CLASSIFICATION OF ABSORBING TARGETS BY A LIDAR SYSTEM
In various embodiments, a process for classifying absorbing targets by a lidar system includes emitting output beams comprising pulses of light for a region in a field of regard, and detecting received pulses of light associated with at least a portion of the emitted pulses of light for the region. The process includes determining a metric associated with the detected received pulses of light, providing at least a portion of the metric to a trained machine learning model to determine a machine learning output, and classifying a light absorbing blockage associated with the region based on the machine learning output.
1 . A system, comprising:
a light source configured to emit output beams comprising pulses of light for a region in a field of regard;
a receiver configured to detect received pulses of light associated with at least a portion of the emitted pulses of light for the region; and
a processor configured to:
determine a metric associated with the detected received pulses of light;
provide at least a portion of the metric to a trained machine learning model to determine a machine learning output; and
classify a light absorbing blockage associated with the region based on the machine learning output.
2 . The system of claim 1 , wherein the metric includes a measure of observability.
3 . The system of claim 1 , wherein the metric includes an empty rays ratio (ERR), the ERR being based on a ratio of (i) a number of empty rays included in the received pulses of light to (ii) a sum of a total number of the received pulses of light and the number of empty rays.
4 . The system of claim 1 , wherein the metric includes a measure of visibility.
5 . The system of claim 1 , wherein the metric includes a further returns ratio (FRR), the FRR being based on a ratio of (i) a number of the received pulses of light with an index greater than an index threshold to (ii) a total number of the received pulses of light.
6 . The system of claim 5 , wherein the further returns ratio (FRR) is based at least on a reflection coefficient.
7 . The system of claim 5 , wherein the index indicates a respective pulse of light that is received.
8 . The system of claim 1 , wherein the region corresponds to a subset of the field of regard.
9 . The system of claim 1 , wherein determining the metric associated with the detected received pulses of light includes determining at least one metric for each of a plurality of regions.
10 . The system of claim 9 , wherein the plurality of regions includes five regions.
11 . The system of claim 9 , the determined metric is based at least on aggregated metrics for the plurality of regions.
12 . The system of claim 1 , wherein classifying the light absorbing blockage in the region includes classifying the light absorbing blockage as a type of water-based blockage.
13 . The system of claim 12 , wherein classifying the type of water-based blockage as at least one of: rain or ice.
14 . The system of claim 1 , wherein the processor is further configured to track the classification of the light absorbing blockage over time.
15 . The system of claim 1 , wherein the light absorbing blockage includes ice that thaws over time.
16 . The system of claim 15 , wherein:
the metric is based on an empty rays ratio (ERR) and a further returns ratio (FRR); and
a state of the ice that thaws over time is indicated by the empty rays ratio (ERR) decreasing over time and the further returns ratio (FRR) increasing over time.
17 . The system of claim 1 , wherein the machine learning model includes at least one of: a support vector machine (SVM), a support vector classifier (SVC), or a decision tree.
18 . The system of claim 1 , wherein the processor is further configured to output an indication causing at least one of: cleaning at least a portion of the system, waiting for a predetermined period of time, preventing a vehicle from being operable, or heating at least a portion of the system to clear the light absorbing blockage.
19 . A method, comprising:
emitting output beams comprising pulses of light for a region in a field of regard;
detecting received pulses of light associated with at least a portion of the emitted pulses of light for the region; and
determining a metric associated with the detected received pulses of light;
providing at least a portion of the metric to a trained machine learning model to determine a machine learning output; and
classifying a light absorbing blockage associated with the region based on the machine learning output.
20 . A computer program product embodied in a non-transitory computer readable medium and comprising computer instructions for:
emitting output beams comprising pulses of light for a region in a field of regard;
detecting received pulses of light associated with at least a portion of the emitted pulses of light for the region; and
determining a metric associated with the detected received pulses of light;
providing at least a portion of the metric to a trained machine learning model to determine a machine learning output; and
classifying a light absorbing blockage associated with the region based on the machine learning output.