Some autonomous vehicles rely on deep learning models to generate outputs that can be used to control the autonomous vehicle. Deep learning models can be trained to fit datasets collected in a particular environment. These deep learning models may not perform as well in a different environment, and new deep learning models may need to be created and trained. Training new models can be computationally expensive, and the amount of datasets collected in a new environment for training the new models may be limited. Various techniques involving sharing parameters (e.g., weights) between deep learning models can alleviate some of these challenges.
WEIGHT SHARING BETWEEN DEEP LEARNING MODELS USED IN AUTONOMOUS VEHICLES
2023-12-14
Patent
Electronic Resource
English
IPC: | G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen / B60W CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION , Gemeinsame Steuerung oder Regelung von Fahrzeug-Unteraggregaten verschiedenen Typs oder verschiedener Funktion |