# A Survey on Hybrid Motion Planning Methods for Automated Driving Systems

arXiv: https://arxiv.org/abs/2406.05575 · HTML: https://mlsanity.com/paper/2406.05575/

## Key facts

- Cited by 1 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 25 of its 111 references that are in the map.
- First posted on arXiv 8 Jun 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, 25 of 25)

- [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)
- [Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives](https://mlsanity.com/paper/2303.09824/) (2023)
- [Cooperative Perception for 3D Object Detection in Driving Scenarios using Infrastructure Sensors](https://mlsanity.com/paper/1912.12147/) (2019)
- [Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles](https://mlsanity.com/paper/2001.11231/) (2020)
- [Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys](https://mlsanity.com/paper/2303.17220/) (2023)
- [Differentiable Integrated Motion Prediction and Planning with Learnable Cost Function for Autonomous Driving](https://mlsanity.com/paper/2207.10422/) (2022)
- [Motion Planning Networks: Bridging the Gap Between Learning-based and Classical Motion Planners](https://mlsanity.com/paper/1907.06013/) (2019)
- [Occupancy Flow Fields for Motion Forecasting in Autonomous Driving](https://mlsanity.com/paper/2203.03875/) (2022)
- [Human-Like Decision Making for Autonomous Driving: A Noncooperative Game Theoretic Approach](https://mlsanity.com/paper/2005.11064/) (2020)
- [The Integration of Prediction and Planning in Deep Learning Automated Driving Systems: A Review](https://mlsanity.com/paper/2308.05731/) (2023)
- [A Fast Integrated Planning and Control Framework for Autonomous Driving via Imitation Learning](https://mlsanity.com/paper/1707.02515/) (2017)
- [Reducing Collision Checking for Sampling-Based Motion Planning Using Graph Neural Networks](https://mlsanity.com/paper/2210.08864/) (2022)
- [Safe Trajectory Generation for Complex Urban Environments Using Spatio-temporal Semantic Corridor](https://mlsanity.com/paper/1906.09788/) (2019)
- [Learning hierarchical behavior and motion planning for autonomous driving](https://mlsanity.com/paper/2005.03863/) (2020)
- [Milestones in Autonomous Driving and Intelligent Vehicles Part II: Perception and Planning](https://mlsanity.com/paper/2306.01980/) (2023)
- [Quadratic Q-network for Learning Continuous Control for Autonomous Vehicles](https://mlsanity.com/paper/1912.00074/) (2019)
- [Imitation Learning of Hierarchical Driving Model: from Continuous Intention to Continuous Trajectory](https://mlsanity.com/paper/2010.10393/) (2020)
- [Occupancy Prediction-Guided Neural Planner for Autonomous Driving](https://mlsanity.com/paper/2305.03303/) (2023)
- [LIDAR-based Driving Path Generation Using Fully Convolutional Neural Networks](https://mlsanity.com/paper/1703.08987/) (2017)
- [DeepGoal: Learning to Drive with driving intention from Human Control Demonstration](https://mlsanity.com/paper/1911.12610/) (2019)
- [Spatiotemporal motion planning with combinatorial reasoning for autonomous driving](https://mlsanity.com/paper/2207.04418/) (2022)
- [GET-DIPP: Graph-Embedded Transformer for Differentiable Integrated Prediction and Planning](https://mlsanity.com/paper/2211.06031/) (2022)
- [A Novel Deep Neural Network for Trajectory Prediction in Automated Vehicles Using Velocity Vector Field](https://mlsanity.com/paper/2309.10948/) (2023)
- [Synchronous Maneuver Searching and Trajectory Planning for Autonomous Vehicles in Dynamic Traffic Environments](https://mlsanity.com/paper/1909.07678/) (2019)

## Built on by (most cited first, 1 of 1)

- [Knowledge Integration Strategies in Autonomous Vehicle Prediction and Planning: A Comprehensive Survey](https://mlsanity.com/paper/2502.10477/) (2025)

## 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.