Method and system for personalized driving lane planning in autonomous driving vehicles

A non-transitory, processor-readable medium storing instructions to cause a processor to:

• receive a first set of images generated from real time visual sensor data and depicting a ground surface on which an autonomous vehicle is positioned;
• detect, in real time and based on the first set of images, marks on the ground surface representing a lane that the autonomous vehicle is currently occupying based on at least one lane detection model obtained via machine learning;
• receive data representing a presence of a passenger within the autonomous vehicle;
• select a personalized lane control model from a plurality of personalized lane control models related to the passenger, wherein each personalized lane control model from the plurality of personalized lane control models is trained via machine learning based on recorded human driving data related to the passenger under different driving conditions;
• generate a planned lane control for the autonomous vehicle based on the lane that the autonomous vehicle is currently occupying and self-aware capability parameters associated with the selected personalized lane control model, the generating the planned lane control including using the self-aware capability parameters to predict an operational capability of the autonomous vehicle with respect to a current location of the autonomous vehicle;
• and
• send a signal to cause implementation of the planned lane control at the autonomous vehicle.

A system, comprising:

• a driving lane detector implemented by a processor and configured to:
• receive a first set of images generated from real time visual sensor data and depicting a ground surface on which an autonomous vehicle is positioned,
• detect, in real time and based on the first set of images, marks on the ground surface representing a lane that the autonomous vehicle is currently occupying based on at least one lane detection model obtained via machine learning;
• and
• a driving lane planning unit implemented by the processor and configured to
• receive data representing a presence of a passenger within the autonomous vehicle,
• select a personalized lane control model from a plurality of personalized lane control models related to the passenger, wherein each personalized lane control model from the plurality of personalized lane control models is trained via machine learning based on recorded human driving data related to the passenger under different driving conditions,
• generate a planned lane control for the autonomous vehicle based on the lane that the autonomous vehicle is currently occupying and self-aware capability parameters associated with the selected personalized lane control model, the generating the planned lane control including using the self-aware capability parameters to predict an operational capability of the autonomous vehicle with respect to a current location of the autonomous vehicle;
• and
• send a signal to cause implementation of the planned lane control at the autonomous vehicle.