A Change of Paradigm for the Design and Reliability Testing of Touch-Based Cabin Controls on the Seats of Self-Driving Cars

Abstract

The current design paradigm of car cabin components assumes seats aligned with the driving direction. All passengers are aligned with the driver that, until recently, was the only element in charge of controlling the vehicle. The new paradigm of self-driving cars eliminates several of those requirements, releasing the driver from control duties and creating new opportunities for entertaining the passengers during the trip. This creates the need for controlling functionalities that must be closer to each user, namely on the seat. This work proposes the use of low-cost capacitive touch sensors for controlling car functions, multimedia controls, seat orientation, door windows, and others. In the current work, we have reached a proof of concept that is functional, as shown for several cabin functionalities. The proposed concept can be adopted by current car manufacturers without changing the automobile construction pipeline. It is flexible and can adopt a variety of new functionalities, mostly software-based, added by the manufacturer, or customized by the end-user. Moreover, the newly proposed technology uses a smaller number of plastic parts for producing the component, which implies savings in terms of production cost and energy, while increasing the life cycle of the component.

Autonomous Vehicles Technological Trends

“…Custódio et al [5] presented the current design paradigm of car cabin components which assumes that seats are aligned with the driving direction. All passengers are aligned with the driver that, until recently, was the only element in charge of controlling the vehicle.…”

Research on environment parameter perception of autonomous vehicle based on multi-vision machine vision

“…The mutual influence relationship between the self-vehicle and other vehicles on the road is mainly reflected in the relative distance and relative speed, considering that the process of changing lanes produces a strong correlation between the self-vehicle and the surrounding traffic environment, and when identifying its own driving mode, it mainly considers the instantaneous changes in the operating state of the self-vehicle, and has less correlation with factors other than the vehicle. However, by judging the distance between the self-car and the vehicle in front of the current lane, it can indirectly reflect the current maneuvering action taken by the autopilot, which may be stimulated by the slower driving speed of the vehicle in front of it to prompt the autopilot to generate the demand for lane change and thus take the lane change operation [1][2]. Then the equation for the expected following distance between the autopilot and the vehicle in front is:…”