# Limited Visibility and Uncertainty Aware Motion Planning for Automated Driving

arXiv: https://arxiv.org/abs/1810.13001 · HTML: https://mlsanity.com/paper/1810.13001/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2018.

## Built on by (most cited first, 7 of 7)

- [Safety-aware Motion Prediction with Unseen Vehicles for Autonomous Driving](https://mlsanity.com/paper/2109.01510/) (2021)
- [Pedestrian Emergence Estimation and Occlusion-Aware Risk Assessment for Urban Autonomous Driving](https://mlsanity.com/paper/2107.02326/) (2021)
- [CARFF: Conditional Auto-encoded Radiance Field for 3D Scene Forecasting](https://mlsanity.com/paper/2401.18075/) (2024)
- [SafePath: Conformal Prediction for Safe LLM-Based Autonomous Navigation](https://mlsanity.com/paper/2505.09427/) (2025)
- [TL-GAN: Improving Traffic Light Recognition via Data Synthesis for Autonomous Driving](https://mlsanity.com/paper/2203.15006/) (2022)
- [Social Occlusion Inference with Vectorized Representation for Autonomous Driving](https://mlsanity.com/paper/2303.10385/) (2023)
- [Framework for Quality Evaluation of Smart Roadside Infrastructure Sensors for Automated Driving Applications](https://mlsanity.com/paper/2304.07745/) (2023)

## Abstract (arXiv)

Adverse weather conditions and occlusions in urban environments result in impaired perception. The uncertainties are handled in different modules of an automated vehicle, ranging from sensor level over situation prediction until motion planning. This paper focuses on motion planning given an uncertain environment model with occlusions. We present a method to remain collision free for the worst-case evolution of the given scene. We define criteria that measure the available margins to a collision while considering visibility and interactions, and consequently integrate conditions that apply these criteria into an optimization-based motion planner. We show the generality of our method by validating it in several distinct urban scenarios.