Bayer

Last updated January 31, 2026

432

Innovation Areas

13,147

Inventors

104

Collaborations

Neural network training architecture: BayerRecent Research Landscape

Manual calibration of autonomous vehicle maneuvers is too slow and error-prone for complex environments. This approach automates decision-making logic through iterative agent training to ensure safe and efficient path execution.

What technical problems is Bayer addressing in Neural network training architecture?

Inaccurate driving situation assessment

(26)evidences

Uncertainty in future spatial positioning leads to unsafe navigation and collision risks. Improving path estimation accuracy ensures reliable autonomous maneuvering and safety.

Cross domain data scarcity

(14)evidences

Discrepancies between distributed data sources and real-world sensor environments lead to model divergence. Aligning these representations ensures reliable validation of autonomous vehicle environments.

Unreliable autonomous decision outputs

(9)evidences

Unpredictable behavior and performance degradation in automated driving functions under complex real-world conditions. Reducing these risks ensures operational reliability and prevents catastrophic failure modes.

Unreliable environmental object classification

(5)evidences

External acoustic disturbances and road surface variability degrade the accuracy of automated driving and safety systems. Eliminating these signal corruptions ensures reliable vehicle control and driver warning integrity.