Methods and systems are provided for navigating a mobile platform in an environment. A processor obtains information about an object in the environment, obtains information about a first satellite, and estimates a probability indicator for a non-line of sight signal transmission between a current satellite location of the first satellite and a current location of the mobile platform using the information about the first satellite and the information about the object. The processor further determines a discrepancy indicator using a movement information of the mobile platform and a movement information of the first satellite such that a weighting indicator can be determined using the estimated probability indicator and the determined discrepancy indicator. The processor then assigns a weighting indicator to a satellite signal transmitted from the first satellite in order to provide a first weighted signal for navigating the mobile platform.

A proactive pedal algorithm is used to modify an accelerator pedal map to ensure the deceleration when the accelerator pedal is released matches driver expectation. Modifying the accelerator pedal map provides the driver of a vehicle the sensation that the vehicle resists moving when travelling in dense scenes with potentially high deceleration requirements and coasts easily in scenes with low deceleration requirements. The accelerator pedal map is modified based on a scene determination to classify other remote vehicles as in-lane, neighbor-lane, or on-coming.

The control apparatus (200) is an apparatus that controls the travel of a target vehicle (10). The control apparatus (200) detects a partial object (20) using a sensor. The partial object (20) is a part of an object riding on the target vehicle (10) and is jutting outside the target vehicle (10). Furthermore, the control apparatus (200) outputs information (travel information) relating to travel of the target vehicle (10) based on the detected partial object (20). For example, the travel information is control information for travel control of the target vehicle (10) and notification information indicating information relating to the partial object (20).

An autonomous vehicle (an AV, or manual vehicle in an autonomous or semi-autonomous mode) includes the ability to sense a command from a source external to the vehicle and modify the behavior of the vehicle in accordance with the command. For example, the vehicle may visualize a police officer or other person associated with traffic control and interpret gestures made by the person causing the vehicle to stop, slow down, pull over, change lanes, back up or take a different route due to unplanned traffic patterns such as accidents, harsh weather, road closings or other situations. The system and method may also be used for non-emergency purposes, including external guidance for load pick-up/placement, hailing a vehicle used as a cab, and so forth. The command may further be spoken or may include a radio frequency (RF) light or other energy component.

Disclosed herein are an autonomous driving method for avoiding a stopped vehicle and an apparatus for the same. The autonomous driving method for avoiding a stopped vehicle is performed by an autonomous driving control apparatus provided in an autonomous vehicle, and includes obtaining taillight recognition information for a stopped vehicle identified ahead of the autonomous vehicle, determining whether the stopped vehicle is to be avoided in consideration of the taillight recognition information, when it is determined that the stopped vehicle is to be avoided, setting an avoidance method in consideration of whether lane returning is to be performed, which is determined based on an autonomous driving task, and setting an avoidance time point corresponding to the avoidance method and controlling the autonomous vehicle to avoid the stopped vehicle by traveling along an avoidance path generated in conformity with the avoidance time point.

A driver assistance system for an ego vehicle, and a method for a driver assistance system is provided. The system is configured to refine a coarse geolocation method based on the detection of the static features located in the vicinity of the ego vehicle. The system performs at least one measurement of the visual appearance of each of at least one static feature located in the vicinity of the ego vehicle. Using the at least one measurement, a position of the ego vehicle relative to the static feature is calculated. The real world position of the static feature is identified. The position of the ego vehicle relative to the static feature is calculated, which is, in turn, used to calculate a static feature measurement of the vehicle location. The coarse geolocation measurement and the the static feature measurement are combined to form a fine geolocation position. By combining the measurements, a more accurate location of the ego vehicle can be determined.

The present invention relates to a system, method, infrastructure, and vehicle for performing automated valet parking. The present disclosure enables an unmanned vehicle to autonomously move to and park at an empty parking space by communicating with a parking infrastructure. The present disclosure enables an unmanned vehicle to autonomously move from a parking space to a pickup zone by communicating with a parking infrastructure.

Surface penetrating radar interrogates a region adjacent a pathway of the vehicle in response to activation by a user. An object detection system which is responsive to the radar transceiver is configured to recognize one or more spatial signatures of one or more detected objects in the region. A controller coupled to the radar transceiver and the object detection system is configured to (i) compare a respective spatial signature of at least one of the detected objects to a plurality of predetermined target signatures to detect an infrastructure asset, (ii) assess a perimeter around the detected infrastructure asset to estimate a severity of an obstruction blocking the infrastructure asset, and (iii) convey an alert message to the user when the estimated severity is greater than a threshold.

Aspects of the disclosed technology provide solutions for performing odometry and in particular, for performing odometry by filtering moving objects from a scene using sensor data. In some aspects, a process can include steps for receiving a first set of sensor data corresponding with a plurality of objects in a scene, determining one or more moving objects and one or more stationary objects from among the plurality of objects, and receiving a second set of sensor data. In some aspects, the process can further include steps for filtering the second set of sensor data to remove data associated with the one or more moving objects and generating odometry data associated with the filtered second set of sensor data. Systems and machine-readable media are also provided.

Provided is a method and apparatus with driving control. A method includes estimating a predicted route of a vehicle based on odometry information of the vehicle and location information of the vehicle, based on road map information, determining a target zone corresponding to the predicted route, and generating driving control information of the vehicle based on an object detection result for the determined target zone.