Map matching on low sampling rate trajectories through deep inverse reinforcement learning and multi-intention modeling

A method for training a multi-intention inverse reinforcement learning model comprising:

• obtaining driving data and known driving paths of a plurality of vehicles on a road network, the driving data comprising known location data and known road segment features;
• applying an expectation-maximization model to categorize two or more sets of the driving data to two or more clusters, respectively;
• and
• training policies for each of the two or more clusters using an inverse reinforcement learning (IRL) model, wherein the IRL model comprises:
• the known road segment features as inputs to the reward function for each of the clusters, and
• the known driving paths corresponding to the driving as expert behavior.

A non-transitory computer-readable medium storing instructions which, when executed, cause one or more processors to perform operations comprising:

• I. obtaining location data of a vehicle on a road network;
• II. using a classifier to classify the location data to a cluster for generating classified location data;
• and
• III. determining a driving path of the vehicle on the road network based on the classified location data by:
• determining possible driving paths on the road network between at least two data points of the location data, and
• applying a policy of the cluster to select the driving path from the possible driving paths;
• wherein:
• the classifier is a trained machine learning model that is trained on known driving data labelled with cluster labels of clusters;
• the known driving data comprises known location data and known road segment features;
• each of the clusters comprises a set of the known driving data and a reward function that are determined by:
• training policies for the clusters using an inverse reinforcement learning (IRL) model, wherein the IRL model comprises:
• the known road segment features as inputs to the reward function for each of the clusters, and
• known driving paths corresponding to the known driving location as expert behavior;
• and
• the clusters and each set of the known driving data corresponding to the clusters are labelled with the cluster labels.