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: Simulation results show that the MFL scheduling algorithm outperforms the traditional first-come-first-serve and earliest-deadline-first methods in terms of service failure and system handoff rates.

TL;DR: This position paper is concerned with the intersection of social behaviour and modern ICT, creating or recreating social conventions and social contexts through the use of pervasive, globe-spanning, omnipresent and participative ICT.

TL;DR: The numerical results and the depiction of the bottleneck phenomenon within the model suggests that the BUS-CTM obtains more realistic results compared with the application of the traditional CTM in a mixed car-bus transportation system.

TL;DR: This paper theoretically analyzes the performance of the greedy routing under three typical vehicle scenarios, i.e., the single-lane road, the multilane road, and the multilevel road and proves that the road-width is ignorable under certain conditions, whereas the data measured in an outdoor experiment reveal that theMultilevel feature is non-ignorable because its existence dramatically degrades the transmission range.