THESIS
2023
1 online resource (x, 48 pages) : illustrations (chiefly color)
Abstract
Recent transportation research often suggests that autonomous vehicles (AVs) can improve traffic
flow efficiency, as they are able to eliminate misconduct and confused mindset of human drivers.
Nevertheless, as a special class of ground robots, autonomous vehicles are inevitably subject to
robotic errors in their operations, particularly in the perception module. This causes inconvenient
uncertainties in their movements, or even car collisions. Consequently, conservative operational
strategies, such as larger headway and slower speeds, are employed when operating automated
functions on roads, sacrificing traffic capacity for safety. To reconcile the inconsistency, this paper
proposes an analytical model framework that delineates the endogenous reciprocity between traffic
safety and effi...[
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Recent transportation research often suggests that autonomous vehicles (AVs) can improve traffic
flow efficiency, as they are able to eliminate misconduct and confused mindset of human drivers.
Nevertheless, as a special class of ground robots, autonomous vehicles are inevitably subject to
robotic errors in their operations, particularly in the perception module. This causes inconvenient
uncertainties in their movements, or even car collisions. Consequently, conservative operational
strategies, such as larger headway and slower speeds, are employed when operating automated
functions on roads, sacrificing traffic capacity for safety. To reconcile the inconsistency, this paper
proposes an analytical model framework that delineates the endogenous reciprocity between traffic
safety and efficiency that arises from robotic uncertainty in AVs. Car-following scenarios are
extensively examined, with uncertain headway as the key parameter for bridging the capacity of a
single lane and collision probability. A two-state stochastic process derived from the dynamics of
the discrete AV system is then introduced to describe the passability of a lane, where the collision-inclusive
capacity is obtained to serve as the ultimate performance measure of fully autonomous
traffic. With the help of this analytical model, it is possible to support the setting of critical
parameters in AV operations and incorporate optimization techniques to improve related managerial
strategies and policies. On the other hand, under the quantitative connection of this method, safety
and efficiency objectives for the transportation system provide more precise targets and directions
for the design and development of autonomous vehicles. Moreover, the generalization of this model
is presented, providing the possibility to explore more complex AV models and scenarios.
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