Abstract: To solve the problem that the cyclist was vulnerable in the traffic accident, a trajectory prediction method was proposed based on the intention identification of cyclist. The diverse characteristics of cyclists were extracted from the perspective of cyclists and vehicles, such as the direction of movement, the probability of returning and the relative position of the vehicle, and the dynamic Bayesian model network was established under the intersection scenario. The influencing factor of cyclist intention was analyzed to obtain the probability of cyclist intention. According to the results of intention identification, the relevant traffic scenarios were built, and the cycling motion equation was given. Through the cycling trajectory prediction algorithm based on particle filtering methods, combined with the motion equation and observation equation, the future trajectory of the cyclist was simulated with particles simulation set. The data collection hardware platform was set up based on laser radar and monocular cameras to obtain the information related to the cyclist movement and posture. Based on the collected 7 358 frame data, the length of the cyclist entering the intersection area was calculated to evaluate the algorithm. The results show that the proposed algorithm can basically identify the intention of the cyclist 0.24-0.54 s before the riding person crossing the street and can predict the rider′s track set of future 5.0 s. The algorithm is of great significance for improving road security.