摘要：

现有的交通环岛通行优化研究多以无损通信为背景，结合车辆速度等基础数据设计协同策略，忽略了影响交通策略的外界环境等数据，无法满足实际应用的需要。为解决实际通信受限问题，根据智能网联汽车的车-路-环境协同特点，提出一种结合用户画像，基于车辆状态预测的环岛车辆协同换道策略。设计基于时空特征的车辆预测方法AP-LSTM，捕捉车辆关键时空特征以实现小样本轨迹预测，有效提高小样本车辆轨迹预测的准确性和实时性。同时，设计基于预测机制的车辆协同算法PMC，弥补车辆协同决策在实时通信受限的条件下所缺失的车辆状态信息，通过历史数据对车辆未来状态进行预测，在此基础上，结合Stackelberg博弈对交通环岛路口处的车辆进行协同控制。在SUMO平台上的实验结果表明，相比长短时记忆算法，所提AP-LSTM预测方法的均方根误差较低，相比SUMO算法，所提PMC协同算法的加速度标准差降低51.7%，且平均速度提高3.0%，有效提高交通环岛的通行效率和驾驶平稳性。

关键词: 智能网联汽车, 车辆画像, 状态预测, Stackelberg博弈, 环岛通行

Abstract:

The existing research on roundabout traffic optimization is mostly based on lossless communication and combines fundamental data, such as vehicle speed, to design collaborative strategies.This approach overlooks data such as the external environment that affects traffic strategies and cannot meet the needs of practical applications.To solve the problem of practical communication constraints, a collaborative lane-change strategy for vehicles around the roundabout based on vehicle state prediction combined with user portraits is proposed.This strategy takes into account the characteristics of vehicle-road-environment collaboration of intelligent connected vehicle.A vehicle prediction method based on spatiotemporal features AP-LSTM, is designed to capture key spatiotemporal features of vehicles to achieve small sample trajectory prediction, effectively improving the accuracy and real-time performance of small sample vehicle trajectory prediction.A predictive mechanism-based vehicle collaboration algorithm PMC is also designed to compensate for the missing vehicle status information in vehicle collaboration decision-making under real-time communication constraints.The future state of vehicles is predicted based on historical data. On this basis, collaborative vehicle control at roundabouts is performed in combination with the Stackelberg game.Simulation experiments are conducted on the SUMO platform.The results show that the proposed AP-LSTM prediction algorithm has a lower Root Mean Square Error(RMSE) value than the Long Short-Term Memory (LSTM) algorithm.At the same time, compared to the SUMO algorithm, the proposed PMC collaborative algorithm has a 51.7% reduction in acceleration standard deviation and an average speed increase of 3.0%, effectively improving the traffic efficiency and driving stability of roundabout traffic.

Key words: intelligent connected vehicle, vehicle portrait, state prediction, Stackelberg game, roundabout traffic