A Predictive Control Design with Speed Previewing Information for Vehicle Fuel Efficiency Improvement

Modeling Driver Behavior in Car-Following Interactions With Automated and Human-Driven Vehicles and Energy Efficiency Evaluation

“…In this section, the energetic impacts of the CAV and the human-driven vehicle on following human-driven vehicles will be comprehensively evaluated in real-world traffic scenarios, considering drivers' behavioral diversity. In this study, an eco-driving strategy which is implemented in our previous work [10] is adopted for the CAV to optimally plan its operations in the car-following scenario. This ecodriving strategy is NMPC based integrated with an instantaneous fuel consumption model and utilizes the preview information of the preceding vehicle (PV) which is a human-driven vehicle, where the CAV is assumed to be able to access the short speed preview information through V2V communication.…”

“…The parameters of the eco-driving design are shown in Table 4. For the details of implementation for the eco-driving strategy, the reader is referred to [10]. Utilizing the driver behavior model and the eco-driving strategy, two case studies are designed in this section to comprehensively evaluate the energetic impacts of the CAV and a human-driven vehicle on following human-driven vehicles in real-world traffic scenarios, considering drivers' behavioral diversity.…”

“…This is commonly referred to as eco-driving in the literature [7][8][9]. In our previous work [10], a Nonlinear Model Predictive Control (NMPC) based eco-driving strategy for a CAV is proposed utilizing the preview speed information from the preceding vehicle through V2V communication. Simulations results show that more than 40% of fuel economy improvement for the CAV compared to the preceding human-driven vehicle can be achieved in real traffic scenarios, particularly under transient traffic conditions.…”

A Comparative Study on Model Predictive Control Design for Highway Car-Following Scenarios: Space-Domain and Time-Domain Model

“…The simulation parameters are provided in Table 1. The parameters to be utilized in the vehicle model and the fuel consumption model are adopted from [36]. Sampling time and sampling distance are determined as 0.2 second and 4.5 meters, respectively.…”

“…Many pieces of MPC-based work focus on reducing fuel consumption during the car-following or vehicle platooning scenarios [5], [10], [33], [34]. For fuel-efficient driving, some works construct a cost-function only with a stand-alone fuel consumption model [25], [35], [36], and others exploit a multi-objective cost function [2], [37]- [39], [39], [40]. Note that the strategy of minimizing fuel consumption usually results in the ego vehicle drifting away from the preceding vehicle within the boundary of inter-vehicle distance, so it contradicts with reducing tracking errors.…”