## 4 Synthesis and Summary
### Future Directions
#### 4.4.6 Navigation Safety and Military Defense  
The future of USVs holds great promise in enhancing security and defense capabilities, particularly in the military domain. This emphasis on safety and self-defense features underscores the growing importance of USVs in modern maritime operations.  
In terms of navigation safety, object detection comes forth and trajectory planning follows in tune. The incorporation of a multi-scale feature extraction layer, encompassing dilation convolution and group convolution, into the baseline model of the Faster Region-based Convolutional Neural Network (Faster-RCNN), accompanied by modifications to the classification algorithm, was suggested. This enhancement aimed at augmenting the model's capability for obstacle detection, with a specific focus on bolstering its robustness and precision [106]. A fusion framework of field theoretical planning and a model predictive control (MPC) algorithm were proposed to obtain a realizable collision-free tracking trajectory to enhance safety, where the trajectory smoothness and collision avoidance constraints under a complex environment needed to be considered [107]. A new process of dynamic collision avoidance, combined with new attractive and repulsive potential field functions, was constructed to ensure the safety of USVs [108]. An innovative collision avoidance algorithm, predicated on Approximate Representation Reinforcement Learning (AR-RL), was developed to facilitate the collision avoidance capabilities of Maritime Autonomous Surface Ships (MASS) within a continuum state space. This algorithm was characterized by an interactive learning feature, mirroring the decision-making process of a human crew in navigational contexts. Its effectiveness has been established in significantly enhancing maritime safety, particularly in scenarios where mixed traffic consists of both manned vessels and MASS, promising substantial improvements in nautical safety in the foreseeable future [109]. In response to the critical requirement for assessing human error probability in terms of autonomous cargo ships with human-autonomy collaboration, a probabilistic model mixed with the Shore Control Centre (SCC), Technique for Human Error Rate Prediction (THERP)and Bayesian Networks was proposed, and was employed to evaluate the likelihood of human errors, specifically concentrating on the interaction between human operators and autonomous systems [110].  
From the point of defense, the new technology of USVs mainly casts light on derivative techniques, combat theories and methods of arrangement in military scenarios. Multiple object tracking (MOT) in USV videos has many application scenarios in the military and civilian fields [111]. By projecting military power in a more affordable way, through the use of USVs, the exposure of human life to military risks should be significantly reduced [112]. A new attack strategy, USV combat based on wolves' attacks with weight, was testified to have conspicuous advantages in USV combat. In the future naval deployment of USVs, it is also proposed that, for the initial stages of exploration and investigation, the application of a queuing network theory was deemed more advantageous than relying on simulation-based analysis [112].  
As is listed above, there are several hot research directions in this prediction, such as Enhanced Safety, Anti-Jamming Communication, Automatic Missile Defense Systems, Surveillance and Reconnaissance, Autonomous Swarming and Cost-Effective Alternatives.