Chaos, Solitons and Fractals 89 (2016) 232–242 Contents lists available at ScienceDirect Chaos, Solitons and Fractals Nonlinear Science, and Nonequilibrium and Complex Phenomena journal homepage: www.elsevier.com/locate/chaos An APF and MPC combined collaborative driving controller using vehicular communication technologies Zichao Huang a,∗ , Qing Wu a , Jie Ma b , Shiqi Fan a a School of Logistics Engineering, Wuhan University of Technology, Wuhan, Hubei 430063, China b School of Navigation, Wuhan University of Technology, Wuhan, Hubei 430063, China a r t i c l e i n f o Article history: Available online 17 December 2015 Keywords: Autonomous car Vehicular communication Collaborative driving Model predictive control Artificial potential field a b s t r a c t Collaborative driving is a growing domain of Intelligent Transportation Systems (ITS) which aim to navigate traffic both efficiently and safely. Cooperation between vehicles heavily rely on the comprehensive information collected. With the development of vehicular communication technologies, information can be shared between vehicles or infrastructures through Vehicle- to-Vehicle (V2V)/Vehicle-to-Infrastructure (V2I) data exchange. By taking advantage of data sharing between vehicles, this paper proposes an Artificial Potential Field (APF) and Model Predictive Control (MPC) combined controller to implement collaborative driving in complex environments. Firstly, an APF model containing three components is developed to describe the mutual effect and collaboration properties between vehicles and surrounding environ- ments. Afterwards, a MPC cost function for optimized control, considering both kinematic characteristics and environmental effect conveyed by APF, is presented to address the problem of collaborative driving. Such controller is designed from the perspective of multi-objective and multi-constraint optimization which takes the vehicle motion constraints, safety and comfort requirements into consideration. The prominent advantage of the proposed approach is that it can deal with the problems of route planning and manipulating simultaneously. To validate the proposed approach, a variety of scenario simulations are conducted in MATLAB, and the performance of the proposed method are verified. © 2015 Elsevier Ltd. All rights reserved. 1. Introduction As the number of vehicles has been increasing signifi- cantly in the last few decades, transportation systems are subjected to severe challenges such as traffic congestion, air pollution and road safety. Autonomous intelligent ve- hicles promise many improvements in terms of accident avoidance and mitigation, better road utilization and en- ergy saving. The autonomous intelligent vehicle can be re- garded as an active safety system which synthesize many active safety technologies such as adaptive cruise control (ACC), pre-crash systems (PCSs), and Lane-keeping assistants (LKAs). Kichun Jo et al. [1] point out that an autonomous ∗ Corresponding author. E-mail address: hzc@whut.edu.cn. intelligent vehicle is a self-driving vehicle that has the ca- pability to perceive the surrounding environment and navi- gate itself without human intervention. Compared with hu- man driving, fully automatic driving can significantly shorten the reaction time and latency, improve safety and road utilization [2]. However, unique challenges including en- vironment perception and modeling, localization and map building, path planning and decision-making, and motion control are still open issues for the autonomous intelligent vehicles. The performance of the autonomous driving system greatly depends on the grasp of the information about the surrounding environment. The data sharing between ve- hicles using vehicular communication technologies can be more effective in avoiding accidents and traffic congestions http://dx.doi.org/10.1016/j.chaos.2015.11.009 0960-0779/© 2015 Elsevier Ltd. All rights reserved.