Currently many autonomous vehicles require a per- son to monitor the system and take over when an unexpected or unusual event occurs. A person may be able to monitor multiple such vehicles but a question arises as to how many, and how to measure the cognitive requirements. Brain Computer Interfaces (BCI) operating passively could aid in assisting remote operators in such tasks but there is as yet significant research to be undertaken before such technology becomes robust and effective. To this end we describe a platform for acquisition of multi-modal data for passive hybrid Brain Computer Inter- face (phBCI) development purposes. The open source system integrates electroencephalography (EEG), computer vision and a wearable inertial measurement unit (IMU) along with time- stamped event markers for a subject engaged in a set of driving-related tasks as applied to blended control of multiple vehicles. The vehicular control task is realised both with graded complexity simulations and physical scale autonomous vehicles. This platform has the following significant advantages: reduced experimental variability due to data acquisition system implementation decisions; ease of reproduction of experiments through shareable configuration information; and acceleration of open science dataset accumulation. Consequently this freely available open source platform has the potential to enhance the reproducibility of passive hybrid BCI experimental research.