# FRENETIX: A High-Performance and Modular Motion Planning Framework for Autonomous Driving arXiv: https://arxiv.org/abs/2402.01443 ยท HTML: https://mlsanity.com/paper/2402.01443/ ## Key facts - Cited by 14 papers in the map (data through 14 Oct 2026). - Placed in Imitation-learning end-to-end planners, one of 9 directions in End-to-end autonomous driving. - Builds on 11 of its 59 references that are in the map. - First posted on arXiv 2 Feb 2024. ## Placed in - End-to-end autonomous driving / [Imitation-learning end-to-end planners](https://mlsanity.com/driving/imitation-learning-end-to-end-planners/) ## Builds on (most cited first, 11 of 11) - [End to End Learning for Self-Driving Cars](https://mlsanity.com/paper/1604.07316/) (2016) - [A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehicles](https://mlsanity.com/paper/1604.07446/) (2016) - [Robot Operating System 2: Design, Architecture, and Uses In The Wild](https://mlsanity.com/paper/2211.07752/) (2022) - [A Survey of End-to-End Driving: Architectures and Training Methods](https://mlsanity.com/paper/2003.06404/) (2020) - [Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives](https://mlsanity.com/paper/2303.09824/) (2023) - [Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles](https://mlsanity.com/paper/2001.11231/) (2020) - [Learning Sampling Distributions for Robot Motion Planning](https://mlsanity.com/paper/1709.05448/) (2017) - [Multilayer Graph-Based Trajectory Planning for Race Vehicles in Dynamic Scenarios](https://mlsanity.com/paper/2005.08664/) (2020) - [A review of mobile robot motion planning methods: from classical motion planning workflows to reinforcement learning-based architectures](https://mlsanity.com/paper/2108.13619/) (2021) - [EDGAR: An Autonomous Driving Research Platform -- From Feature Development to Real-World Application](https://mlsanity.com/paper/2309.15492/) (2023) - [Integration of Reinforcement Learning Based Behavior Planning With Sampling Based Motion Planning for Automated Driving](https://mlsanity.com/paper/2304.08280/) (2023) ## Built on by (most cited first, 14 of 14) - [Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysis](https://mlsanity.com/paper/2506.11526/) (2025) - [From Words to Collisions: LLM-Guided Evaluation and Adversarial Generation of Safety-Critical Driving Scenarios](https://mlsanity.com/paper/2502.02145/) (2025) - [DualAD: Dual-Layer Planning for Reasoning in Autonomous Driving](https://mlsanity.com/paper/2409.18053/) (2024) - [StyleVLA: Driving Style-Aware Vision Language Action Model for Autonomous Driving](https://mlsanity.com/paper/2603.09482/) (2026) - [From Shadows to Safety: Occlusion Tracking and Risk Mitigation for Urban Autonomous Driving](https://mlsanity.com/paper/2504.01408/) (2025) - [Pedestrian-Aware Motion Planning for Autonomous Driving in Complex Urban Scenarios](https://mlsanity.com/paper/2504.01409/) (2025) - [EgoDyn-Bench: Evaluating Ego-Motion Understanding in Vision-Centric Foundation Models for Autonomous Driving](https://mlsanity.com/paper/2604.22851/) (2026) - [PlannerForge: LLM Agents for Scenario-Based Testing of Motion Planners in Autonomous Driving](https://mlsanity.com/paper/2609.08965/) (2026) - [MultiDrive: A Co-Simulation Framework Bridging 2D and 3D Driving Simulation for AV Software Validation](https://mlsanity.com/paper/2505.13959/) (2025) - [Momentum-constrained Hybrid Heuristic Trajectory Optimization Framework with Residual-enhanced DRL for Visually Impaired Scenarios](https://mlsanity.com/paper/2509.15582/) (2025) - [Learning to Sample: Reinforcement Learning-Guided Sampling for Autonomous Vehicle Motion Planning](https://mlsanity.com/paper/2509.24313/) (2025) - [Path Diffuser: Diffusion Model for Data-Driven Traffic Simulator](https://mlsanity.com/paper/2509.24995/) (2025) - [Towards Safe Autonomous Driving: A Real-Time Motion Planning Algorithm on Embedded Hardware](https://mlsanity.com/paper/2601.03904/) (2026) - [Declarative Scenario-based Testing with RoadLogic](https://mlsanity.com/paper/2603.09455/) (2026) ## Abstract (arXiv) 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.