Gm Global Technolgoy Operations, Inc.

Aravindhan Mani

Lawrence A Bush

Upali Priyantha Mudalige

Michael A Losh

Guangyu J Zou

Systems and methods of detecting a traffic object outside of a vehicle and controlling the vehicle. The systems and methods receive perception data from a sensor system included in the vehicle, determine a focused Region Of Interest (ROI) in the perception data, scale the perception data of the at least one focused ROI, process the scaled perception data of the focused ROI using a neural network (NN)-based traffic object detection algorithm to provide traffic object detection data, and control at least one vehicle feature based, in part, on the traffic object detection data.

Systems and methods for detecting traffic objects

A system for generating a map includes a processing device configured to determine a provenance of each received map of a plurality of maps, parse each received map into objects of interest, and compare the objects of interest to identify one or more sets of objects representing a common feature. For each set of objects, the processing device is configured to select a subset of the set of objects based on the provenance associated with each object in the set of objects, and calculate a similarity metric for each object in the subset. The similarity metric is selected from an alignment between an object and a reference object in the subset, and/or a positional relationship between the object and the reference object. The processing device is configured to generate a common object representing the common feature based on the similarity metric, and generate a merged map including the common object.

Combining heterogeneous types of maps

Systems and methods are provided for interpreting traffic information by a vehicle. In one embodiment, a method includes: receiving, by a processor, sensor data from one or more sensing devices of the vehicle, where the sensor data depicts a traffic device in an environment of the vehicle; receiving, by the processor, map data associated with the environment of the vehicle, where the map data includes traffic devices; matching, by the processor, the traffic device of the sensor data with a traffic device of the map data; determining, by the processor, a probability distribution of a traffic signal associated with the matched traffic devices based on a Hidden Markov model (HMM); and planning, by the processor, control of the vehicle based on the probability distribution.

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Troy, MI, United States of America

Aravindhan Mani