Towards a Systematic Risk Assessment of Deep Neural Network Limitations in Autonomous Driving Perception
Safety and security are essential for the admission and acceptance of automated and autonomous vehicles. Deep neural networks (DNNs) are widely used for perception and further components of the autonomous driving (AD) stack. However, they possess several limitations, including lack of generalization, efficiency, explainability, plausibility, and robustness. These insufficiencies can pose significant risks to autonomous driving systems. However, hazards, threats, and risks associated with DNN limitations in this domain have not been systematically studied so far. In this work, we propose a joint workflow for risk assessment combining the hazard analysis and risk assessment (HARA) following...
| Authors | Svetlana Pavlitska, Christopher Gerking, J. Marius Zöllner |
|---|---|
| Primary category | cs.CR |
| Published | 21 April 2026 |
| Last updated at source | 21 April 2026 |
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