Abstract
Of all driving functions, one of the critical maneuvers is the lane merge. A cooperative Nonlinear Model Predictive Control (NMPC)-based optimization method for implementing a highway lane merge of two connected autonomous vehicles is presented using solutions obtained by the direct multiple shooting method. A performance criteria cost function, which is a function of the states and inputs of the system, was optimized subject to nonlinear model and maneuver constraints. An optimal formulation was developed and then solved on a receding horizon using direct multiple shooting solutions; this is implemented using an open-source ACADO code. Numerical simulation results were performed in a real-case scenario. The results indicate that the implementation of such a controller is possible in real time, in different highway merge situations.
Highlights
Autonomous vehicle technology has been a significant research and development topic in the automotive industry during the last decade
The results indicate that the implementation of such a controller is possible in real time, in different highway merge situations
This paper presents a Nonlinear Model Predictive Control (NMPC)-based optimization method for highway merge, respecting certain merge constraints
Summary
Autonomous vehicle technology has been a significant research and development topic in the automotive industry during the last decade. Vehicle automation today is based on advanced sensor technology used to sense the environment [6] It depends on fast, reliable vehicle-to-vehicle (V2V) communication and intelligent control schemes (i.e., involving machine learning algorithms) to implement complex maneuvers. Vehicles 2020, 2 to automate this maneuver, simultaneous control of acceleration, brake, and steering is required. While controlling these inputs, the controller needs to track the road profile, respect the speed limits, and look out for traffic on the highway. For strategic lateral lane change control, the surrounding vehicle environment is crucial It includes the vehicles present in the current lane and in adjacent lanes along with their dynamics. An active steering system based on kinematic and dynamic models was used in simulating path planning steering control [18]
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