# Risk-Aware Off-Road Navigation via a Learned Speed Distribution Map

arXiv: https://arxiv.org/abs/2203.13429 · HTML: https://mlsanity.com/paper/2203.13429/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 42 references that are in the map.
- First posted on arXiv 25 Mar 2022.

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

- [End to End Learning for Self-Driving Cars](https://mlsanity.com/paper/1604.07316/) (2016)
- [CAD2RL: Real Single-Image Flight without a Single Real Image](https://mlsanity.com/paper/1611.04201/) (2016)
- [RELLIS-3D Dataset: Data, Benchmarks and Analysis](https://mlsanity.com/paper/2011.12954/) (2020)
- [BADGR: An Autonomous Self-Supervised Learning-Based Navigation System](https://mlsanity.com/paper/2002.05700/) (2020)
- [How Should a Robot Assess Risk? Towards an Axiomatic Theory of Risk in Robotics](https://mlsanity.com/paper/1710.11040/) (2017)
- [STEP: Stochastic Traversability Evaluation and Planning for Risk-Aware Off-road Navigation](https://mlsanity.com/paper/2103.02828/) (2021)
- [APPLE: Adaptive Planner Parameter Learning from Evaluative Feedback](https://mlsanity.com/paper/2108.09801/) (2021)
- [Learning to Drive Off Road on Smooth Terrain in Unstructured Environments Using an On-Board Camera and Sparse Aerial Images](https://mlsanity.com/paper/2004.04697/) (2020)
- [G-VOM: A GPU Accelerated Voxel Off-Road Mapping System](https://mlsanity.com/paper/2109.13176/) (2021)
- [Active Bayesian Multi-class Mapping from Range and Semantic Segmentation Observation](https://mlsanity.com/paper/2101.01831/) (2021)
- [Control of rough terrain vehicles using deep reinforcement learning](https://mlsanity.com/paper/2107.01867/) (2021)
- [Planning Beyond the Sensing Horizon Using a Learned Context](https://mlsanity.com/paper/1908.09171/) (2019)
- [Fast Local Planning and Mapping in Unknown Off-Road Terrain](https://mlsanity.com/paper/1910.08521/) (2019)

## Built on by (most cited first, 20 of 20)

- [How Does It Feel? Self-Supervised Costmap Learning for Off-Road Vehicle Traversability](https://mlsanity.com/paper/2209.10788/) (2022)
- [EVORA: Deep Evidential Traversability Learning for Risk-Aware Off-Road Autonomy](https://mlsanity.com/paper/2311.06234/) (2023)
- [Wild Visual Navigation: Fast Traversability Learning via Pre-Trained Models and Online Self-Supervision](https://mlsanity.com/paper/2404.07110/) (2024)
- [Learning Risk-Aware Costmaps via Inverse Reinforcement Learning for Off-Road Navigation](https://mlsanity.com/paper/2302.00134/) (2023)
- [PIETRA: Physics-Informed Evidential Learning for Traversing Out-of-Distribution Terrain](https://mlsanity.com/paper/2409.03005/) (2024)
- [Reinforcement Learning for Wheeled Mobility on Vertically Challenging Terrain](https://mlsanity.com/paper/2409.02383/) (2024)
- [Learning Semantics-Aware Locomotion Skills from Human Demonstration](https://mlsanity.com/paper/2206.13631/) (2022)
- [Risk-aware Path Planning via Probabilistic Fusion of Traversability Prediction for Planetary Rovers on Heterogeneous Terrains](https://mlsanity.com/paper/2303.01169/) (2023)
- [Safe Navigation in Unstructured Environments by Minimizing Uncertainty in Control and Perception](https://mlsanity.com/paper/2306.14601/) (2023)
- [UFO: Uncertainty-aware LiDAR-image Fusion for Off-road Semantic Terrain Map Estimation](https://mlsanity.com/paper/2403.02642/) (2024)
- [QuadrupedGPT: Towards a Versatile Quadruped Agent in Open-ended Worlds](https://mlsanity.com/paper/2406.16578/) (2024)
- [VertiSelector: Automatic Curriculum Learning for Wheeled Mobility on Vertically Challenging Terrain](https://mlsanity.com/paper/2409.17469/) (2024)
- [Meta-Learning Online Dynamics Model Adaptation in Off-Road Autonomous Driving](https://mlsanity.com/paper/2504.16923/) (2025)
- [ZeST: A VLM-based Zero-Shot Traversability Navigation for Unknown Environments](https://mlsanity.com/paper/2508.19131/) (2025)
- [Uncertainty-aware Perception Models for Off-road Autonomous Unmanned Ground Vehicles](https://mlsanity.com/paper/2209.11115/) (2022)
- [Situationally-Aware Dynamics Learning](https://mlsanity.com/paper/2505.19574/) (2025)
- [Uncertainty-Aware Gaussian Map for Vision-Language Navigation](https://mlsanity.com/paper/2605.26503/) (2026)
- [NavG: Risk-Aware Navigation in Crowded Environments Based on Reinforcement Learning with Guidance Points](https://mlsanity.com/paper/2503.02111/) (2025)
- [Learning Smooth State-Dependent Traversability from Dense Point Clouds](https://mlsanity.com/paper/2506.04362/) (2025)
- [Mars Traversability Prediction: A Multi-modal Self-supervised Approach for Costmap Generation](https://mlsanity.com/paper/2509.11082/) (2025)

## Abstract (arXiv)

Motion planning in off-road environments requires reasoning about both the geometry and semantics of the scene (e.g., a robot may be able to drive through soft bushes but not a fallen log). In many recent works, the world is classified into a finite number of semantic categories that often are not sufficient to capture the ability (i.e., the speed) with which a robot can traverse off-road terrain. Instead, this work proposes a new representation of traversability based exclusively on robot speed that can be learned from data, offers interpretability and intuitive tuning, and can be easily integrated with a variety of planning paradigms in the form of a costmap. Specifically, given a dataset of experienced trajectories, the proposed algorithm learns to predict a distribution of speeds the robot could achieve, conditioned on the environment semantics and commanded speed. The learned speed distribution map is converted into costmaps with a risk-aware cost term based on conditional value at risk (CVaR). Numerical simulations demonstrate that the proposed risk-aware planning algorithm leads to faster average time-to-goals compared to a method that only considers expected behavior, and the planner can be tuned for slightly slower, but less variable behavior. Furthermore, the approach is integrated into a full autonomy stack and demonstrated in a high-fidelity Unity environment and is shown to provide a 30% improvement in the success rate of navigation.