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MOBILITY AND SAFETY IMPACTS OF AUTONOMOUS VEHICLES
Connected and Autonomous Vehicles (CAV) are revolutionizing the automotive
space. We are at the cusp of a, once in a century, transformation in the automotive
space. This work strives to understand, analyze and provide insights on the various
dimensions this transition is going to impact. We begin with the exploration of the
CAV landscape which is in a continuous state of flux. We attempt to examine, analyze
and evaluate this space using semi-structured interviews with experts from across the
whole country. The interviews are supported additionally by survey questions which
further capture the expert views quantitatively. This initial exploratory study leads
us to the central questions of this study which include (1) Modeling of SAE (Society
of Automotive Engineers) vehicles from level 0 to level 5 using a simulation framework
(2) Analysis of mobility and safety impacts of SAE vehicles. (3) Building a predictive
model of the risk level of autonomous vehicles based on trajectory information.
For the modeling of AVs, the different levels of SAE were mapped to particular
functionalities. Each of these functionalities were then modeled using the external
driver model (EDM) and were tested on VISSIM to evaluate their performance. The
mobility impacts of these models were tested on a highway and an intersection environment. The analysis were conducted for 100% penetration levels for each SAE and
also for different penetration levels.
One of the most important benefits of AVs that has been touted by OEMs and
DOTs alike, are the safety benefits of CAVs. Among many industries which will
be affected by the safety aspects of CAVs, insurance industry is one of them. An
immediate challenge that lies in front of them will be to evaluate the risk level of
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different SAE classes of vehicles. This will be especially true as most of the SAE level
data is unavailable or very scarce. To overcome this limitation, we propose a novel
methodology to identify risky driver behavior for every SAE level. The framework
includes the utilization of surrogate safety measures modified for SAE levels. The
trajectory data created from SAE level simulation is used as the data set for model
training and testing which predicts driving risk. The models evaluated are logistic
regression, decision trees and neural networks. This framework provides a foundation
for modeling the riskiness of autonomous vehicles in traffic networks.
Funding
SPR 4123
History
Degree Type
- Master of Science
Department
- Civil Engineering
Campus location
- West Lafayette
