# Search-based optimal motion planning for automated driving

arXiv: https://arxiv.org/abs/1803.04868 · HTML: https://mlsanity.com/paper/1803.04868/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2018.

## Built on by (most cited first, 24 of 24)

- [End-to-end Interpretable Neural Motion Planner](https://mlsanity.com/paper/2101.06679/) (2021)
- [MP3: A Unified Model to Map, Perceive, Predict and Plan](https://mlsanity.com/paper/2101.06806/) (2021)
- [Towards learning-based planning:The nuPlan benchmark for real-world autonomous driving](https://mlsanity.com/paper/2403.04133/) (2024)
- [LookOut: Diverse Multi-Future Prediction and Planning for Self-Driving](https://mlsanity.com/paper/2101.06547/) (2021)
- [SafetyNet: Safe planning for real-world self-driving vehicles using machine-learned policies](https://mlsanity.com/paper/2109.13602/) (2021)
- [A Survey of World Models for Autonomous Driving](https://mlsanity.com/paper/2501.11260/) (2025)
- [The Integration of Prediction and Planning in Deep Learning Automated Driving Systems: A Review](https://mlsanity.com/paper/2308.05731/) (2023)
- [Real-Time Trajectory Planning for Autonomous Driving with Gaussian Process and Incremental Refinement](https://mlsanity.com/paper/2205.11853/) (2022)
- [Planning with Adaptive World Models for Autonomous Driving](https://mlsanity.com/paper/2406.10714/) (2024)
- [Transcendental Idealism of Planner: Evaluating Perception from Planning Perspective for Autonomous Driving](https://mlsanity.com/paper/2306.07276/) (2023)
- [Autonomous Vehicle Parking in Dynamic Environments: An Integrated System with Prediction and Motion Planning](https://mlsanity.com/paper/2204.10383/) (2022)
- [Search-Based Task and Motion Planning for Hybrid Systems: Agile Autonomous Vehicles](https://mlsanity.com/paper/2301.10384/) (2023)
- [PP-TIL: Personalized Planning for Autonomous Driving with Instance-based Transfer Imitation Learning](https://mlsanity.com/paper/2407.18569/) (2024)
- [HYPE: Hybrid Planning with Ego Proposal-Conditioned Predictions](https://mlsanity.com/paper/2510.12733/) (2025)
- [Vehicle Type Specific Waypoint Generation](https://mlsanity.com/paper/2208.04987/) (2022)
- [A Multi-Heuristic Search-based Motion Planning for Automated Parking](https://mlsanity.com/paper/2307.07857/) (2023)
- [URA*: Uncertainty-aware Path Planning using Image-based Aerial-to-Ground Traversability Estimation for Off-road Environments](https://mlsanity.com/paper/2309.08814/) (2023)
- [Integrated Decision Making and Trajectory Planning for Autonomous Driving Under Multimodal Uncertainties: A Bayesian Game Approach](https://mlsanity.com/paper/2409.13993/) (2024)
- [Vision for Bosnia and Herzegovina in Artificial Intelligence Age: Global Trends, Potential Opportunities, Selected Use-cases and Realistic Goals](https://mlsanity.com/paper/2209.03990/) (2022)
- [Perception Imitation: Towards Synthesis-free Simulator for Autonomous Vehicles](https://mlsanity.com/paper/2304.09365/) (2023)
- [Synergizing Decision Making and Trajectory Planning Using Two-Stage Optimization for Autonomous Vehicles](https://mlsanity.com/paper/2411.18974/) (2024)
- [Scenario-Based Hierarchical Reinforcement Learning for Automated Driving Decision Making](https://mlsanity.com/paper/2506.23023/) (2025)
- [A Real-time Spatio-Temporal Trajectory Planner for Autonomous Vehicles with Semantic Graph Optimization](https://mlsanity.com/paper/2502.18151/) (2025)
- [Mode Collapse Happens: Evaluating Critical Interactions in Joint Trajectory Prediction Models](https://mlsanity.com/paper/2506.23164/) (2025)

## Abstract (arXiv)

This paper presents a framework for fast and robust motion planning designed to facilitate automated driving. The framework allows for real-time computation even for horizons of several hundred meters and thus enabling automated driving in urban conditions. This is achieved through several features. Firstly, a convenient geometrical representation of both the search space and driving constraints enables the use of classical path planning approach. Thus, a wide variety of constraints can be tackled simultaneously (other vehicles, traffic lights, etc.). Secondly, an exact cost-to-go map, obtained by solving a relaxed problem, is then used by A*-based algorithm with model predictive flavour in order to compute the optimal motion trajectory. The algorithm takes into account both distance and time horizons. The approach is validated within a simulation study with realistic traffic scenarios. We demonstrate the capability of the algorithm to devise plans both in fast and slow driving conditions, even when full stop is required.