arXiv:2606.04656v1 Announce Type: cross Abstract: Object detection is a safety-critical component of autonomous driving. It is essential to quantify the uncertainty in bounding-box predictions for safety assurance. Post hoc uncertainty quantification without retraining aligns with real-world deployment requirements; therefore, we employ the Laplace approximation. Because instance-level uncertainty is needed, linearized inference methods that require multiple backpropagations are not time-efficient, and sampling-based methods are not fully post hoc. We propose Monte-Carlo generalized linearized
The increasing deployment of AI in safety-critical applications like autonomous driving necessitates robust uncertainty quantification methods to ensure reliability and trust.
Quantifying uncertainty in AI predictions is crucial for safety assurance and regulatory approval, unlocking broader adoption of AI in high-stakes environments.
This development offers a more efficient and practical way to implement post hoc uncertainty quantification in object detection, addressing key deployment challenges.
- · Autonomous driving companies
- · AI safety and assurance firms
- · Regulators and policymakers
- · Insurance companies
- · Companies with unreliable AI systems
- · Traditional safety validation methods
- · Slow-moving regulatory bodies
Improved safety and reliability of AI systems in critical applications like autonomous vehicles.
Accelerated adoption and public trust in AI technologies across various safety-critical industries.
New regulatory frameworks and industry standards specifically tailored for trustworthy AI with quantified uncertainty.
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Read at arXiv cs.AI