The automotive industry is seen to be making a monumental paradigm shift from manual to semi-autonomous to fully Autonomous Vehicles. An Advanced Driver Assistance System (ADAS) forms a major building block for realizing these next generation of highly Autonomous-capable Vehicles. Although the general ADAS architecture is widely discussed, limited details are available about the functionality of the modules and their interactions, backed up by scientific justification. This limits the utilization of such an architecture for pragmatic implementation. A Cognitive ADAS Architecture for level 4 Autonomous-capable Electrified Vehicles (EV) is proposed in this thesis. Variations for levels 3 and 3.5 (combination of levels 3 and 4, with the primary fallback through a human driver and the secondary through an Automated Driving System) are also presented.
A validated 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 28% energy economy increase, on average, compared to human driver control. Through a quantitative sensitivity analysis, the optimal Mission/Motion Planning and energy management are seen in addition to a positive impact on the EV battery, motor, and dynamics, realized from the minimized instantaneous fluctuations. These factors are considered to contribute to this significant increase in the energy economy of an autonomous-controlled EV. Furthermore, this impact was seen to be relatively higher for autonomous longitudinal vehicle control compared to lateral. This difference in the improved operation of the Autonomous-capable EV components between the Automated Driving System and the human driver control was seen to be the highest for the battery current.
In overall, an increase in vehicle autonomy, resulted in an improvement in the EV performance, dynamics and operation of the battery and motor, compared to a human driver control. / Thesis / Doctor of Philosophy (PhD)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/23832 |
| Date | January 2019 |
| Creators | Divakarla, Kavya Prabha |
| Contributors | Emadi, Ali, Razavi, Saiedeh, Electrical and Computer Engineering |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis, Video |
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