# Safe Merging in Mixed Traffic with Confidence

arXiv: https://arxiv.org/abs/2403.05742 · HTML: https://mlsanity.com/paper/2403.05742/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Mar 2024.

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

- [Exploring the Noise Robustness of Online Conformal Prediction](https://mlsanity.com/paper/2501.18363/) (2025)
- [AI Recommendation Systems for Lane-Changing Using Adherence-Aware Reinforcement Learning](https://mlsanity.com/paper/2504.20187/) (2025)
- [Softmax is not Enough (for Adaptive Conformal Classification)](https://mlsanity.com/paper/2602.19498/) (2026)
- [Conflict Forecasting via Conformal Prediction for Markov Processes](https://mlsanity.com/paper/2604.25139/) (2026)

## Abstract (arXiv)

In this letter, we present an approach for learning human driving behavior, without relying on specific model structures or prior distributions, in a mixed-traffic environment where connected and automated vehicles (CAVs) coexist with human-driven vehicles (HDVs). We employ conformal prediction to obtain theoretical safety guarantees and use real-world traffic data to validate our approach. Then, we design a controller that ensures effective merging of CAVs with HDVs with safety guarantees. We provide numerical simulations to illustrate the efficacy of the control approach.