# DRIVE Through the Unpredictability:From a Protocol Investigating Slip to a Metric Estimating Command Uncertainty

arXiv: https://arxiv.org/abs/2506.16593 · HTML: https://mlsanity.com/paper/2506.16593/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 39 references that are in the map.
- First posted on arXiv 19 Jun 2025.

## Builds on (most cited first, 13 of 13)

- [Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning](https://mlsanity.com/paper/2108.06266/) (2021)
- [Information Theoretic Model Predictive Control: Theory and Applications to Autonomous Driving](https://mlsanity.com/paper/1707.02342/) (2017)
- [TartanDrive: A Large-Scale Dataset for Learning Off-Road Dynamics Models](https://mlsanity.com/paper/2205.01791/) (2022)
- [Model- and Acceleration-based Pursuit Controller for High-Performance Autonomous Racing](https://mlsanity.com/paper/2209.04346/) (2022)
- [Model Predictive Control for Aggressive Driving Over Uneven Terrain](https://mlsanity.com/paper/2311.12284/) (2023)
- [Multimodal dynamics modeling for off-road autonomous vehicles](https://mlsanity.com/paper/2011.11751/) (2020)
- [Kilometer-scale autonomous navigation in subarctic forests: challenges and lessons learned](https://mlsanity.com/paper/2111.13981/) (2021)
- [Evaluation of Skid-Steering Kinematic Models for Subarctic Environments](https://mlsanity.com/paper/2004.05131/) (2020)
- [Dynamics Models in the Aggressive Off-Road Driving Regime](https://mlsanity.com/paper/2405.16487/) (2024)
- [DRIVE: Data-driven Robot Input Vector Exploration](https://mlsanity.com/paper/2309.10718/) (2023)
- [Saturation-Aware Angular Velocity Estimation: Extending the Robustness of SLAM to Aggressive Motions](https://mlsanity.com/paper/2310.07844/) (2023)
- [Safe and Smooth: Certified Continuous-Time Range-Only Localization](https://mlsanity.com/paper/2209.04266/) (2022)
- [Comparing Motion Distortion Between Vehicle Field Deployments](https://mlsanity.com/paper/2405.00189/) (2024)

## Abstract (arXiv)

Off-road autonomous navigation is a challenging task as it is mainly dependent on the accuracy of the motion model. Motion model performances are limited by their ability to predict the interaction between the terrain and the UGV, which an onboard sensor can not directly measure. In this work, we propose using the DRIVE protocol to standardize the collection of data for system identification and characterization of the slip state space. We validated this protocol by acquiring a dataset with two platforms (from 75 kg to 470 kg) on six terrains (i.e., asphalt, grass, gravel, ice, mud, sand) for a total of 4.9 hours and 14.7 km. Using this data, we evaluate the DRIVE protocol's ability to explore the velocity command space and identify the reachable velocities for terrain-robot interactions. We investigated the transfer function between the command velocity space and the resulting steady-state slip for an SSMR. An unpredictability metric is proposed to estimate command uncertainty and help assess risk likelihood and severity in deployment. Finally, we share our lessons learned on running system identification on large UGV to help the community.