As driver assistance technologies become increasingly autonomous and pervasive, there is a growing demand for co-pilot systems that are not just technically sophisticated but also adaptable to individual drivers and dynamic situations. Modern Advanced Driver Assistance Systems (ADAS) fall short of offering personalized and context-sensitive support, leading to low user uptake and effectiveness. This article offers a conceptual review of new directions in intelligent driver assistance based on advances in human–machine interaction, behavior modeling, and affective computing. We outline the main shortcomings of current solutions and suggest a framework of design principles to shape the next generation of co-pilot systems. These principles are focused on continuous personalization, real-time contextual sensitivity, proactive assistance, and user-oriented interaction and are aimed at fostering a more empathic and adaptive driving experience. Our contribution is intended to guide the development of co-pilots that learn with users, improve safety and comfort, and fit into the vision of human-centered mobility systems, while also outlining directions for empirical validation and ethical deployment.