Robotic laparoscopic surgery is moving from conventional teleoperation toward greater autonomy. Previous studies usually only examined individual parts of this transition. The technologies enabling autonomous laparoscopic surgery are less often discussed as one integrated system. This review examines the system through three components: hardware infrastructure, environmental perception, and decision-making. The hardware discussion covers instrument actuation, vision and force sensing, and various robotic platforms. The perception section follows proprioceptive, visual and force data as they are converted into a unified representation of the surgical state. It focuses on 3D scene reconstruction, instrument segmentation, workflow recognition, and multimodal fusion. The decision and control section discusses classical control and data-driven methods, especially the hierarchical imitation learning and reinforcement learning for long-horizon surgical tasks. The review concludes with the main barriers to clinical translation, including the sim-to-real gap, deformable tissue modeling, and safety and regulatory requirements. These areas together show how embodied intelligence may influence future autonomous surgery in the operating room.

Convolutional Neural Networks (CNN) are not widely used in high-risk decision-making because they have black-box nature. This paper investigates three visualization methods, Gradient-weighted class activation mapping (Grad-CAM), Score-weighted visual explanations for convolutional neural networks (Score-CAM), and Local interpretable model-agnostic explanations (LIME), across classification, detection, and medical imaging tasks. Theoretically, Grad-CAM relies on gradient backpropagation to weight feature maps. This offers computational efficiency but suffers from gradient instability and coarse spatial resolution. Score-CAM replaces gradients with forward pass confidence scores. This enhances robustness and spatial alignment at the cost of linearly increasing inference overhead. LIME is a model agnostic surrogate. It provides flexible local explanations but is inherently sensitive to perturbation sampling and hyperparameter choices, leading to poor reproducibility. Grad-CAM performs best in dermatologist agreement and localization accuracy. Score-CAM show superior spatial alignment in pneumonia detection with gradient free robustness. LIME shows flexibility but suffers from instability and hyperparameter sensitivity. The differences are obvious: Grad-CAM is good at interpretability, Score-CAM is good at robustness with computational cost, and LIME is good at model agnostic applicability. Based on these differences, these three visualization tools can be selected for real-world tasks.

Damaged sensor data, often caused by noise interference and data loss, impairs the decision-making accuracy of Deep Reinforcement Learning (DRL) traffic signal control models. This paper takes RobustLight (ICML 2025) as the core case and analyzes it from a data science perspective. We introduce DRL-based traffic signal control and diffusion model principles, then examine RobustLight's dual-process framework, DSI algorithm, Denoise and Repaint modules, and its non-Markov loss design. Experiments on Jinan, Hangzhou and New York datasets under four adversarial attacks and sensor damage show that RobustLight achieves up to 50.43% improvement in state recovery, with ATT reduced by up to 42%. Finally, we discuss real-world challenges including anomaly-detection dependency, inference latency, and cross-domain generalization.