Abstract: A method for predicting lane error based on an identifier of a lane segment is disclosed. The method includes receiving a predicted location in a lane segment of a plurality lane segments for a first vehicle traveling on the lane segment. The method includes receiving a determined location of the first vehicle in the lane segment for the first vehicle. The method includes determining a difference between the predicted location in the lane segment received for the first vehicle and the determined location of the first vehicle in the lane segment. The method includes providing the determined difference between the predicted location in the lane segment received for the first vehicle and the determined location of the first vehicle in the lane segment and an identifier of the lane segment as first training data to a lane error estimation model.

Abstract: System, methods, and other embodiments described herein relate to determining traffic flow along a roadway segment. In one embodiment, a method includes collecting, in an electronic data store from reporting vehicles that travel over the roadway segment, traffic data about at least surrounding vehicles of the reporting vehicles. The method includes analyzing the traffic data to identify a traffic flow for the respective lanes of the roadway segment. The method includes providing a signal identifying the traffic flow.

Abstract: In one embodiment, example systems and methods relate to a manner of providing lane curvature estimations. As vehicles travel, lane curvature estimates for lane segments as provided by the vehicle lane curvature estimation systems are collected. The estimated lane curvatures and the actual paths of the vehicles as they traveled in the lane segments are used as training data for a model that can both determine if a lane curvature estimate for a lane segment is likely incorrect and provide a correct lane curvature estimate. When a vehicle later travels such a lane segment, the model can be used to provide the vehicle with a correct lane curvature estimate. The correct estimate can be used by the vehicle in place of an estimate generated by the existing lane curvature estimation system of the vehicle.

Abstract: In one embodiment, example systems and methods relate to a manner of providing lane curvature estimations. As vehicles travel, lane curvature estimates for lane segments as provided by the vehicle lane curvature estimation systems are collected. The estimated lane curvatures and the actual paths of the vehicles as they traveled in the lane segments are used as training data for a model that can both determine if a lane curvature estimate for a lane segment is likely incorrect and provide a correct lane curvature estimate. When a vehicle later travels such a lane segment, the model can be used to provide the vehicle with a correct lane curvature estimate. The correct estimate can be used by the vehicle in place of an estimate generated by the existing lane curvature estimation system of the vehicle.

Abstract: A method for predicting lane error based on an identifier of a lane segment is disclosed. The method includes receiving a predicted location in a lane segment of a plurality lane segments for a first vehicle traveling on the lane segment. The method includes receiving a determined location of the first vehicle in the lane segment for the first vehicle. The method includes determining a difference between the predicted location in the lane segment received for the first vehicle and the determined location of the first vehicle in the lane segment. The method includes providing the determined difference between the predicted location in the lane segment received for the first vehicle and the determined location of the first vehicle in the lane segment and an identifier of the lane segment as first training data to a lane error estimation model.

Abstract: System, methods, and other embodiments described herein relate to identifying lane obstructions. In one embodiment, a method includes collecting, in an electronic data store, position data of surrounding vehicles observed by reporting vehicles that travel over a roadway segment. The method includes analyzing the position data to identify whether observed positions correlate with an obstruction pattern that is indicative of a lane obstruction in at least one lane of the roadway segment. The method includes, in response to determining the position data indicates the lane obstruction, providing a signal identifying the lane obstruction to oncoming vehicles of the roadway segment.

Abstract: One or more dynamic trailing objects can be detected in an external environment of a vehicle. Position data of the dynamic trailing object(s) can be acquired. It can be determined whether a current position of one of the trailing objects is at substantially the same longitudinal position as a previous position of the ego vehicle. If the current position of the dynamic trailing object is at substantially the same longitudinal position as a previous position of the ego vehicle, a lateral offset between the current position of the trailing object and the previous position of the ego vehicle can be determined. The lateral offset can be used to identify a current travel lane of the ego vehicle, determine lane crossings, and/or determine travel lane probability distributions.

Abstract: System, methods, and other embodiments described herein relate to determining whether lanes in a surrounding environment of an ego vehicle are occupied. In one embodiment, a method includes, in response to detecting a surrounding vehicle from sensor data acquired about the surrounding environment by the ego vehicle, updating a vehicle track for the surrounding vehicle in relation to the ego vehicle. The method includes computing a position probability indicating a probable lateral position of the surrounding vehicle in relation to the ego vehicle as a function of the vehicle track. The method includes generating an occupancy indicator specifying which of the lanes the surrounding vehicle is occupying according to the position probability satisfying an occupancy threshold for a respective one of the lanes.

Abstract: System, methods, and other embodiments described herein relate to identifying lane obstructions. In one embodiment, a method includes collecting, in an electronic data store, position data of surrounding vehicles observed by reporting vehicles that travel over a roadway segment. The method includes analyzing the position data to identify whether observed positions correlate with an obstruction pattern that is indicative of a lane obstruction in at least one lane of the roadway segment. The method includes, in response to determining the position data indicates the lane obstruction, providing a signal identifying the lane obstruction to oncoming vehicles of the roadway segment.

Abstract: System, methods, and other embodiments described herein relate to determining traffic flow along a roadway segment. In one embodiment, a method includes collecting, in an electronic data store from reporting vehicles that travel over the roadway segment, traffic data about at least surrounding vehicles of the reporting vehicles. The method includes analyzing the traffic data to identify a traffic flow for the respective lanes of the roadway segment. The method includes providing a signal identifying the traffic flow.

Abstract: One or more dynamic trailing objects can be detected in an external environment of a vehicle. Position data of the dynamic trailing object(s) can be acquired. It can be determined whether a current position of one of the trailing objects is at substantially the same longitudinal position as a previous position of the ego vehicle. If the current position of the dynamic trailing object is at substantially the same longitudinal position as a previous position of the ego vehicle, a lateral offset between the current position of the trailing object and the previous position of the ego vehicle can be determined. The lateral offset can be used to identify a current travel lane of the ego vehicle, determine lane crossings, and/or determine travel lane probability distributions.