PhD Oral Exam - Hamed Esmaeeli, Civil Engineering

When studying for a doctoral degree (PhD), candidates submit a thesis that provides a critical review of the current state of knowledge of the thesis subject as well as the student’s own contributions to the subject. The distinguishing criterion of doctoral graduate research is a significant and original contribution to knowledge.

Once accepted, the candidate presents the thesis orally. This oral exam is open to the public.

Abstract

Autonomous Vehicles (AVs) are expected to provide a large number of benefits such as improving comfort, vehicle safety and traffic flow. AVs use various sensors and control systems to empower driver’s decision making under uncertainties as well as assist the driving task under deteriorated conditions such as vision impairment. Excessive sunlight has been recognized as the primary source of the reduction in vision performance during daytime. Sun glare oftentimes leads to an impaired visibility for drivers and has been studied from different aspects on roadways. However, there is a lack of knowledge regarding the potential detrimental effects of natural light brightness differential, particularly sun glare on driving behavior and its possible risks.

This dissertation addresses this issue by developing an integrated vehicle safety methodology as an advisory system for safe driving under sun glare. The main contribution of this research is to establish a real-time detection of vision impairment area on roadways. This study also proposes a Collision Avoidance System Under Sun-glare (CASUS) in which upcoming possible vision impairment is detected, a warning message is sent, and the speed of vehicle is adjusted accordingly.

In this context, real-world data is used to calibrate a psychophysical car-following model within VISSIM, a traffic microscopic simulation tool. Traffic safety impacts are explored through the number of conflicts extracted from the microsimulation tool and assessed by the time-to-collision indicator. Conventional/human-driven vehicles and different type of AVs are modeled for a straight segment of the TransCanada highway under various AV penetration rates.

The finding revealed a significant reduction in potential collisions due to adjustment of travel speed of AVs under the sun glare. The results also indicated that applying CASUS to the AVs with a failing sensory system improves traffic safety by providing optimal-safe speeds. Furthermore, the CASUS algorithm has the potential to be integrated into driving simulators or real vehicles to further evaluate and examine its benefits under different vision impairment scenarios.