Abstract: A system and a method to create clusters in an area. The system comprises obtaining a plurality of location data points associated with a plurality of entities in an area. It may be noted that each location data point includes geographic coordinates. Further, the system comprises computing a range of location data points required in each cluster. Furthermore, the system comprises forming a farthest point cluster by determining a farthest location data point from a centroid based on an angular distance. It may be noted that the farthest point cluster comprises a set of location data points having a farthest distance lesser than a centroid distance. The system iteratively forms a new farthest point cluster by excluding the set of location data points present in the farthest point cluster from the plurality of location data points.

Abstract: A system and a method to create clusters in an area are disclosed. Initially, a set of location data points associated with a plurality of user devices in an area and a defined block area are received. Further, a rectangular boundary is created by connecting a first reference point, a second reference point, a third reference point, and a fourth reference point identified based on a latitude and a longitude of each user device. Furthermore, a set of blocks are created by dividing the rectangular boundary based on the defined block area. The set of blocks are sorted based on a number of location data points present in each block. Subsequently, the set of blocks are reconfigured by determining a centroid of a plurality of location data points present in each block. Finally, a plurality of clusters is created in the area upon marking the reconfigured set of blocks.

Abstract: In some examples, a system may receive location information. The system may determine at least one landmark based on the location information. In addition, the system may determine one or more current conditions for the at least one landmark. Further, the system may receive sensor configuration information. Based on the sensor configuration information and the one or more current conditions, the system may determine the detectability of the at least one landmark.

Abstract: In some examples, a vehicle suspension for supporting, at least in part, a sprung mass, includes a damper connected to the sprung mass, the damper including a movable piston. The vehicle suspension further includes an actuator and a controller. The controller may be configured to determine a frequency of motion associated with the sprung mass. When the frequency of motion is below a first frequency threshold, the controller may send a control signal to cause the actuator to apply a deceleration force to the sprung mass. Further, when the frequency of motion associated with the sprung mass exceeds the first frequency threshold, the controller may send a control signal to cause the actuator to apply a compensatory force to the sprung mass. For instance, a magnitude of the compensatory force may be based on a friction force determined for the damper.

Abstract: In some examples, a system may receive location information. The system may determine at least one landmark based on the location information. In addition, the system may determine one or more current conditions for the at least one landmark. Further, the system may receive sensor configuration information. Based on the sensor configuration information and the one or more current conditions, the system may determine the detectability of the at least one landmark.

Abstract: To localize a vehicle in the given map precisely for autonomous driving, the vehicle needs to compare the sensor perceived environment with the map information accurately. To achieve this, the vehicle may use information received from multiple on-board sensors in combination of GPS and map data. Example implementations described herein are directed to maintaining accuracy of sensor measurements for autonomous vehicles even during sensor failure through a raw data fusion technique and a repopulation of data through a prediction technique based on historical information and statistical method if any of the sensors fail.

Abstract: In some examples, a vehicle suspension for supporting, at least in part, a sprung mass, includes a damper connected to the sprung mass, the damper including a movable piston. The vehicle suspension further includes an actuator and a controller. The controller may be configured to determine a frequency of motion associated with the sprung mass. When the frequency of motion is below a first frequency threshold, the controller may send a control signal to cause the actuator to apply a deceleration force to the sprung mass. Further, when the frequency of motion associated with the sprung mass exceeds the first frequency threshold, the controller may send a control signal to cause the actuator to apply a compensatory force to the sprung mass. For instance, a magnitude of the compensatory force may be based on a friction force determined for the damper.

Abstract: To localize a vehicle in the given map precisely for autonomous driving, the vehicle needs to compare the sensor perceived environment with the map information accurately. To achieve this, the vehicle may use information received from multiple on-board sensors in combination of GPS and map data. Example implementations described herein are directed to maintaining accuracy of sensor measurements for autonomous vehicles even during sensor failure through a raw data fusion technique and a repopulation of data through a prediction technique based on historical information and statistical method if any of the sensors fail.