The field of autonomous vehicle navigation is moving towards the development of more integrated and adaptive frameworks that can address the complexities of dynamic and interactive driving. Researchers are focusing on creating systems that can navigate non-linear road geometries, handle dynamic obstacles, and meet stringent safety and comfort requirements. Noteworthy papers in this area include:

• A novel approach to autonomous vehicle navigation that integrates artificial potential fields, Frenet coordinates, and improved particle swarm optimization, which has been validated through extensive simulations and real-world scenarios.
• An extended horizon tactical decision-making approach for automated driving based on Monte Carlo Tree Search, which enables long-term, real-time decision-making and significantly enhances planning robustness and decision efficiency.