The recent developments in the research area of autonomous systems and robotics have shown a significant shift towards more adaptive, efficient, and real-time capable planning and control strategies. A common theme across the latest research is the integration of advanced optimization techniques with data-driven approaches to enhance the performance and safety of autonomous operations. Notably, there is a strong emphasis on contingency planning, where systems are designed to handle sudden changes and unforeseen events, ensuring robustness and reliability. Additionally, the field is witnessing innovations in motion planning, with methods that combine geometric and probabilistic approaches to achieve smoother and more efficient trajectories, even in complex environments. Reinforcement learning is also making strides, with new algorithms that promise monotonic performance enhancements, critical for the evolution of automated driving systems. Furthermore, the use of Monte Carlo Tree Search with spectral expansion is proving to be a powerful tool for real-time planning in continuous dynamical systems, offering solutions that were previously unattainable. These advancements collectively push the boundaries of what autonomous systems can achieve, making them more capable, efficient, and adaptable to a wide range of real-world scenarios.