the road curvature is large, priority should be given to the lateral stability for driving safety consideration. As the lateral stability of an automotive vehicle in a steering maneuver is critical to the overall safety [11], an integrated adaptive cruise control (IACC) model which takes the longitudinal inter-vehicle and lateral dynamics into consideration as a whole is suggested so as to achieve a maximization of the longitudinal car-following performance while guaranteeing the lateral stability.