Abstract Connected and Automated Vehicles (CAVs) can potentially improve the
performance of the transportation system by reducing human errors. This paper investigates …

Automated driving desires better performance on tasks like motion planning and interacting
with pedestrians in mixed-traffic environments. Deep learning algorithms can achieve high …

While the cost of crashes exceeds $1 Trillion a year in the US alone, the availability of high-
resolution naturalistic driving data provides an opportunity for researchers to conduct an in …

Human–machine interactions (HMIs) describe how humans engage various systems,
including those that are smart, autonomous, or both. Most HMIs either allow the human to …

In this research, we propose novel mathematical models and algorithms for optimizing
connected and automated vehicles'(CAVs) trajectories at freeway weaving segments …

With the development of the Internet of Things, millions of sensors are being deployed in
cities to collect real-time data. This leads to a need for checking city states against city …

This study presents a model to characterize changes in network traffic flows as a result of
implementing connected and autonomous vehicle (CAV) technology based on traffic …

Trust in automation has gained much attention in both industry and academia. More and
more studies and evidence prove its importance in new technology acceptance and efficient …

Autonomous vehicles (AVs) enable drivers to devote their primary attention to non-driving-
related tasks (NDRTs). Consequently, AVs must provide intelligibility services appropriate to …

We present SaSTL-a novel Spatial Aggregation Signal Temporal Logic-for the efficient
runtime monitoring of safety and performance requirements in smart cities. We first describe …