In order to determine navigation instructions, a mapping server can use digital map data, which can include a set of intersections connected by road segments. In some cases, the mapping server generates navigation guidance data for one or more intersections of digital map data. Responsive to a request for navigation instructions from a client device, the mapping server can generate a navigation route between an origin and destination location, and a corresponding set of navigation instructions can be generated based on the navigation route. In some implementations, the generated navigation instructions are then modified to clarify or otherwise improve the quality of the navigation instructions. Finally, the finalized navigation instructions can be sent to a client device for display to the requesting user.

A vehicle position estimation device includes an external information acquisition unit, a vehicle parameter acquisition unit, a map data acquisition unit, a lane change determination unit, and a position estimation unit. The external information acquisition unit acquires external information. The vehicle parameter acquisition unit acquires a vehicle parameter related to traveling. The map data acquisition unit acquires map data. The lane change determination unit determines whether the vehicle is changing lanes. The position estimation unit estimates a self-position of the vehicle on a map. When the lane change determination unit determines that the vehicle is changing lanes, the position estimation unit estimates the self-position using dead reckoning. When the lane change determination unit determines that the vehicle is not changing lanes, the position estimation unit estimates the self-position based on the external information and the map data.

A navigation system for a host vehicle may include a processing device including circuitry and a memory storing instructions that when executed by the circuitry cause the at least one processing device to receive images acquired by a camera representative of an environment of the host vehicle, and analyze the images to identify a double merge scenario including a first flow of traffic and a second flows of traffic in a same direction that merge to form a merged flow of traffic in a merged lane. The instructions that when executed by the circuitry may further cause the processing device to cause a navigational change in the host vehicle based on a trajectory of a first target vehicle in the first flow of traffic and a trajectory of a second target vehicle in the second flow of traffic.

A system includes a support, a sensor coupled to the support and arranged to output signals in a direction towards a haul road on which a machine traverses, an indicator coupled to the support, a first line coupled to the support, and a second line coupled to the support. Sensor data is received from the sensor, and based on the sensor data, a position of the machine on the haul road is determined. An alignment of the machine on the haul road is determined, where the alignment is associated with the machine maintaining contact with the first line and the second line. An indication is displayed for laterally aligning the machine on the haul road.

This document describes techniques for localizing a vehicle to map data, for example, to enable assisted driving and automated driving within a road. A road model builder is described that uses vehicle location data to determine its position relative to map data obtained from a provider, which can be different than the location data's source. Using the vehicle position, the road model builder maintains a current lane segment for the vehicle and its relative position within the current lane segment. Based on its relative position, the road model builder determines whether a transition to another lane segment is appropriate before setting the current lane segment to be the other lane segment. The current lane segment, when provided as an input to an assisted-driving or automated-driving system, can enable safer operation of the vehicle and use fewer computing resources than other systems.

Using a planning circuit of a vehicle, a map is accessed that includes information identifying at least one lane on which vehicles can travel. Using the planning circuit and from the map, a graph representing a driving environment of the vehicle is generated. The graph includes multiple trajectories. At least one trajectory includes a lane change. Each trajectory is a path for the vehicle to move from a first spatiotemporal location to a second spatiotemporal location. The trajectory includes at least one lane alone which the vehicle can move. Using the planning circuit, a trajectory of the multiple trajectories for the vehicle to travel is selected based on an initial vehicle trajectory of the vehicle. The selected trajectory includes a stem. The stem is a portion of the selected trajectory to which the vehicle is configured to adhere. Using the control circuit, the vehicle is moved along the selected trajectory.

The vehicle lane changing trajectory planning method includes: determining a starting point and an end point of a to-be-planned path on a grid lane changing map, which is a gridded local map reflecting a lane changing condition and takes a road boundary as a map boundary; performing weight allocation on all grids in the grid lane changing map by using a potential function; searching for optimal solutions corresponding to different weight requirements between the starting point and the end point; smoothing the optimal solutions corresponding to the different weight requirements to obtain lane changing trajectories corresponding to the different weight requirements; and selecting an optimal lane changing trajectory from the lane changing trajectories corresponding to the different weight requirements.

A method of outputting a prompt information, a device, a medium, and a vehicle, which relate to a field of artificial intelligence, in particular to a field of assisted driving, a field of intelligent transportation and a field of computer vision. The method of outputting the prompt information may include: determining a type of an auxiliary prompt information in response to a determination that the auxiliary prompt information is required to be output, wherein the determination that the auxiliary prompt information is required to be output is performed according to a navigation information and an environment information; determining an output time of the auxiliary prompt information according to the type of the auxiliary prompt information; and outputting the auxiliary prompt information in response to the output time being reached.

Aspects of the disclosure provide a method of identifying off-road entry lane waypoints. For instance, a polygon representative of a driveway or parking area may be identified from map information. A nearest lane may be identified based on the polygon. A plurality of lane waypoints may be identified. Each of the lane waypoints may correspond to a location within at least one lane. The polygon and the plurality of lane waypoints may be input into a model. A lane waypoint of the plurality of lane waypoints may be selected as an off-road entry lane waypoint. The off-road entry lane waypoint may be associated with the nearest lane. The association may be provided to an autonomous vehicle in order to allow the autonomous vehicle to use the association to control the autonomous vehicle in an autonomous driving mode.

Systems and methods of processing crowdsourced navigation information for use in autonomous vehicle navigation are disclosed. A method may include processing, by a mapping server, crowdsourced navigation information from a plurality of vehicles obtained by sensors coupled to the plurality of vehicles, wherein the navigation information describes road lanes of a road segment; collecting data about landmarks identified proximate to the road segment, the landmarking including a traffic sign; generating, by the mapping server, an autonomous vehicle map for the road segment, wherein the autonomous vehicle map includes a spline corresponding to a lane in the road segment and the landmarks identified proximate to the road segment; and distributing, by the mapping server, the autonomous vehicle map to an autonomous vehicle for use in autonomous navigation over the road segment.