Frenetix Motion Planner: High-Performance and Modular Trajectory Planning Algorithm for Complex Autonomous Driving Scenarios

Our work aims to present a high-performance and modular sampling-based trajectory planning algorithm for autonomous vehicles. This algorithm is tailored to address the complex challenges in solution space construction and optimization problem formulation within the path planning domain. 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. This algorithm was then 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. While our study presents promising results, it's important to note that our assessments have been conducted exclusively in simulated environments, and real-world testing is required to fully validate our findings. 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.

Overcoming Blind Spots: Occlusion Considerations for Improved Autonomous Driving Safety

Our work introduces a module for assessing the trajectory safety of autonomous vehicles in dynamic environments marked by high uncertainty. We focus on occluded areas and occluded traffic participants with limited information about surrounding obstacles. To address this problem, we propose a software module that handles blind spots (BS) created by static and dynamic obstacles in urban environments. We identify potential occluded traffic participants, predict their movement, and assess the ego vehicle's trajectory using various criticality metrics. The method offers a straightforward and modular integration into motion planner algorithms. We present critical real-world scenarios to evaluate our module and apply our approach to a publicly available trajectory planning algorithm. Our results demonstrate that safe yet efficient driving with occluded road users can be achieved by incorporating safety assessments into the planning process. The code used in this research is publicly available as open-source software and can be accessed at the following link: https://github.com/TUM-AVS/Frenetix-Occlusion.