Safe Driving in Occluded Environments
Abstract
Ensuring safe autonomous driving in the presence of occlusions poses a significant challenge in its policy design. While existing model-driven control techniques based on set invariance can handle visible risks, occlusions create latent risks in which safety-critical states are not observable. Data-driven techniques also struggle to handle latent risks because direct mappings from risk-critical objects in sensor inputs to safe actions cannot be learned without visible risk-critical objects. Motivated by these challenges, in this paper, we propose a probabilistic safety certificate for latent risk. Our key technical enabler is the application of probabilistic invariance: It relaxes the strict observability requirements imposed by set-invariance methods that demand the knowledge of risk-critical states. The proposed techniques provide linear action constraints that confine the latent risk probability within tolerance. Such constraints can be integrated into model predictive controllers or embedded in data-driven policies to mitigate latent risks. The proposed method is tested using the CARLA simulator and compared with a few existing techniques. The theoretical and empirical analysis jointly demonstrate that the proposed methods assure long-term safety in real-time control in occluded environments without being overly conservative and with transparency to exposed risks.
Cite
@article{arxiv.2510.13114,
title = {Safe Driving in Occluded Environments},
author = {Zhuoyuan Wang and Tongyao Jia and Pharuj Rajborirug and Neeraj Ramesh and Hiroyuki Okuda and Tatsuya Suzuki and Soummya Kar and Yorie Nakahira},
journal= {arXiv preprint arXiv:2510.13114},
year = {2025}
}