A Cognitive Advanced Driver Assistance Systems Architecture for Autonomous-Capable Electrified Vehicles

Autonomous vehicle industry is making rapid progress in the development of commercial vehicles with higher levels of autonomy. Although the general advanced driver assistance system (ADAS) architecture is widely discussed, limited details are available about the functionality of the modules and their interactions, backed up by scientific justification. This, in turn, limits the utilization of such architecture for pragmatic implementation. A cognitive ADAS architecture for level 4 autonomous-capable electrified vehicles (EVs) is proposed. Variations for levels 3 and 3.5, which are simply seen to be a combination of 3 and 4, with the primary fallback through a human driver and the secondary through an automated driving system, are also presented. A simulation framework is built for highway driving based on the proposed level 4 architecture for an enhanced Tesla Model S. It was concluded that the autonomous control provided a 23% energy economy increase, on average, compared to a human driver control. Through a detailed sensitivity analysis, the optimal mission/motion planning and energy management in addition to the positive impact on the EV battery, motor, and acceleration/deceleration profiles are considered to contribute to this significant increase in the energy economy of an autonomous-controlled EV.