Magna revealed its vision for a flexible vehicle interior centered around seating reconfigurability and the opportunities new mobility and autonomy will offer passengers in the future.
The company used rich digital and in-market consumer research in China, Europe, and the U.S. to understand the views of the consumer when it comes to their personal experiences with seats. After observing consumers in their daily lives, Magna says it created the seating platform focused on delivering the ideal user experience. The reconfigurable seating solutions and technologies are designed to aid in activities from carrying cargo to fostering conversation on a long car ride and enabling a mobile meeting space during a ride share.
"Magna's seating innovation is driven by the belief that while the vehicle occupant experience will be very different with the introduction of mobility and autonomy, the functional basics will remain the same: passengers want convenience, flexibility, and comfort," said Mike Bisson, President of Magna Seating. "This approach has essentially helped us create seats that adjust to the consumer, instead of having the consumer adjust to seats."
The concept seat ecosystem includes three specific configurations.
The carsharing configuration features cargo mode, which includes power long rails and seats that slide up under the instrument panel for maximum space, flexible hardware to create various cargo options, and a mobile app interface to preconfigure cargo mode (min, max, left, or right side).
The long road trip configuration has campfire seating with stadium swivel on power long rails, four-way headrest sleep support, pixelated vibratory/haptic massage seat for optimal blood flow, in-cabin communication, and personal sound zones.
The autonomous ridesharing configuration features conference seating with stadium swivel, reversible on power long rails; available seat signal that indicates which seat is available in a ride sharing vehicle; three-wide seating; easy-to-clean trim covers; personal sound zones; and object detection.