基于GA-TD3算法的交叉路口决策模型

为了解决交叉路口场景下无人驾驶决策模型成功率低，模型不稳定，车辆通行效率低的问题，从两个方面对TD3算法作出改进，提出了基于GA-TD3算法的交叉路口决策模型。首先引入记忆模块，使用GRU神经网络来提升决策模型的成功率；其次在状态空间引入社会注意力机制，更加关注与社会车辆的交互行为，保证模型稳定性的同时提升车辆的通行效率。采用CARLA仿真器进行20 000回合的模型训练后，TD3算法通过路口的成功率为92.4%，GA-TD3算法的成功率为97.6%，且车辆的通行时间缩短了3.36 s。GA-TD3算法模型在学习效率和通行效率上均有所提升，从而缓解城市中的交通压力，提高驾驶效率。

Intersection decision model based on GA-TD3 algorithm

Addressing problems such as low success rates, instability, and inefficient traffic flow in autonomous decision-making models at intersections, this study proposed enhancements to the TD3 algorithm through the GA-TD3(GRU attention twin delayed deep deterministic policy gradient) algorithm. Firstly, it introduced a memory module which using GRU neural network to improve the success rate of the decision model. Secondly, it introduced a social attention mechanism in the state space to focus on interactions with social vehicles. This mechanism ensured the stability of the model while improving the traffic efficiency of vehicles. After 20 000 rounds of training in the CARLA simulator, the TD3 algorithm achieves a success rate of 92.4%, while the success rate of the GA-TD3 algorithm is 97.6%. Additionally, the vehicle''s travel time is shortened by 3.36. GA-TD3 algorithm improves both learning efficiency and traffic efficiency, which can alleviate traffic pressure in urban scenes and improve driving efficiency.