Autonomous vehicle
An autonomous vehicle includes one or more sensors that measures the distance to and/or other properties of a target by illuminating the target with light. The sensor(s) provides sensor data indicative of one or more surface manifolds. Point data is generated with respect to the surface manifold(s). AHaH (Anti-Hebbian and Hebbian) based mechanism performs an AHaH-based feature extraction operation on the point data for compression and processing thereof.
1. An autonomous vehicle, comprising:
at least one sensor associated with said autonomous vehicle, wherein said at least one sensor measures a distance to and/or other properties of a target by illuminating said target with light, wherein said at least one sensor provides sensor data indicative of at least one surface manifold and wherein point data is generated with respect to said at least one surface manifold; and
an AHaH (Anti-Hebbian and Hebbian) based mechanism that performs an AHaH-based feature extraction operation on said point data for compression and processing thereof, wherein at least one voxel of a point cloud composed of said point data is scanned to recover said compressed binary label which represents prototypical surface patches with respect to said at least one surface manifold.
2. The autonomous vehicle of claim 1 wherein said at least one sensor comprises at least one of:
a LIDAR (Light Detection and Ranging) device;
a digital camera;
a coherent optical sensor; and
a camera-radar fusion unit.
3. The autonomous vehicle of claim 1 wherein the at least one sensor comprises a LIDAR (Light Detection and Ranging) device.
4. The autonomous vehicle of claim 3 wherein said point data comprises LIDAR point data and said surface manifolds comprises LIDAR surface manifolds.
5. The autonomous vehicle of claim 4 wherein said LIDAR point data is represented as the at least one voxel with at least one active grid element and at least one inactive grid element, wherein a voxel grid element is considered active when said voxel grid element contains within a spatial boundary thereof at least one LIDAR point.
6. The autonomous vehicle of claim 5 wherein said point data comprises a sparse binary activation vector including grid elements that are active.
7. The autonomous vehicle of claim 1 further comprising an AHaH based mechanism acting on a plurality of synaptic weights.
8. The autonomous vehicle of claim 7 wherein each synaptic weight among said plurality of synaptic weights comprises at least one memristor.
9. The autonomous vehicle of claim 7 wherein each synaptic weight among said plurality of synaptic weight comprises a differential pair of memristors formed from at least one of three electrode configurations including a 1-2 electrode configuration, a 2-1 electrode configuration, or a 2-2 electrode configuration.
10. The autonomous vehicle of claim 7 wherein said AHaH based mechanism acts on at least one memristor composed of said plurality of meta-stable switches.
11. The autonomous vehicle of claim 1 wherein said AHaH based mechanism comprises at least one AHaH node comprising a plurality of synaptic weights formed of memristors.
12. The autonomous vehicle of claim 11 wherein said synaptic weights are further formed of three electrode configurations including a 1-2 electrode configuration, a 2-1 electrode configuration, or a 2-2 electrode configuration.
13. An autonomous vehicle, comprising:
at least one sensor associated with said autonomous vehicle, wherein said at least one sensor measures a distance to and/or other properties of a target by illuminating said target with light, wherein said at least one sensor provides sensor data indicative of at least one surface manifold and wherein point data is generated with respect to said at least one surface manifold; and
an AHaH (Anti-Hebbian and Hebbian) based mechanism that performs an AHaH-based feature extraction operation on said point data for compression and processing thereof, said AHaH based mechanism acting on a plurality of synaptic weights, said synaptic weights formed of memristors.
14. The autonomous vehicle of claim 13 wherein each synaptic weight of said synaptic weights is formed from the memristors comprising a differential pair of memristors.
15. The autonomous vehicle of claim 13 wherein each synaptic weight of said synaptic weights is formed from at least one of three electrode configurations including a 1-2 electrode configuration, a 2-1 electrode configuration, or a 2-2 electrode configuration.
16. An autonomous vehicle, comprising:
at least one sensor associated with said autonomous vehicle, wherein said at least one sensor measures a distance to and/or other properties of a target by illuminating said target with light, wherein said at least one sensor provides sensor data indicative of at least one surface manifold and wherein point data is generated with respect to said at least one surface manifold; and
a liquid state machine forming spatial-temporal encoding of said point data; and
an AHaH (Anti-Hebbian and Hebbian) based mechanism that performs an AHaH-based feature extraction operation on said spatial-temporal encoding for compression and processing thereof, wherein said AHaH based mechanism acts on synaptic weights formed from memristors.
17. The autonomous vehicle of claim 16 wherein the at least one sensor comprises a LIDAR (Light Detection and Ranging) device.
18. The autonomous vehicle of claim 16 wherein said synaptic weights formed from memristors are formed from at least one of three electrode configurations including a 1-2 electrode configuration, a 2-1 electrode configuration, or a 2-2 electrode configuration.
19. The autonomous vehicle of claim 16 wherein said at least one sensor comprises at least one of:
a LIDAR (Light Detection and Ranging) device;
a digital camera;
a coherent optical sensor; and
a camera-radar fusion unit.
20. The autonomous vehicle of claim 16 wherein said at least one sensor comprises at least one of: a digital camera and a coherent optical sensor.