Personalized Gear Shifting Architecture for Next Generation Automatic Transmission Systems

Personalization is one of the trending topics of nowadays. Artificial Intelligence based technologies enable us to personalize systems to reflect user desire and driving profile. In automotive domain, we see intelligent software takes place in many aspects of the vehicle including transmission systems. Today, most of the vehicles are produced with an automatic transmission system which works as programmed according to the development expertise but does not incorporate behavior feedbacks. This paper proposes a novel contribution to automatic transmission systems by incorporating driver feedback to achieve personalization. This way, next generation automatic transmission systems can learn from user behavior taking their inputs into account and reflect under certain conditions. The system learns driver’s demands on the road via supervised learning and predicts driver’s desired gear according to the road conditions, user manipulations, and all relevant information gathered from the vehicle at run time. Learning desires of the driver can fit into the automatic transmission’s decision-making process without violating safety standards and the operational durability as well as leaving very small footprint in terms of memory, space and computation respecting to the limited capability of the environment that the method resides. The proposed method was tested in a realistic testing environment and the results are promising so that it can be deployed in a vehicle to extend automatic transmissions’ capabilities with personalization. In fact, personalized shifting leads to better customer experience and retention.