Abstract: Improved calibration of a vehicle sensor based on static objects detected within an environment being traversed by the vehicle is disclosed. A first sensor such as a LiDAR can be calibrated to a global coordinate system via a second pre-calibrated sensor such as a GPS IMU. A static object present in the environment is detected such as signage. A type of the detected object is determined from static map data. Point cloud data representative of the static object is captured by the first sensor and a first transformation matrix for performing a transformation from a local coordinate system of the first sensor to a local coordinate system of the second sensor is iteratively redetermined until a desired calibration accuracy is achieved. Transformation to the global coordinate system is then achieved via application of the first transformation matrix followed by a second known transformation matrix.

Abstract: Described herein is a sensor assembly and a method of operating the sensor assembly. In various embodiments, the sensor assembly can comprise a base component, a light detection and ranging (LiDAR) sensor, a transparent cylinder, a motor component, and a controller. The LiDAR sensor can be mounted on a support platform disposed centrally on the base component. The transparent cylinder can be disposed peripherally to the LiDAR sensor and can provide a field of view (FOV) for the LiDAR sensor. The transparent cylinder can be rotated independently of the base component. The motor component can be disposed on the base component, adjacent to the support platform. The motor component can be coupled to the transparent cylinder through a gearset and configured to rotate the transparent cylinder. The controller can be configured to obtain sensor data from on-board vehicle sensors. The controller can determine a level of obscurement on the transparent cylinder based on the sensor data.

Abstract: A light detection and ranging (LiDAR) sensor is described herein. The LiDAR sensor can comprise a fiber optic ending, a laser assembly, and one or more processors. The fiber optic ending can comprise a fiber optic cable terminated by a reflector. The laser assembly can emit a chirp signal to detect an object in an environment. A portion of the chirp signal can be diverted to the fiber optic ending. The one or more processors construct a profile of the chirp signal based on the diverted portion of the chirp signal. The one or more processors determine a best fit curve based on the profile of the chirp signal and one or more parameters associated with the best fit curve. A frequency offset between an emitted chirp signal and a returned chirp signal can be computed based on the best fit curve and the one or more parameters. Based on the frequency offset, the one or more processors can determine a range of the object.

Abstract: Systems and methods of linearizing a signal of a light detection and ranging (LiDAR) sensor are described herein. A system receives a portion of a non-linear chirp signal. The portion of the non-linear chirp signal is sampled at a sampling frequency to generate data points corresponding to the portion of the non-linear chirp signal. A profile of the non-linear chirp signal is generated based on the data points. The non-linear chirp signal is linearized based on the profile of the non-linear chirp signal.

Abstract: A light detection and ranging (LiDAR) sensor is described herein. The LiDAR sensor can comprise a fiber optic ending, a laser assembly, and one or more processors. The fiber optic ending can comprise a fiber optic cable terminated by a reflector. The laser assembly can emit a chirp signal to detect an object in an environment. A portion of the chirp signal can be diverted to the fiber optic ending. The one or more processors construct a profile of the chirp signal based on the diverted portion of the chirp signal. The one or more processors determine a best fit curve based on the profile of the chirp signal and one or more parameters associated with the best fit curve. A frequency offset between an emitted chirp signal and a returned chirp signal can be computed based on the best fit curve and the one or more parameters. Based on the frequency offset, the one or more processors can determine a range of the object.

Abstract: Described herein are aerodynamically enhanced sensor housings. An aerodynamically enhanced sensor housing has an asymmetrical lateral cross-section that includes a first portion having a substantially spherical curvature and a second portion having a non-spherical curvature. The second portion having the non-spherical curvature may be elongated in relation to the first portion. An aerodynamically enhanced housing can also include one or more indentations formed in an exterior surface thereof to further enhance drag reducing characteristics of the housing. In addition, air flow characteristics around the sensor housing during vehicle operation can be assessed and a drag reduction protocol can be generated and implemented to further enhanced the drag reducing characteristics of the sensor housing.

Abstract: Provided herein is a system on a vehicle, the system comprising an active Doppler sensor; one or more processors; and a memory storing instructions that, when executed by the one or more processors, causes the system to perform: obtaining a Doppler signature from each of one or more entities; and determining one or more calibration parameters of the active Doppler sensor based on the Doppler signature from at least a portion of the one or more entities.

Abstract: Provided herein is a system on a vehicle, the system comprising an active Doppler sensor; one or more processors; and a memory storing instructions that, when executed by the one or more processors, causes the system to perform: obtaining a Doppler signature from each of one or more entities; and calibrating the active Doppler sensor based on the Doppler signature from at least a portion of the one or more entities.

