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| Main Author: | |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.19006 |
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| _version_ | 1866929293727105024 |
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| author | Wolf, Patrick |
| author_facet | Wolf, Patrick |
| contents | Autonomous driving vehicles provide a vast potential for realizing use cases in the on-road and off-road domains. Consequently, remarkable solutions exist to autonomous systems' environmental perception and control. Nevertheless, proof of safety remains an open challenge preventing such machinery from being introduced to markets and deployed in real world. Traditional approaches for safety assurance of autonomously driving vehicles often lead to underperformance due to conservative safety assumptions that cannot handle the overall complexity. Besides, the more sophisticated safety systems rely on the vehicle's perception systems. However, perception is often unreliable due to uncertainties resulting from disturbances or the lack of context incorporation for data interpretation. Accordingly, this paper illustrates the potential of a modular, self-adaptive autonomy framework with integrated dynamic risk management to overcome the abovementioned drawbacks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_19006 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Ensuring Safe Autonomy: Navigating the Future of Autonomous Vehicles Wolf, Patrick Robotics Autonomous driving vehicles provide a vast potential for realizing use cases in the on-road and off-road domains. Consequently, remarkable solutions exist to autonomous systems' environmental perception and control. Nevertheless, proof of safety remains an open challenge preventing such machinery from being introduced to markets and deployed in real world. Traditional approaches for safety assurance of autonomously driving vehicles often lead to underperformance due to conservative safety assumptions that cannot handle the overall complexity. Besides, the more sophisticated safety systems rely on the vehicle's perception systems. However, perception is often unreliable due to uncertainties resulting from disturbances or the lack of context incorporation for data interpretation. Accordingly, this paper illustrates the potential of a modular, self-adaptive autonomy framework with integrated dynamic risk management to overcome the abovementioned drawbacks. |
| title | Ensuring Safe Autonomy: Navigating the Future of Autonomous Vehicles |
| topic | Robotics |
| url | https://arxiv.org/abs/2403.19006 |