OBJECT DETECTION BASED ON LIDAR INTENSITY
FIG1 is a block diagram illustrating an example autonomous vehicle (AV) system, according to some embodiments
With reference to FIG1, an example autonomous vehicle (AV) system 100 is illustrated, according to some embodiments. To avoid obscuring the inventive subject matter with unnecessary detail, various functional components that are not germane to conveying an understanding of the inventive subject matter have been omitted from FIG1. However, a skilled artisan will readily recognize that various additional functional components may be included as part of the AV system 100 to facilitate additional functionality that is not specifically described herein. As illustrated in FIG1, the vehicle computing system 102 can include one or more computing devices that assist in controlling the AV system 100. The vehicle computing system 102 can include a localizer system 106, a perception system 108, a prediction system 110, a motion planning system 112, and a reflectance inference system 120 that cooperate to perceive the dynamic surrounding environment of the AV system 100 and determine a trajectory describing a proposed motion path for the AV system 100. The vehicle computing system 102 can additionally include a vehicle controller 114 configured to control the one or more vehicle controls 116 (e.g., actuators that control gas flow (propulsion), steering, braking, etc.) to execute the motion of the AV system 100 to follow the trajectory.
block diagram
122
map data
124
positioning system
1
into two phases -
100
system
104
sensors
118
Lidar unit
102
vehicle computing system
108
perception system
106
localizer system
110
prediction system
114
vehicle controller
120
system
['1. An autonomous vehicle "AV" system comprising: one or more processors of a machine; and a machine-storage medium storing instructions that, when executed by the one or more processors, cause the machine to perform operations comprising: accessing an incoming data point output by a light detection and ranging (Lidar) unit during operation of the AV system; inferring, using a reflectance inference model, a reflectance value of an object based on the incoming data point, the reflectance inference model comprising a mapping of a set of previously collected data points to a coordinate system using range and raw intensity values of the previously collected data points as coordinates, the reflectance value being interpolated from at least two previously collected data points of the set of previously collected data points mapped to the coordinate system, each of the two previously collected data points corresponding to one of multiple known reflectance values; and determining one or more characteristics of the object based on the inferred reflectance value.', '3. The AV system of claim 1 or claim 2, wherein: the reflectance inference model comprises a look-up table comprising a plurality of reflectance values interpolated from the set of previously collected data points mapped to the coordinate system, each reflectance value corresponding to a particular combination of range and raw intensity values; and the inferring of the reflectance value of the object comprises: accessing the look-up table; and determining the reflectance value from the look-up table based on a raw intensity value and a range value of the incoming data point; and/or the Lidar unit comprises a plurality of channels, each channel being capable of operating at a plurality of power levels; the reflectance inference model comprises multiple mappings of the previously collected data points to the coordinate system, each of the mappings corresponding to a particular power level of a particular channel of the Lidar unit; and the inferring of the reflectance value of the object comprises selecting the mapping of the set of previously collected data points from the multiple mappings based on a channel of the Lidar unit that output the incoming data point and a power level of the channel; and/or wherein the operations further comprise generating state data that describes the object, the state data comprising at least the inferred reflectance value; and/or wherein the operations further comprise: generating a reflectance map that includes an indication of the inferred reflectance value of the object; and performing vehicle localization based on the reflectance map. and/or wherein the operations further comprise: collecting a data set comprising data points output by the Lidar unit, each of the data points corresponding to one of multiple targets, each target having one of the multiple known reflectance values, the data set corresponding to the set of previously collected data points; and generating the reflectance inference model for the Lidar unit based on the data set, the generating of the reflectance inference model comprising mapping the set of previously collected data points to the coordinate system.', '6. The Lidar calibration system of claim 5, wherein the operations further comprise: compressing the look-up table to generate a compressed look-up table; and storing the compressed look-up table in a memory device of a vehicle computing system.']