Detection of loss-of-control vulnerable road users in automotive environments

A system comprising:

• a sensing system of an autonomous vehicle, the sensing system to collect sensing data for an environment of the autonomous vehicle;
• and
• a perception system of the autonomous vehicle, the perception system to:
• process the sensing data by one or more machine learning models to:
• identify a plurality of reference points associated with a vulnerable road user (VRU) in the environment;
• determine a prone state score for the VRU, wherein the prone state score characterizes a likelihood that the VRU is in a prone position or transitioning to the prone position;
• and
• identify one or more height differentials for the plurality of reference points;
• determine that the VRU is at risk of loss of control, based on an acceleration of the VRU and at least one of:
• a change of the one or more height differentials;
• or
• the prone state score;
• and
• cause a control system of the autonomous vehicle to perform an avoidance action.

A method comprising:

• collecting, using a sensing system of an autonomous vehicle, sensing data for an environment of the autonomous vehicle;
• processing the sensing data by one or more machine learning models to identify a plurality of reference points associated with a vulnerable road user (VRU) in the environment;
• identifying one or more height differentials for the plurality of reference points;
• determining that the VRU is at risk of loss of control, based at least on a change of the one or more height differentials;
• and
• causing a control system of the autonomous vehicle to perform an avoidance action.