Abstract

The advanced driver assistance systems and connected vehicle (ADAS-CV) technologies may offer a promising approach to reduce vehicle crashes. However, their safety effectiveness can be affected by many factors. This will determine each ADAS-CV technology's promotion and development strategies. This study first summarized the major ADAS-CV technologies that were developed in recent years. By comparing the experiment and field test procedures conducted for these technologies, the study selected the most reliable results and suggested maximum safety effectiveness for each type of ADAs-CV technology. Then, this study analyzed the practical safety effectiveness of ADAS-CV technologies when they are promoted on the market and widely used in the real world. The study demonstrated that the safety effectiveness of ADAS-CV technologies were affected by features of system limitation, adoption and usage. Further, based on association analysis, this study proposed a scenario library for the testing and evaluating ADAS-CV technologies. Then, by using a driving simulator, this study assessed the effectiveness of ADAS-CV technologies in different pre-crash scenarios, considering the scenario heterogeneities. Two types of ADAs-CV technologies were investigated and they were pedestrian-to-vehicle technology and forward collision warning technology. This study analyzed their impacts on both driver behavior and safety benefits. Finally, this study conducted a Monte-Carlo simulation and identified the parameters of ADAS-CV that may achieve the maximum safety effectiveness in different pre-crash scenarios.

Notes

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Graduation Date

2020

Semester

Spring

Advisor

Abdel-Aty, Mohamed

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Civil, Environmental and Construction Engineering

Degree Program

Civil Engineering

Format

application/pdf

Identifier

CFE0008435; DP0023871

Language

English

Release Date

11-15-2025

Length of Campus-only Access

5 years

Access Status

Doctoral Dissertation (Campus-only Access)

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