  Our research introduces a modular motion planning framework for autonomous
vehicles using a sampling-based trajectory planning algorithm. This approach
effectively tackles the challenges of solution space construction and
optimization in path planning. The algorithm is applicable to both real
vehicles and simulations, offering a robust solution for complex autonomous
navigation. Our method employs a multi-objective optimization strategy for
efficient navigation in static and highly dynamic environments, focusing on
optimizing trajectory comfort, safety, and path precision. The algorithm is
used to analyze the algorithm performance and success rate in 1750 virtual
complex urban and highway scenarios. Our results demonstrate fast calculation
times (8ms for 800 trajectories), a high success rate in complex scenarios
(88%), and easy adaptability with different modules presented. The most
noticeable difference exhibited was the fast trajectory sampling, feasibility
check, and cost evaluation step across various trajectory counts. We
demonstrate the integration and execution of the framework on real vehicles by
evaluating deviations from the controller using a test track. This evaluation
highlights the algorithm's robustness and reliability, ensuring it meets the
stringent requirements of real-world autonomous driving scenarios. The code and
the additional modules used in this research are publicly available as
open-source software and can be accessed at the following link:
https://github.com/TUM-AVS/Frenetix-Motion-Planner.
