Sensors and Sensing for Intelligent Vehicles

Abstract

Over the past decades, both industry and academy have made enormous advancements in the field of intelligent vehicles, and a considerable number of prototypes are now driving our roads, railways, air and sea autonomously. However, there is still a long way to go before a widespread adoption. Among all the scientific and technical problems to be solved by intelligent vehicles, the ability to perceive, interpret, and fully understand the operational environment, as well as to infer future states and potential hazards, represent the most difficult and complex tasks, being probably the main bottlenecks that the scientific community and industry must solve in the coming years to ensure the safe and efficient operation of the vehicles (and, therefore, their future adoption). The great complexity and the almost infinite variety of possible scenarios in which an intelligent vehicle must operate, raise the problem of perception as an "endless" issue that will always be ongoing. As a humble contribution to the advancement of vehicles endowed with intelligence, we organized the Special Issue on Intelligent Vehicles. This work offers a complete analysis of all the mansucripts published, and presents the main conclusions drawn.

A Survey of Autonomous Vehicles: Enabling Communication Technologies and Challenges

“…In [14], the architecture design and implementation of an AV is discussed, and, in [15], various strategies of multi-sensor fusion are discussed. The recent developments and advancements in the perception and sensor technologies for AVs are presented in [16]. In [17], multiple-target and multiple-source are combined to form a framework for onboard sensors.…”

“…This uncertainty in the data (of position and velocity) can be reduced by combining the measured data with the prediction of the states utilising the Kalman filter. The Kalman filter models the uncertainty using a Gaussian model [16]. The variance of the Gaussian model, σ 2 , measures the amount of uncertainty in the state of the system and thus, a larger variance implies greater uncertainty [96].…”