# Sampling-Based Motion Planning with Online Racing Line Generation for Autonomous Driving on Three-Dimensional Race Tracks

arXiv: https://arxiv.org/abs/2403.18643 · HTML: https://mlsanity.com/paper/2403.18643/

## Key facts

- Cited by 4 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 6 of its 20 references that are in the map.
- First posted on arXiv 27 Mar 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, 6 of 6)

- [Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing](https://mlsanity.com/paper/2202.07008/) (2022)
- [Optimization-Based Autonomous Racing of 1:43 Scale RC Cars](https://mlsanity.com/paper/1711.07300/) (2017)
- [A Non-Cooperative Game Approach to Autonomous Racing](https://mlsanity.com/paper/1712.03913/) (2017)
- [Motion Planning and Control for Multi Vehicle Autonomous Racing at High Speeds](https://mlsanity.com/paper/2207.11136/) (2022)
- [Online Time-Optimal Trajectory Planning on Three-Dimensional Race Tracks](https://mlsanity.com/paper/2304.10954/) (2023)
- [Hierarchical Time-Optimal Planning for Multi-Vehicle Racing](https://mlsanity.com/paper/2309.06768/) (2023)

## Built on by (most cited first, 4 of 4)

- [Trajectory Planning Using Reinforcement Learning for Interactive Overtaking Maneuvers in Autonomous Racing Scenarios](https://mlsanity.com/paper/2404.10658/) (2024)
- [A Survey on Deep Multi-Task Learning in Connected Autonomous Vehicles](https://mlsanity.com/paper/2508.00917/) (2025)
- [Reinforcement Learning-based Dynamic Adaptation for Sampling-Based Motion Planning in Agile Autonomous Driving](https://mlsanity.com/paper/2510.10567/) (2025)
- [Driving Context-guided Model Predictive Planning and Control for Autonomous Car Racing at the Limit and Beyond](https://mlsanity.com/paper/2609.12660/) (2026)

## Abstract (arXiv)

Existing approaches to trajectory planning for autonomous racing employ sampling-based methods, generating numerous jerk-optimal trajectories and selecting the most favorable feasible trajectory based on a cost function penalizing deviations from an offline-calculated racing line. While successful on oval tracks, these methods face limitations on complex circuits due to the simplistic geometry of jerk-optimal edges failing to capture the complexity of the racing line. Additionally, they only consider two-dimensional tracks, potentially neglecting or surpassing the actual dynamic potential. In this paper, we present a sampling-based local trajectory planning approach for autonomous racing that can maintain the lap time of the racing line even on complex race tracks and consider the race track's three-dimensional effects. In simulative experiments, we demonstrate that our approach achieves lower lap times and improved utilization of dynamic limits compared to existing approaches. We also investigate the impact of online racing line generation, in which the time-optimal solution is planned from the current vehicle state for a limited spatial horizon, in contrast to a closed racing line calculated offline. We show that combining the sampling-based planner with the online racing line generation can significantly reduce lap times in multi-vehicle scenarios.