Data-Driven-Based Eco Approach for Connected and Automated Articulated Trucks in the Space Domain

Abstract

Since conventional eco approach systems can only achieve longitudinal automation, they may be disabled due to the impedance of the slow-moving vehicle. In addition, they could sacrifice a lot in travel time and it is hard to control the articulated truck with a complex dynamic model. This research presents an enhanced eco approach system for connected and automated articulated trucks, and is able to: (i) overtake slow-moving vehicles for sustainability and mobility; (ii) efficiently optimize the travel duration approaching a signalized intersection; (iii) achieve the trade-off between fuel saving and vehicle mobility; and (iv) improve computational efficiency and optimality for the articulated truck control. To achieve these features, the problem was formulated as an optimal control problem. A longitudinal and lateral coupled truck dynamic model was utilized for enabling the truck to own the automatic overtaking capability. The data-driven-based Koopman operator theory was adopted to globally linearize the truck dynamic model for reducing the computational burden while ensuring optimality. The optimal control problem is transformed from the time domain to the space domain in order for optimizing travel duration and considering the signal timing constraint. A quantitative evaluation was conducted to validate the performance of the Koopman system dynamics. In addition, the simulation experiment was designed to compare the proposed controller against human drivers and the conventional eco approach, which only has longitudinal automation. The results demonstrate that the proposed controller improves the fuel efficiency by 5.12–67.15%, and outperforms the two baseline controllers by 9.09–32.65% in terms of fuel saving. This range is caused by the different arrival times of the ego articulated truck.