In driving scenes, road agents often engage in frequent interaction and strive to understand
their surroundings. Ego-agent (each road agent itself) predicts what behavior will be …

Gastrointestinal (GI) tract cancers pose a global health challenge, demanding precise
radiotherapy planning for optimal treatment outcomes. This paper introduces a cutting-edge …

With the ongoing development of automated driving systems, the crucial task of predicting
pedestrian behavior is attracting growing attention. The prediction of future pedestrian …

Clinical patient notes are critical for documenting patient interactions, diagnoses, and
treatment plans in medical practice. Ensuring accurate evaluation of these notes is essential …

Predicting pedestrian crossing intention to ensure pedestrian safety has garnered significant
attention in autonomous driving. Balancing the accuracy and real-time performance of …

Accurate prediction of whether pedestrians will cross the street is prevalently recognized as
an indispensable function of autonomous driving systems, especially in urban environments …

Predicting Vulnerable Road User (VRU) crossing intention is one of the major challenges in
automated driving. Crossing intention prediction systems trained only on pedestrian features …

Abstract Machine learning-based techniques have shown great promises in perception,
prediction, planning, and general decision-making for improving task performance of …

While crowdsourcing is commonly used for objective labeling, eliciting subjective
annotations, like estimating mental states or perception of other's intention, remains …

In this paper, we present a systematic literature review that explores the utilization of
machine learning (ML) algorithms for analyzing datasets from Electroencephalography …