Energy efficient navigation constitutes an important challenge in electric vehicles, due to
their limited battery capacity. We employ a Bayesian approach to model the energy …

In this paper, we study bottleneck identification in networks via extracting minimax paths.
Many real-world networks have stochastic weights for which full knowledge is not available …

Recent developments in artificial intelligence and automation support a new drug design
paradigm: autonomous drug design. Under this paradigm, generative models can provide …

Advanced route planning algorithms are one of the key enabling technologies for emerging
electric and autonomous mobility. Large realistic data sets are needed to test such …

Bottleneck identification is a challenging task in network analysis, especially when the
network is not fully specified. To address this task, we develop a unified online learning …

Electric Vehicle routing is often modeled as a generalization of the energy-constrained
shortest path problem, taking travel times and energy consumptions on road network edges …

In this work, we address the problem of long-distance navigation for battery electric vehicles
(BEVs), where one or more charging sessions are required to reach the intended …

Modeling driver behavior provides several advantages in the automotive industry, including
prediction of electric vehicle energy consumption. Studies have shown that aggressive …

Effective time-driven navigation is an operative way to alleviate traffic congestion, which is
also a challenging problem in the Internet of Vehicles context. Most existing centralized …

In this paper, we propose machine learning solutions to predict the time of future trips and
the possible distance the vehicle will travel. For this prediction task, we develop and …