Controlling an automated vehicle using visual anchors, including receiving, from one or more cameras of an autonomous vehicle, first video data; identifying one or more visual anchors in the first video data; determining one or more differentials between the one or more visual anchors and one or more predicted visual anchors; and determining, based on the one or more differentials, one or more control operations for the autonomous vehicle to reduce the one or more differentials.

Examples relating to vehicle velocity calculation using radar technology are described. An example method performed by a computing system may involve, while a vehicle is moving on a road, receiving, from two or more radar sensors mounted at different locations on the vehicle, radar data representative of an environment of the vehicle. The method may involve, based on the data, detecting at least one scatterer in the environment. The method may involve making a determination of a likelihood that the at least one scatterer is stationary with respect to the vehicle. The method may involve, based on the determination being that the likelihood is at least equal to a predefined confidence threshold, calculating a velocity of the vehicle based on the data from the sensors. The calculated velocity may include an angular and linear velocity. Further, the method may involve controlling the vehicle based on the calculated velocity.

An action selection device (10) includes an action selection unit (22). The action selection unit (22) acquires from a memory (30), an action list (31) in which a requirement recognition area is associated with each action of a plurality of actions, the requirement recognition area indicating an area for which recognition by a sensor is required. The action selection unit (22) acquires from a peripheral recognition device (53), a recognition area (53a) recognized by sensors (53-1) that the peripheral recognition device (53) has. The action selection unit (22) selects from the action list (31), an action associated with the requirement recognition area included in the recognition area (53a).

In one embodiment, a method for monitoring a localization function in an autonomous driving vehicle (ADV) can use known static objects as ground truths to determine when the localization function encounter errors. The known static objects are marked on a high definition (HD) map for the real-time driving environment. When the ADV detects one or more known static objects, the ADV can use sensor data, locations of the one or more static objects, and one or more error tolerance parameters to create a localization error tolerance area surrounding a current location of the ADV. The ADV can project the tolerance area on the HD map, performs a localization operation to generate an expected location of the ADV on the HD map, and determines whether the generated location falls within the projected tolerance area. If the generated location falls outside the projected tolerance area, indicating a localization function of the ADV encounter errors, the ADV can generate an alarm to alert a human driver to switch to a manual driving mode. If no human driver is available in the ADV, the ADV can activate a vision-based fail-safe localization procedure.

An inter-vehicle communication and driving assistance device performs wireless communication with other vehicles, and includes an inter-vehicle communication unit including a reception level detection unit, a position information reception unit, and an arithmetic processing unit. The arithmetic processing unit calculates inter-vehicle distances to other vehicles using latitude and longitude information of the other vehicles and the own vehicle, receives a position error radius, and acquires a reception level from the other vehicles. When a difference between the inter-vehicle distances is smaller than position error radius, a vehicle with a larger reception level is determined as a vehicle closer to the own vehicle. When the difference between the inter-vehicle distances is larger than the position error radius, a vehicle with a smaller inter-vehicle distance is determined as a vehicle closer to the own vehicle, and a distance to a leading vehicle is calculated.

A navigational system for a host vehicle may comprise at least one processing device. The processing device may be programmed to receive a first output and a second output associated with a host vehicle, wherein at least one of the outputs is received from a sensor onboard the host vehicle. The processing device may identify a target object in the first output and determine whether a characteristic of the target object triggers a navigational constraint by verifying the identification of the target object based on the first output; and, if the navigational constraint is not verified based on the first output, then verifying the identification of the target object based on a combination of the first output and the second output. In response to the verification, the processing device may cause at least one navigational change to the host vehicle.

A navigational system for a host vehicle may comprise at least one processing device. The processing device may be programmed to receive a first output and a second output associated with the host vehicle and identify a representation of a target object in the first output. The processing device may determine whether a characteristic of the target object triggers a navigational constraint by verifying the identification of the target object based on the first output and, if the at least one navigational constraint is not verified based on the first output, then verifying the identification of the target object based on a combination of the first output and the second output. In response to the verification, the processing device may cause at least one navigational change to the host vehicle.

Disclosed embodiments provide a technical improvement for providing localization for a transportation vehicle by detecting road wear reference lines in a roadway on which the transportation vehicle is travelling and controlling, guiding or otherwise facilitating alignment of the transportation vehicle wheel centers with the detected centers of the road wear.

A system controls a vehicle. The vehicle implements at least one control application using a variable measured by at least one sensor of a perception system installed in the vehicle. The control system includes an adaptive controller to dynamically activate one or more elementary controllers of a set of elementary controllers including at least two elementary controllers, each elementary controller can apply a control function of a vehicle parameter acting on actuators of the vehicle, the control functions being separate, based on a precision indicator of the perception system determined according to a real-time value of the variable.

Aspects of the disclosure relate to detecting unmapped drivable road surfaces. In one instance, sensor data captured by a sensor of an autonomous vehicle may be projected onto a grid having a plurality of cells. The plurality of cells may be classified by generating a label for each of the plurality of cells. Each label may identifies whether or not a corresponding cell contains a drivable surface. Ones of the plurality of cells may be clustered based on the labels to form a cluster of cells. An area of the cluster of cells may be compared to a map. Whether the area of the cluster of cells is an unmapped drivable road surface may be determined based on the comparison.