Abstract
Model predictive control (MPC) has been widely adopted for cooperative adaptive cruise control (CACC) due to its superior performance in achieving fuel-efficient driving while satisfying constraints such as inter-vehicle distance. The core of an MPC-based algorithm is to predict the vehicle’s behavior using a dynamic model, and the space-domain vehicle dynamic model is frequently implemented in recent research along with the time-domain vehicle dynamic model. This paper presents a comparative performance analysis between the space-domain and the time-domain models in the MPC framework for the car-following problem. An MPC design process and analysis method for the high-speed car-following scenario is suggested and presented for equivalent performance comparison between the two approaches. In order to analyze trends between speed tracking and fuel-saving performance, which are conflicting objectives as car-following performance, a bi-objective cost function is proposed and manipulated by various weightings. It is observed that the space-domain model presents stable tracking performance, and the time-domain model shows better fuel efficiency. However, the space-domain model with road information is superior in tracking and fuel efficiency compared to the time-domain model with limited road information. Pareto analysis was implemented to visualize and describe performance differences in various situations regarding tracking error, fuel efficiency, and road grade information levels.
Highlights
T He car-following problem with an autonomous driving system has been actively researched due to increased attention and advancement in cooperative adaptive cruise control (CACC) with a real-time control system
In [2], it is mentioned that safety constraints in the car-following problem can be handled more in the time domain, but this paper suggests that their argument depends on the case
The total fuel consumption of each Model Predictive Control (MPC) framework can be calculated by accumulating the instantaneous fuel consumption, and the performance of each approach will be compared in terms of fuel efficiency
Summary
T He car-following problem with an autonomous driving system has been actively researched due to increased attention and advancement in cooperative adaptive cruise control (CACC) with a real-time control system. In the car-following problem, the MPC technique can improve fuel consumption, tracking error, riding comfort, and accomplish defensive and ecological driving by predicting the behavior of vehicles and satisfying various constraints [12]–[16]. As examined in these MPC applications, the vehicle dynamics model is necessary to predict the vehicle’s behavior, VOLUME 4, 2016. If the inter-vehicle distance is constrained in terms of time headway in the car-following problem, the space-domain approach can produce a lower speed tracking error compared to the timedomain approach. The total fuel consumption of each MPC framework can be calculated by accumulating the instantaneous fuel consumption, and the performance of each approach will be compared in terms of fuel efficiency
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