Abstract:

The autonomous driving perception system must perceive the movement of the target vehicle to make reasonable interactive decisions. For the time lag in behavior perception， as well as the problem that possible fluctuations and outliers in the data lead to poor perception accuracy， an online semi-supervised hybrid approach is proposed in this paper. Firstly， a data-driven online prediction algorithm for vehicle motion state is designed using autoregressive integral moving average and online gradient descent optimizer. Then， an initial model based on micro-clusters is constructed， and an ensemble learning strategy is established using K nearest neighbor as the base classifier. Error-driven representative learning and exponential decay strategies are designed to achieve iterative updates of the initial model. Finally， experimental data to verify the effectiveness of the proposed algorithm is collected based on the driving simulation platform. The results show that the proposed method has rapid adaptability to vehicle behavior fluctuations. The online prediction algorithm can accurately predict vehicle motion trends， and the behavior perception algorithm has strong adaptability to vehicle behavior at different prediction times.

Key words: autonomous driving, behavior prediction, autoregressive integral moving average, ensemble learning, semi-supervised learning