Deep learning technology can significantly enhance the target recognition and attack decision-making capabilities of unmanned ground vehicles (UGVs), but its application requires comprehensive consideration of technical feasibility, ethics and practical challenges. The following is a detailed analysis： --- ### **1. Enhancement of target recognition ability** The core advantage of deep learning in target recognition lies in the ability to process complex visual and unstructured data： -Sensor fusion**: UGVs are usually equipped with multi-modal sensors such as cameras, infrared sensors, and LiDAR. Deep learning models (such as convolutional neural networks CNN and Transformer) can integrate these data to improve the accuracy of target detection. For example, YOLOv8 or Faster R-CNN can be used to detect enemy vehicles or personnel in real time, while point cloud data can be processed through PointNet. -**Complex environmental adaptability**: Through adversarial training or domain adaptive techniques (such as CycleGAN), the model can adapt to changes in light, blocking or bad weather (such as rain and fog), reducing the false detection rate. -**Time series data processing**: Combined with recurrent neural networks (RNN) or time convolutional networks (TCN), UGV can track dynamic target trajectories and predict movement trends. **Case **: The U.S. Army project "Project Maven" uses CNN to analyze drone video streams, and similar technologies can be migrated to UGV for battlefield target recognition. --- ### **2. Intelligence of attack decision-making** Deep reinforcement learning (DRL) and multi-agent collaboration are the key to improving decision-making ability： -**Enhanced learning framework**: UGV can train DRL models (such as PPO, A3C) in simulation environments (such as CARLA, Gazebo) to learn optimal attack strategies. For example, balance the attack success rate and one's own survival rate in a simulated confrontation. -**Multi-objective optimization**: Through multitasking learning or hierarchical reinforcement learning, UGV can simultaneously handle path planning, threat assessment, and attack timing selection. For example, use DQN to select high-value targets while avoiding friendly forces. - **Human-computer collaboration**: Using semi-supervised learning or Human-in-the-Loop mechanism, human operators can correct AI decisions in real time to ensure ethical compliance. **Case **: Part of the decision-making module of the Israeli “Iron Dome” system adopts AI-assisted threat prioritization, and similar logic can be applied to UGV. --- ### **3. Key challenges and limitations** -**Real-time constraints**: Edge computing devices (such as NVIDIA Jetson) need to deploy lightweight models (such as MobileNet, EconomentNet), combining model pruning and quantification techniques to reduce latency. -**Confrontation attack risk**: Confrontation samples may mislead the target recognition system, and the robustness needs to be improved through confrontation training or certified robustness. -**Data scarcity**: The real data of military scenarios is limited, which can be solved by synthetic data generation (such as using Unity to simulate the battlefield environment) or migration learning. -**Ethics and compliance**: Autonomous attacks involve international law (such as the Convention on Lethal Autonomous Weapon Systems), and a “human veto” mechanism needs to be designed to ensure that the final decision is controlled by humans. --- ### **4. Application scenario** -**Military field**: Border patrol, EOD, precision strikes on high-value targets. -**Security field**: Rapid threat identification and response in counter-terrorism scenarios. -**Civilian field**: Identification of trapped persons (non-attack scenarios) in disaster relief, or abnormal detection in industrial inspections. --- ### **5. conclusion** Deep learning can significantly improve the target recognition accuracy and decision-making efficiency of UGV, but it needs to solve real-time, robustness and ethical issues. Future directions include： -Algorithm optimization**: Develop low-power, high-precision edge AI models. -**Alignment of simulation with the real world**: Close the gap through intensive learning simulation to reality (Sim2Real) technology. -**Interdisciplinary collaboration**: Unite technical experts, ethicists, and policymakers to ensure technical compliance. In the end, the intelligence of UGV needs to strike a balance between technological innovation and ethical constraints in order to achieve reliable application in complex environments.