# Models and Predictive Control for Nonplanar Vehicle Navigation

arXiv: https://arxiv.org/abs/2104.08427 · HTML: https://mlsanity.com/paper/2104.08427/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2021.

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

- [Learning Model Predictive Controllers with Real-Time Attention for Real-World Navigation](https://mlsanity.com/paper/2209.10780/) (2022)
- [A Contextual Bandit Approach for Learning to Plan in Environments with Probabilistic Goal Configurations](https://mlsanity.com/paper/2211.16309/) (2022)
- [Predictive Control for Autonomous Driving with Uncertain, Multi-modal Predictions](https://mlsanity.com/paper/2310.20561/) (2023)
- [GAN-MPC: Training Model Predictive Controllers with Parameterized Cost Functions using Demonstrations from Non-identical Experts](https://mlsanity.com/paper/2305.19111/) (2023)

## Abstract (arXiv)

We present a simplified model of a vehicle driving on a nonplanar road. A parametric surface is used to describe the nonplanar road which can describe any combination of curvature, bank and slope. We show that the proposed modeling approach generalizes planar vehicle models that reference a centerline, such as the Frenet model. We use the proposed approach for vehicle path planning and following using model predictive control. We also model and control vehicle contact with the road surface. We demonstrate that the proposed controller improves speed and lane following on complex roads compared to planar vehicle controllers, and mitigates loss of control on complex road surfaces including off-camber turns.