To improve the trajectory prediction performance of human-driven vehicles in mixed traffic flow, we propose a novel interaction-aware network framework based on mixed teacher forcing GRU (Gate Recurrent Unit). Firstly, we filter and normalize the vehicle trajectory, divide it into three categories (left lane change, lane keeping, and right lane change), and build a trajectory prediction dataset. Then, we encode the historical trajectory of the target vehicle and the information about surrounding vehicles into the context vector. Next, we decode the content vector into future trajectory by mixed teaching force mode. Finally, the model is verified on the real main road datasets NGSIM US101 and I-80 and compared with the state-of-the-art model. The experimental results show that the proposed model achieves the state-of-the-art accuracy. The code can be accessed at https://github.com/ColinFanghz/MTF-GRU.git.
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