Safe Driving in Occluded Environments
Fuente:
arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866908593932992512 |
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| author | Wang, Zhuoyuan Jia, Tongyao Rajborirug, Pharuj Ramesh, Neeraj Okuda, Hiroyuki Suzuki, Tatsuya Kar, Soummya Nakahira, Yorie |
| author_facet | Wang, Zhuoyuan Jia, Tongyao Rajborirug, Pharuj Ramesh, Neeraj Okuda, Hiroyuki Suzuki, Tatsuya Kar, Soummya Nakahira, Yorie |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_13114 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Safe Driving in Occluded Environments Wang, Zhuoyuan Jia, Tongyao Rajborirug, Pharuj Ramesh, Neeraj Okuda, Hiroyuki Suzuki, Tatsuya Kar, Soummya Nakahira, Yorie Systems and Control Robotics 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. |
| title | Safe Driving in Occluded Environments |
| topic | Systems and Control Robotics |
| url | https://arxiv.org/abs/2510.13114 |