The field of autonomous systems is witnessing significant developments in simulation and navigation technologies. Researchers are focusing on creating more realistic and controllable simulation environments to test and evaluate autonomous vehicles and drones. This includes the development of new frameworks and methods that can generate statistically realistic traffic scenes and simulate complex scenarios. Additionally, there is a growing emphasis on enabling safe and autonomous navigation of small-scale drones in partially known environments. Innovative approaches, such as AI-aided vision-based reactive planning and closed-loop reinforcement learning fine-tuning, are being explored to address the challenges of autonomous navigation. These advancements have the potential to significantly improve the performance and safety of autonomous systems. Noteworthy papers include: RADE, which proposes a simulation framework for generating risk-adjustable traffic scenes, and RIFT, which introduces a closed-loop RL fine-tuning strategy for realistic and controllable traffic simulation. AI and Vision based Autonomous Navigation of Nano-Drones in Partially-Known Environments is also a notable work, presenting a novel AI-aided vision-based reactive planning method for obstacle avoidance in nano-drones.