Camera and radar-based threat monitoring for micromobility platforms

A system for detecting collision threats on a micromobility platform, comprising:

• an imaging device mounted on a micromobility platform;
• a ranging device mounted on the micromobility platform;
• and
• a processing system communicatively coupled with the imaging device and the ranging device and having a memory with executable instructions stored thereon and one or more processors configured to execute the executable instructions to cause the processing system to:
• capture image data from the imaging device and corresponding ranging data from the ranging device;
• identify, based on an object detection machine learning model and the image data, one or more objects to monitor;
• predict, based on processing the image data and the corresponding ranging data through a machine learning model, future motion of the identified one or more objects;
• determine, based on the predicted future motion of the identified one or more objects and a type of the identified one or more objects, that a threatening object from the identified one or more objects is a collision threat to the micromobility platform, wherein to determine that the threating object is a collision threat, the one or more processors are configured to cause the processing system to:
• compare a predicted future location of the threatening object, determined based on the predicted future motion of the threatening object, to a predicted future location of the micromobility platform,
• determine that the threatening object is a collision threat when the predicted future location of the threatening object is within a threshold location of the predicted future location of the micromobility platform, and
• the threshold location comprises a specified distance from the micromobility platform and a lateral range in which a vector points, the vector starting at a prior location associated with the threatening object in the image data and ending at a current location associated with the threatening object in the image data;
• and
• record subsequent image data from the imaging device based on the determination until a threshold amount of time after which the machine learning model determines that the threatening object is no longer a collision threat has elapsed.

A non-transitory computer-readable medium having executable instructions stored thereon which, when executed by one or more processors, causes the one or more processors to perform an operation comprising:

• capturing image data from a first sensor and corresponding ranging data from a second sensor communicatively coupled with a processing system, the first sensor, the second sensor, and the processing system being mounted on a micromobility platform;
• identifying, based on an object detection machine learning model and the image data, one or more objects to monitor;
• predicting, based on processing the image data and the corresponding ranging data through a machine learning model, future motion of the identified one or more objects;
• determining, based on the predicted future motion of the identified one or more objects and a type of the identified one or more objects, that a threatening object from the identified one or more objects is a collision threat to the micromobility platform, wherein determining that the threating object is a collision threat comprises:
• comparing a predicted future location of the threatening object, determined based on the predicted future motion of the threatening object, to a predicted future location of the micromobility platform,
• determining that the threatening object is a collision threat when the predicted future location of the threatening object is within a threshold location of the predicted future location of the micromobility platform, and
• the threshold location comprises a specified distance from the micromobility platform and a lateral range in which a vector points, the vector starting at a prior location associated with the threatening object in the image data and ending at a current location associated with the threatening object in the image data;
• and
• recording subsequent image data from the first sensor based on the determination until a threshold amount of time after which the machine learning model determines that the threatening object is no longer a collision threat has elapsed.