Abstract:

In this paper， an automatic test scene primitive extraction method from the naturalistic driving dataset for autonomous vehicles is proposed. With the hidden Markov model （HMM） as framework， the extraction method uses the vector autoregressive model as the observation probability distribution function， and adopt the Hierarchical Dirichlet Process to fulfill prior distribution and posterior updating for HMM. Then the disentangled and sticky process are used to suppress the frequent switching of the HMM hidden states for achieving the solution of hidden states with given observation data， and the scene primitives are automatically divided according to the hidden states. Finally， a piece of naturalistic driving series is randomly selected to verify the method. The results show that the method proposed can automatically extract explicable test scene primitives with clear physical meanings from the naturalistic driving dataset without setting parameters， laying a good foundation for scene-based autonomous vehicle testing.

Key words: autonomous vehicle testing, scene primitives extraction, naturalistic driving dataset, hidden Markov model