PERSONALIZED VEHICLE LANE CHANGE MANEUVER PREDICTION: abstract simplified (17715011)

• This abstract for appeared for patent application number 17715011 Titled 'PERSONALIZED VEHICLE LANE CHANGE MANEUVER PREDICTION'

Simplified Explanation

The abstract describes a learning-based algorithm that can predict when a driver will change lanes. The algorithm analyzes the driving behaviors of a specific driver and uses that information to make personalized predictions about lane changes. The algorithm has two phases: an offline training phase where a machine learning model is trained using historical driving data, and an online validation phase where real-time driving data is used to predict lane change maneuvers and determine the most likely trajectory based on the driver's preferences.

Original Abstract Submitted

A learning-based lane change prediction algorithm, and systems and methods for implementing the algorithm, are disclosed. The prediction algorithm evaluates the driving behaviors of a target human driver and predicts lane change maneuvers based on those personalized driving behaviors. The algorithm may include an online lane change decision prediction phase and an offline prediction training and cost function recovery phase. During the offline training phase, a machine learning model may be trained based on historical vehicle states. During the online validation phase, driving data may be collected and fed to the trained model to predict a driver's lane change maneuver, identify potential vehicle trajectories, and determine a most probable vehicle trajectory based on a driver's cost function recovered during the offline phase.