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Comprehensive predictive control method for automated vehicles in dynamic traffic circumstances

Comprehensive predictive control method for automated vehicles in dynamic traffic circumstances

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Motion control problems remain to be fully solved for automated vehicles in dynamic traffic circumstances. Existing approaches usually first make a driving behaviour decision, then design a reference trajectory that may not match the vehicle dynamic constraints explicitly and finally adopt a local feedback control method to track the reference. Important commands may be lost or not well translated in the process of information exchange and transmission. Moreover, multiple methods specifically designed for different tasks may not cooperate well in one system. In this study, the authors propose a comprehensive predictive control method which can directly generate the control commands from the traffic circumstance and the vehicle dynamics, without involving any driving decision-making modules and any predefined reference trajectories. Virtual potential fields are introduced to model the traffic circumstance including the road boundaries, lane markings and moving obstacle vehicles. A model predictive control problem is formulated with the overall potential function and constraints including the vehicle dynamics and the safety distances between the ego vehicle and other vehicles. Lane keeping, lane changing, car following and overtaking driving behaviours are simulated in different scenarios. Results show that this method is capable of controlling the automated vehicle in different traffic circumstances.

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