24,960 research outputs found
A Review of Sensor Technologies for Perception in Automated Driving
After more than 20 years of research, ADAS are
common in modern vehicles available in the market. Automated
Driving systems, still in research phase and limited in their
capabilities, are starting early commercial tests in public roads.
These systems rely on the information provided by on-board
sensors, which allow to describe the state of the vehicle, its
environment and other actors. Selection and arrangement of
sensors represent a key factor in the design of the system. This
survey reviews existing, novel and upcoming sensor technologies,
applied to common perception tasks for ADAS and Automated
Driving. They are put in context making a historical review of
the most relevant demonstrations on Automated Driving, focused
on their sensing setup. Finally, the article presents a snapshot of
the future challenges for sensing technologies and perception,
finishing with an overview of the commercial initiatives and
manufacturers alliances that will show future market trends in
sensors technologies for Automated Vehicles.This work has been partly supported by ECSEL Project ENABLE-
S3 (with grant agreement number 692455-2), by the
Spanish Government through CICYT projects (TRA2015-
63708-R and TRA2016-78886-C3-1-R)
A Survey on Modelling of Automotive Radar Sensors for Virtual Test and Validation of Automated Driving
Radar sensors were among the first perceptual sensors used for automated driving. Although several other technologies such as lidar, camera, and ultrasonic sensors are available, radar sensors have maintained and will continue to maintain their importance due to their reliability in adverse weather conditions. Virtual methods are being developed for verification and validation of automated driving functions to reduce the time and cost of testing. Due to the complexity of modelling high-frequency wave propagation and signal processing and perception algorithms, sensor models that seek a high degree of accuracy are challenging to simulate. Therefore, a variety of different modelling approaches have been presented in the last two decades. This paper comprehensively summarises the heterogeneous state of the art in radar sensor modelling. Instead of a technology-oriented classification as introduced in previous review articles, we present a classification of how these models can be used in vehicle development by using the V-model originating from software development. Sensor models are divided into operational, functional, technical, and individual models. The application and usability of these models along the development process are summarised in a comprehensive tabular overview, which is intended to support future research and development at the vehicle level and will be continuously updated
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