Prediction of freeway self-driving traffic flow based on bidirectional GRU recurrent neural network

Engineering notes

Key topics: self-driving, prediction. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

This paper uses the Bi-directional Gated Recurrent Unit(BI-GRU) recurrent neural network, combined with the historical data of the high-speed toll station entrances and exits at different time nodes on weekdays, weekends and holidays, to predict the traffic flow of vehicles entering the province and reaching key tourist cities, and realize the expressway in Gansu Province. It can be seen from the experimental results that in a larger time and space range, BI-GRU has improved prediction accuracy compared with standard Gated Recurrent Unit (GRU) and Long short-term memory (LSTM), and its prediction ability for data with large fluctuations and peak data is more prominent.

Links and sources

• Official / arXiv page

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