The Vehicle Intention Recognition with Vehicle-Following Scene Based on Probabilistic Neural Networks

In the vehicle-following scenario of autonomous driving, the change of driving style in the front vehicle will directly affect the decision on the rear vehicle. In this paper, a strategy based on a probabilistic neural network (PNN) for front vehicle intention recognition is proposed, which enables the rear vehicle to obtain the driving intention of the front vehicle without communication between the two vehicles. First, real vehicle data with different intents are collected and time-frequency domain variables are extracted. Secondly, Principal Component Analysis (PCA) is performed on the variables in order to obtain comprehensive features. Meanwhile, two cases are classified according to whether the front vehicle can transmit data to the rear vehicle. Finally, two recognition models are trained separately according to a PNN algorithm, and the two models obtained from the training are verified separately. When the front vehicle can communicate with the rear vehicle, the recognition accuracy of the corresponding PNN model reaches 96.39% (simulation validation) and 95.08% (real vehicle validation). If it cannot, the recognition accuracy of the corresponding PNN model reaches 78.18% (simulation validation) and 73.74% (real vehicle validation).