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 driverxe2x80x99s demands on the road via supervised learning and predicts driverxe2x80x99s 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 transmissionxe2x80x99s decisionmaking 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 transmissionsxe2x80x99 capabilities with personalization. In fact, personalized shifting leads to better customer experience and retention.

AutoCoach: Driving Behavior Management Using Intelligent IoT Services AutoCoach is an intelligent agent intended for improving automobile driversu0027 performance by applying persuasive technology. System models like Advanced driverassistance (ADAS) and some UsagebasedInsurance (UBI) share an aim to increase car and road safety. However, most prior models do not consider the differences between driving habits. The AutoCoach design includes two unique components to build an effective persuasive system. The first component is the personality classification, which recognizes driversu0027 personalities by analyzing driving behavior patterns. The second component is the rewarding system, which determines the current driving behavioru0027s risk score based on some immediate past behavior. We propose the idea of memory factor, which decides when to provide feedback to drivers based on their personality. This memory factor identifies the most critical behaviors within a flexible timeperiod. AutoCoach then decides on feedback to maintain safe driving or improve the level of awareness for risky driving habits.