A Survey on Hybrid Motion Planning Methods for Automated Driving Systems
On the map: End-to-end autonomous driving → Imitation-learning end-to-end planners
Abstract (arXiv)
Motion planning is an essential element of the modular architecture of autonomous vehicles, serving as a bridge between upstream perception modules and downstream low-level control signals. Traditional motion planners were initially designed for specific Automated Driving Functions (ADFs), yet the evolving landscape of highly automated driving systems (ADS) requires motion for a wide range of ADFs, including unforeseen ones. This need has motivated the development of the ``hybrid" approach in the literature, seeking to enhance motion planning performance by combining diverse techniques, such as data-driven (learning-based) and logic-driven (analytic) methodologies. Recent research endeavours have significantly contributed to the development of more efficient, accurate, and safe hybrid methods for Tactical Decision Making (TDM) and Trajectory Generation (TG), as well as integrating these algorithms into the motion planning module. Owing to the extensive variety and potential of hybrid methods, a timely and comprehensive review of the current literature is undertaken in this survey article. We classify the hybrid motion planners based on the types of components they incorporate, such as combinations of sampling-based with optimization-based/learning-based motion planners. The comparison of different classes is conducted by evaluating the addressed challenges and limitations, as well as assessing whether they focus on TG and/or TDM. We hope this approach will enable the researchers in this field to gain in-depth insights into the identification of current trends in hybrid motion planning and shed light on promising areas for future research.
Key facts. Cited by 1 paper 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 25 of its 111 references that are in the map. First posted on arXiv 8 Jun 2024.
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25 of 111 references.
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Other references · 24
- 2016End to End Learning for Self-Driving Carscited by 1,009
- 2016A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehiclescited by 254
- 2023Motion Planning for Autonomous Driving: The State of the Art and Future Perspectivescited by 114
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- 2022Reducing Collision Checking for Sampling-Based Motion Planning Using Graph Neural Networkscited by 17
12 more
- 2019Safe Trajectory Generation for Complex Urban Environments Using Spatio-temporal Semantic Corridorcited by 16
- 2020Learning hierarchical behavior and motion planning for autonomous drivingcited by 15
- 2023Milestones in Autonomous Driving and Intelligent Vehicles Part II: Perception and Planningcited by 9
- 2019Quadratic Q-network for Learning Continuous Control for Autonomous Vehiclescited by 8
- 2020Imitation Learning of Hierarchical Driving Model: from Continuous Intention to Continuous Trajectorycited by 8
- 2023Occupancy Prediction-Guided Neural Planner for Autonomous Drivingcited by 8
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Citing papers per year: 2025: 1.
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- 2024Towards A General-Purpose Motion Planning for Autonomous Vehicles Using Fluid Dynamics
- 2025A Survey of Reinforcement Learning-Based Motion Planning for Autonomous Driving: Lessons Learned from a Driving Task Perspective
- 2024Integrating Decision-Making Into Differentiable Optimization Guided Learning for End-to-End Planning of Autonomous Vehicles
- 2024A Reinforcement Learning-Boosted Motion Planning Framework: Comprehensive Generalization Performance in Autonomous Driving
- 2021How To Not Drive: Learning Driving Constraints from Demonstration