# RouteFormer: A Transformer-Based Routing Framework for Autonomous Vehicles

arXiv: https://arxiv.org/abs/2504.05407 · HTML: https://mlsanity.com/paper/2504.05407/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Cooperative and V2X driving, one of 9 directions in End-to-end autonomous driving.
- Builds on 9 of its 46 references that are in the map.
- First posted on arXiv 7 Apr 2025.

## Placed in

- End-to-end autonomous driving / [Cooperative and V2X driving](https://mlsanity.com/driving/cooperative-and-v2x-driving/)

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Attention, Learn to Solve Routing Problems!](https://mlsanity.com/paper/1803.08475/) (2018)
- [Neural Combinatorial Optimization with Reinforcement Learning](https://mlsanity.com/paper/1611.09940/) (2016)
- [Learning the Travelling Salesperson Problem Requires Rethinking Generalization](https://mlsanity.com/paper/2006.07054/) (2020)
- [Extending the OpenAI Gym for robotics: a toolkit for reinforcement learning using ROS and Gazebo](https://mlsanity.com/paper/1608.05742/) (2016)
- [gym-gazebo2, a toolkit for reinforcement learning using ROS 2 and Gazebo](https://mlsanity.com/paper/1903.06278/) (2019)

## Abstract (arXiv)

Autonomous surveillance missions in Internet of Things (IoT) networks often involve solving NP-hard combinatorial optimization problems to ensure efficient resource utilization. To address the limitations of conventional heuristics in dynamic environments, we propose RouteFormer, a novel framework for single-agent routing in graph-based terrains. RouteFormer creates a synergy between the global context awareness of the transformer self-attention mechanism and the adaptive decision-making capabilities of Reinforcement Learning (RL). This architecture allows the system to output optimized routing decisions that adapt to complex task dependencies and resource availability without requiring labeled training datasets. We evaluated RouteFormer on varying graph sizes designed to resemble realistic reconnaissance missions. The results indicate that our model effectively handles the complexity of missions requiring multiple action profiles, outperforming baseline approaches, in terms of both time and distance. Specifically, RouteFormer achieved 10% and 7% reduction in distance compared to the solutions obtained from well-established solvers like Concorde and Lin-Kernighan-Helsgaun-3 (LKH-3). This improvement was achieved by effectively incorporating mission-specific constraints that traditional solvers overlook. The proposed framework serves as a modular, scalable pipeline for diverse autonomous scheduling and routing tasks.