Abstract: Systems and methods of linearizing a signal of a light detection and ranging (LiDAR) sensor are described herein. A system receives a portion of a non-linear chirp signal. The portion of the non-linear chirp signal is sampled at a sampling frequency to generate data points corresponding to the portion of the non-linear chirp signal. A profile of the non-linear chirp signal is generated based on the data points. The non-linear chirp signal is linearized based on the profile of the non-linear chirp signal.

Abstract: A light detection and ranging (LiDAR) sensor is described herein. The LiDAR sensor can comprise a fiber optic ending, a laser assembly, and one or more processors. The fiber optic ending can comprise a fiber optic cable terminated by a reflector. The laser assembly can emit a chirp signal to detect an object in an environment. A portion of the chirp signal can be diverted to the fiber optic ending. The one or more processors construct a profile of the chirp signal based on the diverted portion of the chirp signal. The one or more processors determine a best fit curve based on the profile of the chirp signal and one or more parameters associated with the best fit curve. A frequency offset between an emitted chirp signal and a returned chirp signal can be computed based on the best fit curve and the one or more parameters. Based on the frequency offset, the one or more processors can determine a range of the object.

Abstract: Described herein is a sensor assembly and a method of operating the sensor assembly. In various embodiments, the sensor assembly can comprise a base component, a light detection and ranging (LiDAR) sensor, a transparent cylinder, a motor component, and a controller. The LiDAR sensor can be mounted on a support platform disposed centrally on the base component. The transparent cylinder can be disposed peripherally to the LiDAR sensor and can provide a field of view (FOV) for the LiDAR sensor. The transparent cylinder can be rotated independently of the base component. The motor component can be disposed on the base component, adjacent to the support platform. The motor component can be coupled to the transparent cylinder through a gearset and configured to rotate the transparent cylinder. The controller can be configured to obtain sensor data from on-board vehicle sensors. The controller can determine a level of obscurement on the transparent cylinder based on the sensor data.

Abstract: Improved calibration of a vehicle sensor based on static objects detected within an environment being traversed by the vehicle is disclosed. A first sensor such as a LiDAR can be calibrated to a global coordinate system via a second pre-calibrated sensor such as a GPS IMU. A static object present in the environment is detected such as signage. A type of the detected object is determined from static map data. Point cloud data representative of the static object is captured by the first sensor and a first transformation matrix for performing a transformation from a local coordinate system of the first sensor to a local coordinate system of the second sensor is iteratively redetermined until a desired calibration accuracy is achieved. Transformation to the global coordinate system is then achieved via application of the first transformation matrix followed by a second known transformation matrix.

Abstract: Improved calibration of a vehicle sensor based on static objects detected within an environment being traversed by the vehicle is disclosed. A first sensor such as a LiDAR can be calibrated to a global coordinate system via a second pre-calibrated sensor such as a GPS IMU. Static objects present in the environment are detected such as signage. Point cloud data representative of the static objects are captured by the first sensor and a first transformation matrix for performing a transformation from a local coordinate system of the first sensor to a local coordinate system of the second sensor is iteratively redetermined until a desired calibration accuracy is achieved. Transformation to the global coordinate system is then achieved via application of the first transformation matrix followed by application of a second known transformation matrix to transition from the local coordinate system of the second pre-calibrated sensor to the global coordinate system.

Abstract: Described herein are aerodynamically enhanced sensor housings. An aerodynamically enhanced sensor housing has an asymmetrical lateral cross-section that includes a first portion having a substantially spherical curvature and a second portion having a non-spherical curvature. The second portion having the non-spherical curvature may be elongated in relation to the first portion. An aerodynamically enhanced housing can also include one or more indentations formed in an exterior surface thereof to further enhance drag reducing characteristics of the housing. In addition, air flow characteristics around the sensor housing during vehicle operation can be assessed and a drag reduction protocol can be generated and implemented to further enhanced the drag reducing characteristics of the sensor housing.

Abstract: Provided herein is a system on a vehicle, the system comprising an active Doppler sensor; one or more processors; and a memory storing instructions that, when executed by the one or more processors, causes the system to perform: obtaining a Doppler signature from each of one or more entities; and calibrating the active Doppler sensor based on the Doppler signature from at least a portion of the one or more entities.

Abstract: Provided herein is a system on a vehicle, the system comprising an active Doppler sensor; one or more processors; and a memory storing instructions that, when executed by the one or more processors, causes the system to perform: obtaining a Doppler signature from each of one or more entities; and determining one or more calibration parameters of the active Doppler sensor based on the Doppler signature from at least a portion of the one or more entities.