MAConAuto: Framework for Mobile-Assisted Human-in-the-Loop Automotive System

Abstract

Automotive is becoming more and more sensor-equipped. Collision avoidance, lane departure warning, and self-parking are examples of applications becoming possible with the adoption of more sensors in the automotive industry. Moreover, the driver is now equipped with sensory systems like wearables and mobile phones. This rich sensory environment and the real-time streaming of contextual data from the vehicle make the human factor integral in the loop of computation. By integrating the human’s behavior and reaction into the advanced driver-assistance systems (ADAS), the vehicles become a more context-aware entity. Hence, we propose MAConAuto, a framework that helps design human-in-the-loop automotive systems by providing a common platform to engage the rich sensory systems in wearables and mobile to have context-aware applications. By personalizing the context adaptation in automotive applications, MAConAuto learns the behavior and reactions of the human to adapt to the personalized preference where interventions are continuously tuned using Reinforcement Learning. Our general framework satisfies three main design properties, adaptability, generalizability, and conflict resolution. We show how MAConAuto can be used as a framework to design two applications as human-centric applications, forward collision warning, and vehicle HVAC system with negligible time overhead to the average human response time.