# A Conflicts-free, Speed-lossless KAN-based Reinforcement Learning Decision System for Interactive Driving in Roundabouts

arXiv: https://arxiv.org/abs/2408.08242 · HTML: https://mlsanity.com/paper/2408.08242/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Reinforcement learning for driving, one of 9 directions in End-to-end autonomous driving.
- Builds on 12 of its 43 references that are in the map.
- First posted on arXiv 15 Aug 2024.

## Placed in

- End-to-end autonomous driving / [Reinforcement learning for driving](https://mlsanity.com/driving/reinforcement-learning-for-driving/)
- 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, 12 of 12)

- [KAN: Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2404.19756/) (2024)
- [Self-Driving Cars: A Survey](https://mlsanity.com/paper/1901.04407/) (2019)
- [KANQAS: Kolmogorov-Arnold Network for Quantum Architecture Search](https://mlsanity.com/paper/2406.17630/) (2024)
- [An Integrated Framework of Decision Making and Motion Planning for Autonomous Vehicles Considering Social Behaviors](https://mlsanity.com/paper/2005.11059/) (2020)
- [DQ-GAT: Towards Safe and Efficient Autonomous Driving with Deep Q-Learning and Graph Attention Networks](https://mlsanity.com/paper/2108.05030/) (2021)
- [Game-theoretic Modeling of Traffic in Unsignalized Intersection Network for Autonomous Vehicle Control Verification and Validation](https://mlsanity.com/paper/1910.07141/) (2019)
- [Fair collaborative vehicle routing: A deep multi-agent reinforcement learning approach](https://mlsanity.com/paper/2310.17485/) (2023)
- [Discretizing Continuous Action Space with Unimodal Probability Distributions for On-Policy Reinforcement Learning](https://mlsanity.com/paper/2408.00309/) (2024)
- [Robust Dynamic Bus Control: A Distributional Multi-agent Reinforcement Learning Approach](https://mlsanity.com/paper/2111.01946/) (2021)
- [Multi-Agent Reinforcement Learning via Distributed MPC as a Function Approximator](https://mlsanity.com/paper/2312.05166/) (2023)
- [Perimeter Control with Heterogeneous Metering Rates for Cordon Signals: A Physics-Regularized Multi-Agent Reinforcement Learning Approach](https://mlsanity.com/paper/2308.12985/) (2023)
- [Control of Cross-Directional Systems with Approximate Symmetries](https://mlsanity.com/paper/2306.17565/) (2023)

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

- [Evaluating Scenario-based Decision-making for Interactive Autonomous Driving Using Rational Criteria: A Survey](https://mlsanity.com/paper/2501.01886/) (2025)

## Abstract (arXiv)

Safety and efficiency are crucial for autonomous driving in roundabouts, especially mixed traffic with both autonomous vehicles (AVs) and human-driven vehicles. This paper presents a learning-based algorithm that promotes safe and efficient driving across varying roundabout traffic conditions. A deep Q-learning network is used to learn optimal strategies in complex multi-vehicle roundabout scenarios, while a Kolmogorov-Arnold Network (KAN) improves the AVs' environmental understanding. To further enhance safety, an action inspector filters unsafe actions, and a route planner optimizes driving efficiency. Moreover, model predictive control ensures stability and precision in execution. Experimental results demonstrate that the proposed system consistently outperforms state-of-the-art methods, achieving fewer collisions, reduced travel time, and stable training with smooth reward convergence.