# Autonomous Driving Decision-Making Method Based on Spatial-Temporal Fusion Trajectory Prediction
###### Abstract  
Due to the challenge that the behavior of traffic participants in the driving environment is highly stochastic and uncertain, it is difficult for self-driving vehicles to make accurate decisions based only on the current environmental state. In this paper, we propose a driving strategy learning method based on spatial-temporal feature prediction. Firstly, the spatial interaction between vehicles is implicitly modeled using a graph convolutional neural network and multi-head attention mechanism, and the gated loop unit is embedded to capture the sequential temporal relationship to establish a prediction model incorporating spatial-temporal features. Then, a reinforcement learning-based driving strategy method is constructed using some of the predictive features of the ego-vehicle and surrounding vehicles as predictive state inputs. Finally, based on the real dataset and CARLA simulation platform, the prediction ability of the prediction model and the effectiveness of the prediction-based decision-making model are verified. The simulation results prove that the prediction algorithm can achieve the minimum error compared with the baseline trajectory prediction algorithm, and effectively improves the accuracy and reliability of the autopilot decision-making in various dynamic scenarios. intelligent decision-making; trajectory prediction; graph convolutional neural networks; reinforcement learning 2020  
(LSTM) network, and adds a hybrid density network layer to the original encoder-decoder structure to represent the future position of the vehicle using probability distributions, which improves the accuracy of trajectory prediction, but this method is only applicable to conventional scenarios such as highways. The literature [7] enriches the information of traffic participants in the driving scenario expressed by using a graph structure and especially takes features such as pedestrians and obstacles into account for trajectory prediction. The literature [8] proposes a graph-based spatial-temporal convolutional network, which uses a graph convolutional neural network to capture the temporal features and a convolutional neural network to capture the spatial features, and successfully predicts the distribution of future trajectories of neighboring vehicles. However, trajectory prediction is a spatial-temporal task. How to fully utilize these features and improve prediction accuracy remains a key issue. There are two kinds of existing autonomous driving decision-making methods: rule-based and learning-based [9]. Rule-based methods rely on a large number of manually formulated rules to impose safe and controllable behaviors on the vehicle; however, real environments contain a large number of emergent conditions, and the manual rules often cannot fully cover all working conditions. Learning-based methods rely on a large amount of data for training mainly through neural networks, which can overcome the disadvantage of poor environmental adaptability to a certain extent and have now become the mainstream algorithms for solving the decision-making problem of autonomous driving. Learning-based methods are primarily categorized into imitation learning (IL) [10] and reinforcement learning (RL) [11]. Imitation learning relies on a large amount of experts' experience data for learning and lacks the ability to actively explore unseen scenes. On the other hand, reinforcement learning optimizes driving performance through interaction with the environment and repeated trial and error, thus it has stronger scene adaptation and generalization [12]. The literature [13] models self-driving vehicle following scenarios as a classical Markov decision process and solves the optimal following policy for this model using the Q-learning algorithm. The literature [14] proposes a Q-value estimation method based on a weighted average of sample variance, which effectively improves the Q-value underestimation problem of the twin delayed deep deterministic policy gradient (TD3) algorithm.