# Design of an Intelligent Vehicle Behavior Decision Algorithm Based on DGAIL
## 5 Conclusions  
Relying on relevant scientific research projects, this paper treated intelligent vehicles as the research object and analyzed the decision-making method for vehicle driving behavior. First, the development and related algorithms of intelligent vehicle behavior decision-making were introduced. Second, the DQN algorithm was used to optimize the GAIL algorithm, and the DGAIL algorithm was proposed. In addition, a vehicle decision-making model based on DGAIL was constructed, including the design of the state space, design of the network structure and design of the training parameters. Last, in a typical scenario, the training and verification in the simulation scenario were completed according to the vehicle decision model. The results show that the algorithm meets the requirements of intelligent vehicles for decision-making systems. Core innovations include the following:  
Figure 14: Comparison of two methods’ motion curve for the merging road.  
A DGAIL-based intelligent vehicle driving behavior decision-making method, which can realize real-time, reliable, and stable decision-making in traffic scenarios based on structured roads, is proposed. Compared with the deep reinforcement learning DQN method, the tedious design of the reward value function is omitted. Compared with the traditional GAIL method, the DGAIL method is more suitable for scenes where the action space is discrete, the training convergence is faster, and the stability is higher.
2. Presently, most research on intelligent vehicle driving behavior decision-making is mainly aimed at straight roads and intersections. In this paper, by constructing merging and roundabout scenarios in the simulation environment, the research scenarios of intelligent vehicles are enriched, and the decision-making of intelligent vehicle driving behavior can be more comprehensively verified. The applicability of the method accelerates the research process of a deep reinforcement learning algorithm in driving behavior decision-making.
3. By proposing the DGAIL method, the effectiveness of generative adversarial imitation learning in structured scenarios is evaluated and then trained and validated in traffic simulation scenarios. However, there are still some deficiencies in this paper in some aspects. Further research and exploration can be carried out from the following aspects:
4. The complexity of the scene: Although the scene in this paper included straight road and merging scenes, there were still certain constraints on the environmental vehicles in it, and the complexity was relatively simple. The driving behavior of smart cars in more free scenes can be explored in future studies.
5. Optimization of the DGAIL algorithm: Although the DGAIL algorithm omits the design process of the reward value function, its training results have no obvious advantages over the traditional DQN algorithm. In follow-up research, the structure of the DGAIL algorithm can be optimized to improve the effectiveness and stability of the algorithm.
6. Real vehicle test: The research in this paper was completely based on simulation and is still far from real practical use. In future studies, the algorithm in the simulation can be extended to real scenes through transfer learning, and the effectiveness of the method can be further demonstrated from an actual environment.  
Conceptualization, B.R. and Y.R.; methodology, J.J. and Y.R.; software, P.L.; validation, Y.R. and B.R.; formal analysis, J.J., Y.R. and B.R.; resources, J.J. and Y.R.; data curation, P.L.; investigation, P.L.; writing--original draft preparation, J.J. and Y.R.; writing--review and editing, J.J. and P.L.; visualization, P.L.; supervision, Y.R. and B.R.; project administration, Y.R.; funding acquisition, Y.R. and B.R. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key R&D Program of Shandong Province, China (grant no. 2020 CXGC010118), the National Natural Science Foundation of China grant no. 41971342).  
The authors declare no conflict of interest.