IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS Editor-In-Chief

TL;DR: This paper provides a review of the literature in on-road vision-based vehicle detection, tracking, and behavior understanding, and discusses the nascent branch of intelligent vehicles research concerned with utilizing spatiotemporal measurements, trajectories, and various features to characterize on- road behavior.

TL;DR: A survey of the traffic sign detection literature, detailing detection systems for traffic sign recognition (TSR) for driver assistance and discussing future directions of TSR research, including the integration of context and localization.

TL;DR: The SARIMA model coupled with a Kalman filter is the most accurate model; however, the proposed seasonal support vector regressor turns out to be highly competitive when performing forecasts during the most congested periods.

TL;DR: The issues that existing CACC control modules face when considering close to ideal driving conditions are discussed, including how to keep drivers engaged in driving tasks during CACC operations.

TL;DR: A computational model, which is referred to as the dynamic visual model (DVM), is proposed to detect critical motions of nearby vehicles while driving on a highway and a series of experimental results with both single and multiple critical motions are demonstrated to show the feasibility of the proposed system.

TL;DR: The prototype developed can be considered to be a very valuable complementary device to traditional radar-based techniques that are not totally valid for accurate short-range exploration, improving efficiency and safety in ship operations.

TL;DR: A critical analysis of the existing approaches for modeling and predicting air traffic complexity, examining their portability to autonomous ATM systems is presented.

TL;DR: This paper designs a cache strategy that allows vehicles to retrieve their unrequested data items and proposes a network-coding-assisted scheduling algorithm to enable the hybrid of vehicle-to-vehicle (V2V) and vehicle- to-infrastructure ( V2I) communications and exploit their joint effects on providing efficient data services.