# Unveiling Uncertainty-Aware Autonomous Cooperative Learning Based Planning Strategy

arXiv: https://arxiv.org/abs/2510.11041 · HTML: https://mlsanity.com/paper/2510.11041/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for autonomous driving and traffic, one of 15 directions in Deep reinforcement learning methods.
- Builds on 14 of its 29 references that are in the map.
- First posted on arXiv 13 Oct 2025.

## Placed in

- Deep reinforcement learning methods / [RL for autonomous driving and traffic](https://mlsanity.com/deeprl/rl-for-autonomous-driving-and-traffic/)

## Builds on (most cited first, 14 of 14)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [FAST-LIO2: Fast Direct LiDAR-inertial Odometry](https://mlsanity.com/paper/2107.06829/) (2021)
- [NeuPAN: Direct Point Robot Navigation with End-to-End Model-based Learning](https://mlsanity.com/paper/2403.06828/) (2024)
- [Autonomous Platoon Control with Integrated Deep Reinforcement Learning and Dynamic Programming](https://mlsanity.com/paper/2206.07536/) (2022)
- [Edge Accelerated Robot Navigation With Collaborative Motion Planning](https://mlsanity.com/paper/2311.08983/) (2023)
- [Enhancing Large Vision Model in Street Scene Semantic Understanding through Leveraging Posterior Optimization Trajectory](https://mlsanity.com/paper/2501.01710/) (2025)
- [Formation and Reconfiguration of Tight Multi-Lane Platoons](https://mlsanity.com/paper/2003.08595/) (2020)
- [Efficient Rate-Splitting Multiple Access for the Internet of Vehicles: Federated Edge Learning and Latency Minimization](https://mlsanity.com/paper/2212.06396/) (2022)
- [Motion Planning and Tracking Control of Unmanned Underwater Vehicles: Technologies, Challenges and Prospects](https://mlsanity.com/paper/2207.04360/) (2022)
- [Collaborative Planning for Catching and Transporting Objects in Unstructured Environments](https://mlsanity.com/paper/2311.07100/) (2023)
- [Multi-Uncertainty Aware Autonomous Cooperative Planning](https://mlsanity.com/paper/2411.00413/) (2024)
- [AVDDPG: Federated reinforcement learning applied to autonomous platoon control](https://mlsanity.com/paper/2207.03484/) (2022)
- [Decentralized Planning for Car-Like Robotic Swarm in Cluttered Environments](https://mlsanity.com/paper/2210.05863/) (2022)
- [Edge-Assisted V2X Motion Planning and Power Control Under Channel Uncertainty](https://mlsanity.com/paper/2212.06459/) (2022)

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

- [Discrete Prototypical Memories for Federated Time Series Foundation Models](https://mlsanity.com/paper/2604.04475/) (2026)

## Abstract (arXiv)

In future intelligent transportation systems, autonomous cooperative planning (ACP), becomes a promising technique to increase the effectiveness and security of multi-vehicle interactions. However, multiple uncertainties cannot be fully addressed for existing ACP strategies, e.g. perception, planning, and communication uncertainties. To address these, a novel deep reinforcement learning-based autonomous cooperative planning (DRLACP) framework is proposed to tackle various uncertainties on cooperative motion planning schemes. Specifically, the soft actor-critic (SAC) with the implementation of gate recurrent units (GRUs) is adopted to learn the deterministic optimal time-varying actions with imperfect state information occurred by planning, communication, and perception uncertainties. In addition, the real-time actions of autonomous vehicles (AVs) are demonstrated via the Car Learning to Act (CARLA) simulation platform. Evaluation results show that the proposed DRLACP learns and performs cooperative planning effectively, which outperforms other baseline methods under different scenarios with imperfect AV state information.