# An Online Spatial-Temporal Graph Trajectory Planner for Autonomous Vehicles

arXiv: https://arxiv.org/abs/2404.12256 · HTML: https://mlsanity.com/paper/2404.12256/

## 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 7 of its 43 references that are in the map.
- First posted on arXiv 18 Apr 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, 7 of 7)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles](https://mlsanity.com/paper/2001.11231/) (2020)
- [Differentiable Integrated Motion Prediction and Planning with Learnable Cost Function for Autonomous Driving](https://mlsanity.com/paper/2207.10422/) (2022)
- [Combining Planning and Deep Reinforcement Learning in Tactical Decision Making for Autonomous Driving](https://mlsanity.com/paper/1905.02680/) (2019)
- [An Ethical Trajectory Planning Algorithm for Autonomous Vehicles](https://mlsanity.com/paper/2212.08577/) (2022)
- [Multilayer Graph-Based Trajectory Planning for Race Vehicles in Dynamic Scenarios](https://mlsanity.com/paper/2005.08664/) (2020)
- [An Efficient Spatial-Temporal Trajectory Planner for Autonomous Vehicles in Unstructured Environments](https://mlsanity.com/paper/2208.13160/) (2022)

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

- [NSP: A Neuro-Symbolic Natural Language Navigational Planner](https://mlsanity.com/paper/2409.06859/) (2024)

## Abstract (arXiv)

The autonomous driving industry is expected to grow by over 20 times in the coming decade and, thus, motivate researchers to delve into it. The primary focus of their research is to ensure safety, comfort, and efficiency. An autonomous vehicle has several modules responsible for one or more of the aforementioned items. Among these modules, the trajectory planner plays a pivotal role in the safety of the vehicle and the comfort of its passengers. The module is also responsible for respecting kinematic constraints and any applicable road constraints. In this paper, a novel online spatial-temporal graph trajectory planner is introduced to generate safe and comfortable trajectories. First, a spatial-temporal graph is constructed using the autonomous vehicle, its surrounding vehicles, and virtual nodes along the road with respect to the vehicle itself. Next, the graph is forwarded into a sequential network to obtain the desired states. To support the planner, a simple behavioral layer is also presented that determines kinematic constraints for the planner. Furthermore, a novel potential function is also proposed to train the network. Finally, the proposed planner is tested on three different complex driving tasks, and the performance is compared with two frequently used methods. The results show that the proposed planner generates safe and feasible trajectories while achieving similar or longer distances in the forward direction and comparable comfort ride.