The field of autonomous vehicle path planning is moving towards more dynamic and adaptive approaches, with a focus on real-time risk assessment and obstacle avoidance. Recent developments have seen the integration of machine learning and computer vision techniques to improve the accuracy and efficiency of path planning algorithms. Notable advancements include the use of probabilistic visibility volumes and iterative deepening A* search to account for occlusions and sensor constraints in urban environments. Additionally, the development of novel optimization strategies, such as quantum tunneling and bio-phototactic driven methods, has shown promise in improving the performance of metaheuristic algorithms in complex scenarios. Noteworthy papers include: ARGUS, which presents a framework for risk-aware path planning in tactical UGV operations, demonstrating its effectiveness in a practical exercise with the Portuguese Army. Occlusion-Aware Ground Target Search by a UAV in an Urban Environment, which proposes a search strategy that exploits a probabilistic visibility volume to plan its future motion. A Quantum Tunneling and Bio-Phototactic Driven Enhanced Dwarf Mongoose Optimizer for UAV Trajectory Planning, which introduces an Enhanced Multi-Strategy Dwarf Mongoose Optimization algorithm that outperforms advanced algorithms in convergence speed and optimization accuracy.