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Radar Sensing for Intelligent Vehicles in Urban Environments.

Reina G, Johnson D, Underwood J - Sensors (Basel) (2015)

Bottom Line: Radar overcomes the shortcomings of laser, stereovision, and sonar because it can operate successfully in dusty, foggy, blizzard-blinding, and poorly lit scenarios.The algorithm operates directly in the sensor frame, without the need for a separate synchronised navigation source, calibration parameters describing the location of the radar in the vehicle frame, or the geometric restrictions made in the previous main method in the field.Experimental results are presented in various urban scenarios to validate this approach, showing its potential applicability for advanced driving assistance systems and autonomous vehicle operations.

View Article: PubMed Central - PubMed

Affiliation: Department of Engineering for Innovation, University of Salento, via Arnesano, 73100 Lecce, Italy. giulio.reina@unisalento.it.

ABSTRACT
Radar overcomes the shortcomings of laser, stereovision, and sonar because it can operate successfully in dusty, foggy, blizzard-blinding, and poorly lit scenarios. This paper presents a novel method for ground and obstacle segmentation based on radar sensing. The algorithm operates directly in the sensor frame, without the need for a separate synchronised navigation source, calibration parameters describing the location of the radar in the vehicle frame, or the geometric restrictions made in the previous main method in the field. Experimental results are presented in various urban scenarios to validate this approach, showing its potential applicability for advanced driving assistance systems and autonomous vehicle operations.

No MeSH data available.


Radar-based ground detection in a general environment where motion plane and area under investigation do not coincide.
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f5-sensors-15-14661: Radar-based ground detection in a general environment where motion plane and area under investigation do not coincide.

Mentions: A novel ground detection algorithm, referred to as the RCGD algorithm, is presented that operates directly in the sensor frame of the radar, without the need for a separate synchronised navigation source or any geometric restriction of the area investigated. Let us refer to the general case where the plane of vehicular motion is not coincident with the ground plane in view of the radar. In addition, the two planes can be freely tilted in the space and no assumption is made about their location. These observations lead to the representation in Figure 5. Therefore, the main idea is that the ground-echo depends only on the information described by the sensor frame and the relative position between the bore-sight and the ground plane, defined by the grazing angle. This is suggested by Equation (5), which is theoretically independent of the navigation solution.


Radar Sensing for Intelligent Vehicles in Urban Environments.

Reina G, Johnson D, Underwood J - Sensors (Basel) (2015)

Radar-based ground detection in a general environment where motion plane and area under investigation do not coincide.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4507681&req=5

f5-sensors-15-14661: Radar-based ground detection in a general environment where motion plane and area under investigation do not coincide.
Mentions: A novel ground detection algorithm, referred to as the RCGD algorithm, is presented that operates directly in the sensor frame of the radar, without the need for a separate synchronised navigation source or any geometric restriction of the area investigated. Let us refer to the general case where the plane of vehicular motion is not coincident with the ground plane in view of the radar. In addition, the two planes can be freely tilted in the space and no assumption is made about their location. These observations lead to the representation in Figure 5. Therefore, the main idea is that the ground-echo depends only on the information described by the sensor frame and the relative position between the bore-sight and the ground plane, defined by the grazing angle. This is suggested by Equation (5), which is theoretically independent of the navigation solution.

Bottom Line: Radar overcomes the shortcomings of laser, stereovision, and sonar because it can operate successfully in dusty, foggy, blizzard-blinding, and poorly lit scenarios.The algorithm operates directly in the sensor frame, without the need for a separate synchronised navigation source, calibration parameters describing the location of the radar in the vehicle frame, or the geometric restrictions made in the previous main method in the field.Experimental results are presented in various urban scenarios to validate this approach, showing its potential applicability for advanced driving assistance systems and autonomous vehicle operations.

View Article: PubMed Central - PubMed

Affiliation: Department of Engineering for Innovation, University of Salento, via Arnesano, 73100 Lecce, Italy. giulio.reina@unisalento.it.

ABSTRACT
Radar overcomes the shortcomings of laser, stereovision, and sonar because it can operate successfully in dusty, foggy, blizzard-blinding, and poorly lit scenarios. This paper presents a novel method for ground and obstacle segmentation based on radar sensing. The algorithm operates directly in the sensor frame, without the need for a separate synchronised navigation source, calibration parameters describing the location of the radar in the vehicle frame, or the geometric restrictions made in the previous main method in the field. Experimental results are presented in various urban scenarios to validate this approach, showing its potential applicability for advanced driving assistance systems and autonomous vehicle operations.

No MeSH data available.