Research on Path Planning of Driverless Vehicle Based on Optimized Artificial Potential Field Method
The traditional artificial potential field method has some problems in vehicle path planning,such as unattainable target and local optimization.The traditional artificial potential field algorithm is optimized by optimizing the obstacle model to improve the accuracy and smoothness of the planned path.Then combined with the model predictive control algorithm,the driving plan-ning path of driverless car is tracked and verified.The results show that the optimized model can generate an accurate and smooth path.The model prediction algorithm can accurately realize trajectory tracking,that is,the trajectory tracking error is less than 0.20 m under lane changing conditions,and within the ideal error range,the tracking error under overtaking conditions is-0.4～0.6 m,which can not only achieve ideal overtaking,but also ensure the stability and safety of vehicle driving.
artificial potential field methodpath planningdriverless carmodel prediction algorithm
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