Abstract: Techniques are described for monitoring tire conditions on an autonomous vehicle, more specifically, an autonomous truck that includes a tractor unit and a trailer. An example autonomous vehicle includes a tractor unit configured to be coupled to a trailer that comprises multiple tires. The autonomous vehicle may also include one or more infrared sensors. At least part of the one or more infrared sensors is positioned at a rear side of the tractor unit and faced towards the trailer. The one or more infrared sensors are configured to capture one or more heatmaps each representing a temperature distribution of a tire. The one or more infrared sensors are in communication with a control system that is configured to receive the temperature distribution from the one or more infrared sensors, determine a tire condition of the tire, and operate the tractor unit according to the tire condition.

Abstract: A system installed in a vehicle includes a first group of sensing devices configured to allow a first level of autonomous operation of the vehicle; a second group of sensing devices configured to allow a second level of autonomous operation of the vehicle, the second group of sensing devices including primary sensing devices and backup sensing devices; a third group of sensing devices configured to allow the vehicle to perform a safe stop maneuver; and a control element communicatively coupled to the first group of sensing devices, the second group of sensing devices, and the third group of sensing devices. The control element is configured to: receive data from at least one of the first group, the second group, or the third group of sensing devices, and provide a control signal to a sensing device based on categorization information indicating a group to which the sensing device belongs.

Abstract: Devices, systems, and methods for remote detection and ranging are described. In an embodiment, a remote sensing method includes obtaining data points that are spatially distributed and have respective intensity values, by performing a remote detection and ranging operation, determining a spatial autocorrelation of a set of data points, out of the data points, based on a difference in distances between data points in the data points, determining an intensity weight multiplier based on a reference intensity value of the data points and an average intensity value of the data points, and determining a quality score of the set of data points by applying the intensity weight multiplier to the spatial autocorrelation of the set of data points; and identifying, based on the quality score, whether the set of data points includes one or more data points that are created by a noise source.

Abstract: An electronic device includes a Lidar sensor, a radar sensor, and a processor. The processor is configured to identify one or more objects from Lidar scans. The processor is configured to transmit, via the radar sensor, radar signals for object detection based on reflections of the radar signals received by the radar sensor. While the electronic device travels the area, the processor is configured to generate a first map indicating one or more objects within an area based on the Lidar scans and a second map based on the radar signals. The processor is configured to determine whether the second map indicates a missed object at the portion of the first map that is unoccupied. In response to a determination that the second map indicates the missed object, the processor is configured to modify the first map with the missed object.

Abstract: An example sensor system for use with a vehicle includes a first group of sensing devices that are associated with an essential level of autonomous operation of the vehicle. The first group of sensing devices are configured and intended for continuous use while the vehicle is being autonomously operated. The sensor system further includes a second group of sensing devices that are associated with a non-essential level of autonomous operation of the vehicle. For example, the second group of sensing devices are configured to be redundant to and/or to enhance the performance of the first group of sensing devices. The second group operates in response to certain conditions being determined during autonomous operation of the vehicle. The sensor system further includes a plurality of assemblies attached to the vehicle. Each assembly includes two or more sensing devices from the first group and/or the second group.

Abstract: Methods, systems, apparatus for mitigating adverse effects of road vibrations on radars mounted on a vehicle include an apparatus used for mounting a radar on a rear side of the vehicle. The apparatus includes a base plate, a bracket, one or more fasteners, and one or more washers. The washers may include a vibration dampener material that dampens horizontal and vertical vibration force.

Abstract: A system installed in a vehicle includes a first group of sensing devices configured to allow a first level of autonomous operation of the vehicle; a second group of sensing devices configured to allow a second level of autonomous operation of the vehicle, the second group of sensing devices including primary sensing devices and backup sensing devices; a third group of sensing devices configured to allow the vehicle to perform a safe stop maneuver; and a control element communicatively coupled to the first group of sensing devices, the second group of sensing devices, and the third group of sensing devices. The control element is configured to: receive data from at least one of the first group, the second group, or the third group of sensing devices, and provide a control signal to a sensing device based on categorization information indicating a group to which the sensing device belongs.

Abstract: Disclosed are devices, systems and methods for compensating radar measurements of a vehicle. One exemplary method includes generating a set of velocity hypotheses of a target object based on a first measurement data obtained from sensors mounted on the vehicle; generating cluster velocity estimates by applying a clustering algorithm to a second measurement data obtained from the sensors; and providing one or more selected velocity hypotheses from the set of velocity hypothesis as compensated radar measurements for the target object based on the cluster velocity estimates.

Abstract: A system and method providing truck-mounted sensors to detect trailer following vehicles and trailer conditions are disclosed. A system of an example embodiment comprises: a vehicle control subsystem installed in an autonomous truck, the vehicle control subsystem comprising a data processor; and a truck-mounted sensor subsystem installed on a portion of a tractor of the autonomous truck to which a trailer is attachable, the truck-mounted sensor subsystem being coupled to the vehicle control subsystem via a data connection, wherein the truck-mounted sensor subsystem is configured to emit electromagnetic waves propagating in a space under the trailer, to generate object data representing objects detected by receiving a reflection of the electromagnetic waves, and to transfer the object data to the vehicle control subsystem.

Abstract: A method termed “superacid-surfactant exchange” (S2E) for the dispersion of carbon nanomaterials in aqueous solutions. This S2E method enables nondestructive dispersion of carbon nanomaterials (including single-walled carbon nanotubes, double-walled carbon nanotubes, multi-wall carbon nanotubes, and graphene) at rapidly and at large scale in aqueous solution without a requirement for expensive or complicated equipment. Dispersed carbon nanotubes obtained from this method feature long length, low defect density, high electrical conductivity, and in the case of semiconducting single-walled carbon nanotubes, bright photoluminescence in the near-infrared.

Abstract: An electronic device includes a Lidar sensor, a radar sensor, and a processor. The processor is configured to identify one or more objects from Lidar scans. The processor is configured to transmit, via the radar sensor, radar signals for object detection based on reflections of the radar signals received by the radar sensor. While the electronic device travels the area, the processor is configured to generate a first map indicating one or more objects within an area based on the Lidar scans and a second map based on the radar signals. The processor is configured to determine whether the second map indicates a missed object at the portion of the first map that is unoccupied. In response to a determination that the second map indicates the missed object, the processor is configured to modify the first map with the missed object.

Abstract: A method termed “superacid-surfactant exchange” (S2E) for the dispersion of carbon nanomaterials in aqueous solutions. This S2E method enables nondestructive dispersion of carbon nanomaterials (including single-walled carbon nanotubes, double-walled carbon nanotubes, multi-wall carbon nanotubes, and graphene) at rapidly and at large scale in aqueous solution without a requirement for expensive or complicated equipment.