Abstract The integration of Deep Learning (DL) algorithms in Autonomous Vehicles (AVs)
has revolutionized their precision in navigating various driving scenarios, ranging from anti …

Many real-time application scenarios are developed in 6G communications. Driven by the
low-latency data processing requirements, multi-tier computing has become an important …

Air transportation communication jamming recognition model based on deep learning (DL)
can quickly and accurately identify and classify communication jamming, to improve the …

Increasing levels of communication and automation has led to the development of cyber
physical systems applications known as cooperative driving automation. While such …

Traffic congestion has been an actual problem in large cities, causing personal
inconvenience and environmental pollution. To solve this problem, new applications for …

Connected and automated vehicles (CAVs) provide various valuable and advanced
services to manufacturers, owners, mobility service providers, and transportation authorities …

Numerous studies have demonstrated, in-depth, the vulnerability of the deep reinforcement
learning (DRL) model's elements (eg, reward), which is a factor limiting the widespread …

Multiagent reinforcement learning (MARL) promises outstanding performance for
multiintersection traffic signal control systems (TSCS), enabling intelligent administration of …

In the labyrinthine world of cybersecurity, the ever-evolving specter of cyber-attacks offers an
inevitable challenge to the fortifications of protection measures. Past investigations have …

This paper investigates the role of driver behavior especially head pose dynamics in safety–
critical events (SCEs). Using a large dataset collected in a naturalistic driving study, this …