A computer-implemented method for predicting properties of a plurality of objects in a vicinity of a vehicle includes multiple steps that can be carried out by computer hardware components. The method includes determining a grid map representation of road-users perception data, with the road-users perception data including tracked perception results and/or untracked sensor intermediate detections. The method also includes determining a grid map representation of static environment data based on data obtained from a perception system and/or a pre-determined map. The method further includes determining the properties of the plurality of objects based on the grid map representation of road-users perception data and the grid map representation of static environment data.

A system for associating perceived and mapped lane edges can include a processor and a memory. The memory includes instructions such that the processor is configured to receive a sensor data representing a perceived object; receive map data representing a map object; determine a cost matrix a cost matrix indicative of an association cost for associating the map object to the perceived object; compare the association cost with an association cost threshold; and associate the perceived object with the map object based on the association cost.

An embodiment terminal system for a taxi vehicle includes a first terminal positioned for a passenger of the taxi vehicle, and a second terminal positioned for a driver of the taxi vehicle and configured to display a first communication user interface (UI) for receiving information to be transferred from the driver to the passenger on a first screen, and in response to any one of first options included in the first communication UI being selected, to transmit first message information that is determined based on the selected any one first option to the first terminal, wherein the first options included in the first communication UI are chosen based on driving status information of the taxi vehicle, a boarding type of the passenger, or surrounding environment information of the taxi vehicle.

Systems and methods in accordance with aspects of this disclosure may be provided to determine and calculate an overall driving risk index value corresponding to a driver and scene configuration. The overall driving risk index value may provide driving risk modeling and estimation at a personalized driving level. In some cases, the overall driving risk index value may be determined using a risk-predictive modeling system with weighting and machine learning and may include one or more of: a driver score system, a driver-contextual risk score system, and a conflict index system.

A curbside management system and method for managing curbside use by connected and/or autonomous vehicles that includes one or more computing devices that execute instructions. One or more processors integrate at least a navigation component, an asset verification component, and a curbside management component, configured to generate recommended curbside access destinations for a vehicle associated with the customer and one of which is selected as the final curbside destination for the vehicle.

An apparatus for displaying a virtual lane may include: a processor to generate the virtual lane by converting lane information of a preceding vehicle into lane information viewed in a viewpoint of a host vehicle, in platooning, and a display to display the virtual lane.

Systems and methods are disclosed for generating a display of a navigation map. The system may comprise a historical data source device having, for example, a historical data source computer and a database storing historical data associated with one or more of vehicle accident data, traffic data, vehicle volume data, vehicle density data, road characteristic data, or weather data. The system may comprise a map data processing device having a map data processing computer and memory storing computer-executable instructions that, when executed by the map data processing computer, cause the map data processing device to, for example, determine, based on a location determining device, a location of a vehicle. The map data processing system may determine one or more historical factors based on the location of the vehicle. The map data processing system may receive, from the historical data source device and for the location, historical data associated with the one or more historical factors. Based on the location of the vehicle, one or more real time factors and real time data associated with the one or more real time factors may be calculated. The map data processing system may calculate, using the one or more historical factors and the one or more real time factors, a navigation score for each segment of a route from the location to a destination location. The map data processing system may determine one or more colors for each navigation score and/or generate a display of a navigation map comprising the one or more colors.

Aspects of the subject disclosure may include, for example, determining, by a system comprising a processor, a driver profile according to a driver identity for a driver of a vehicle, selecting a driver-specific enforcement scenario for the vehicle according to the driver profile and traffic enforcement information that is associated with a vehicle location, and presenting an in-vehicle alert to convey the driver-specific enforcement scenario to the driver. Other embodiments are disclosed.

Disclosed are systems, methods, and non-transitory computer-readable media for automated fleet tracking. A route management system enables fleet managers to define and assign routes for vehicles in a fleet, as well as set route tracking configurations for customized tracking of the vehicles. For example, the route tracking configuration may include customizations to the scheduled start and/or end time of a route, a threshold for determining that a vehicle has arrived and/or departed from a scheduled stop, and the like.

Techniques to predict object behavior in an environment are discussed herein. For example, such techniques may include determining a trajectory of the object, determining an intent of the trajectory, and sending the trajectory and the intent to a vehicle computing system to control an autonomous vehicle. The vehicle computing system may implement a machine learned model to process data such as sensor data and map data. The machine learned model can associate different intentions of an object in an environment with different trajectories. A vehicle, such as an autonomous vehicle, can be controlled to traverse an environment based on object's intentions and trajectories.