# Interaction-Aware Trajectory Prediction and Planning in Dense Highway Traffic using Distributed Model Predictive Control

arXiv: https://arxiv.org/abs/2308.13053 · HTML: https://mlsanity.com/paper/2308.13053/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Aug 2023.

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

- [Interactive Trajectory Planning with Learning-based Distributionally Robust Model Predictive Control and Markov Systems](https://mlsanity.com/paper/2605.07768/) (2026)

## Abstract (arXiv)

In this paper we treat optimal trajectory planning for an autonomous vehicle (AV) operating in dense traffic, where vehicles closely interact with each other. To tackle this problem, we present a novel framework that couples trajectory prediction and planning in multi-agent environments, using distributed model predictive control. A demonstration of our framework is presented in simulation, employing a trajectory planner using non-linear model predictive control. We analyze performance and convergence of our framework, subject to different prediction errors. The results indicate that the obtained locally optimal solutions are improved, compared with decoupled prediction and planning.