Vehicle sensor point cloud probability density function estimation based on vision sensor data

A method comprising:

• receiving vision sensor data;
• processing the vision sensor data to produce an estimated object location sensor point cloud distribution;
• receiving a general sensor point cloud distribution corresponding to the vision sensor data;
• comparing the general sensor point cloud distribution with the estimated object location sensor point cloud distribution;
• identifying a vehicle, an environmental object, or a ghost object in the general sensor point cloud distribution based on the comparison;
• and
• modifying the estimated object location sensor point cloud distribution or the general sensor point cloud distribution based on the comparison.

A non-transitory computer readable medium embodying a set of executable instructions, the set of executable instructions to manipulate at least one processor to:

• receive vision sensor data;
• process the vision sensor data to produce an estimated object location sensor point cloud distribution;
• receive a general sensor point cloud distribution corresponding to the vision sensor data;
• compare the general sensor point cloud distribution with the estimated object location sensor point cloud distribution;
• identify a vehicle, an environmental object, or a ghost object in the general sensor point cloud distribution based on the comparison;
• and
• modify the estimated object location sensor point cloud distribution or the general sensor point cloud distribution based on the comparison.

A method comprising:

• receiving vision sensor data;
• processing the vision sensor data to estimate a vehicle location sensor point cloud distribution, wherein the processing is performed using a deep learning network trainable using only vision sensor training data and other training data corresponding to the vision sensor training data as input training data, and wherein the estimated vehicle location sensor point cloud distribution includes estimated radar points having range, azimuth, elevation, and Doppler information;
• and
• producing the estimated vehicle location sensor point cloud distribution corresponding to a general sensor point cloud distribution, wherein the general sensor point cloud distribution is a radar point distribution that includes radar points having range and azimuth information.