Abstract: In some examples, a system may receive location information. The system may determine at least one landmark based on the location information. In addition, the system may determine one or more current conditions for the at least one landmark. Further, the system may receive sensor configuration information. Based on the sensor configuration information and the one or more current conditions, the system may determine the detectability of the at least one landmark.

Abstract: In some examples, one or more processors may receive at least one image of a road, and may determine at least one candidate group of pixels in the image as potentially corresponding to debris on the road. The one or more processors may determine at least two height-based features for the candidate group of pixels. For instance, the at least two height-based features may include a maximum height associated with the candidate group of pixels relative to a surface of the road, and an average height associated the candidate group of pixels relative to the surface of the road. In addition, the one or more processors may determine at least one weighting factor based on comparing the at least two height-based features to respective thresholds, and may determine whether the group of pixels corresponds to debris based at least on the comparing.

Abstract: In some examples, one or more processors of a vehicle may store, in a computer-readable medium, sign information relating a sign type of a traffic sign to a distance to a road edge for a plurality of sign types. The one or more processors may receive at least one image from at least one sensor, and may recognize a sign type of a traffic sign in the at least one image. Further, the one or more processors may determine a likely location of a road edge in relation to the vehicle based at least partially on the recognized sign type and the stored sign information. In some examples, the one or more processors may send one or more control signals to at least one component of the vehicle based on the determined likely location of the road edge relative to the vehicle.

Abstract: In some examples, one or more processors of a vehicle may receive location field-of-view (FOV) data for an upcoming location on a route traversed by the vehicle. For instance, the one or more processors may select, based on the location FOV data, one or more sensors to activate on the vehicle for performing sensing in a direction of a field of view indicated by the location FOV data. In addition, the one or more processors may cause the vehicle to traverse the upcoming location with the one or more sensors activated on the vehicle for performing sensing in the direction of the FOV indicated by the location FOV data.

Abstract: In some examples, processor(s) of a vehicle may store specified dimensions of a plurality of first objects. The processor(s) may receive a first image from a camera onboard the vehicle, recognize a first object within the image, and determine, based on the first image, a distance to the first object from the camera, and a width and height of the first object. Further, the processor(s) may determine a first measurement error from the determined width of the first object and a specified width of the first object, and a second measurement error from the determined height of the first object and the specified height of the first object. Based on the first and second measurement error, the processor(s) may determine a third measurement error, may determine one or more calibration parameters of the camera based on the determined measurement errors, and may use the calibration parameters for subsequently received images.

Abstract: Example implementations described herein involve a system for predicting vehicle behavior with a higher efficiency which can reduce the number of sensors and the amount of data needed for vehicle control systems. Example implementations described herein can be used for any type of vehicle control system, such as a powertrain control system, an adaptive cruise control system, an autonomous driving system, because the target vehicle to be predicted can be replaced to a preceding vehicle, a surrounding vehicle, and an ego vehicle.

Abstract: Example implementations described herein are directed to depression detection on roadways (e.g., potholes, horizontal panel lines of a roadway, etc.) through using vision sensor to realize improved safety for advanced driver assistance systems (ADAS) and autonomous driving (AD). Example implementations described herein detect candidate depressions in the roadway in real time and adjust the control of the vehicle system according to the detected depressions.

Abstract: In some examples, processor(s) of a vehicle may store specified dimensions of a plurality of first objects. The processor(s) may receive a first image from a camera onboard the vehicle, recognize a first object within the image, and determine, based on the first image, a distance to the first object from the camera, and a width and height of the first object. Further, the processor(s) may determine a first measurement error from the determined width of the first object and a specified width of the first object, and a second measurement error from the determined height of the first object and the specified height of the first object. Based on the first and second measurement error, the processor(s) may determine a third measurement error, may determine one or more calibration parameters of the camera based on the determined measurement errors, and may use the calibration parameters for subsequently received images.

Abstract: In some examples, one or more processors of a vehicle may store, in a computer-readable medium, sign information relating a sign type of a traffic sign to a distance to a road edge for a plurality of sign types. The one or more processors may receive at least one image from at least one sensor, and may recognize a sign type of a traffic sign in the at least one image. Further, the one or more processors may determine a likely location of a road edge in relation to the vehicle based at least partially on the recognized sign type and the stored sign information. In some examples, the one or more processors may send one or more control signals to at least one component of the vehicle based on the determined likely location of the road edge relative to the vehicle.

Abstract: In some examples, a system may generate an alternating current signal to one of a first sensor electrode or a second sensor electrode. For instance, the first sensor electrode may be associated with a seatbelt and located adjacent to a torso of a vehicle operator of a vehicle. Further, the second sensor electrode may be positioned to be contacted by or otherwise proximate to another body part of the vehicle operator, such as at least one of an arm, hand, leg, or foot of the vehicle operator. The system may receive from the other one of the first sensor electrode or the second sensor electrode, a signal indicative of a capacitance, and may determine a respiration rate of the vehicle operator based at least partially on the signal indicative of the capacitance.

Abstract: In some examples, a system may generate an alternating current signal to one of a first sensor electrode or a second sensor electrode. For instance, the first sensor electrode may be associated with a seatbelt and located adjacent to a torso of a vehicle operator of a vehicle. Further, the second sensor electrode may be positioned to be contacted by or otherwise proximate to another body part of the vehicle operator, such as at least one of an arm, hand, leg, or foot of the vehicle operator. The system may receive from the other one of the first sensor electrode or the second sensor electrode, a signal indicative of a capacitance, and may determine a respiration rate of the vehicle operator based at least partially on the signal indicative of the capacitance.