An autonomous vehicle having a LIDAR system that scans a field of view is described herein. With more specificity, a computing system of the autonomous vehicle defines a region of interest in the field of view for a scan of the field of view by the LIDAR system. The region of interest is a portion of the field of view. Based on the region of interest, the computing system transmits a control signal to the LIDAR system that causes the LIDAR system to emit first light pulses with a first intensity within the region of interest during the scan and second light pulses with a second intensity outside the region of interest during the scan. The first intensity is different from the second intensity to provide different ranges for distance measurements inside and outside the region of interest.

A system and method for large-scale lane marking detection using multimodal sensor data are disclosed. A particular embodiment includes: receiving image data from an image generating device mounted on a vehicle; receiving point cloud data from a distance and intensity measuring device mounted on the vehicle; fusing the image data and the point cloud data to produce a set of lane marking points in three-dimensional (3D) space that correlate to the image data and the point cloud data; and generating a lane marking map from the set of lane marking points.

A method for detecting unmanned aerial vehicles (UAV) includes detecting an unknown flying object in a monitored zone of air space. An image of the detected unknown flying object is captured. The captured image is analyzed to classify the detected unknown flying object. A determination is made, based on the analyzed image, whether the detected unknown flying object comprises a UAV.

A method for processing data provided by a lidar. Obtaining, for measurement points of a lidar, data representative of a diffuse intensity and a distance between the measurement point and the lidar; determining an angle of incidence of the lidar at each measurement point; calculating, for each measurement point, reflectivity from the angle of incidence, the distance and the diffuse intensity; identifying spatially adjacent measurement points having a reflectivity within a given range of values to form common reflectivity zones; defining a cost function for at least one common reflectivity zone, the cost function including a first diffuse intensity and a second diffuse intensity, the angle and the distance of the measurement point in question; minimizing the cost function in order to update at least one of the angle, the distance and the reflectivity at the measurement point, for each of the measurement points belonging to a common reflectivity zone.

A sensor aiming device includes: a target positional relationship processing unit for outputting positional relationship information of first and second targets; a sensor observation information processing unit configured to convert the observation result of the first and second targets into a predetermined unified coordinate system according to a coordinate conversion parameter, perform time synchronization at a predetermined timing, and extract first target information indicating a position of the first target and second target information indicating a position of the second target; a position estimation unit configured to estimate a position of the second target using the first target information, the second target information, and the positional relationship information; and a sensor correction amount estimation unit configured to calculate a deviation amount of the second sensor using the second target information and an estimated position of the second target and estimate a correction amount.

A computer-implemented method comprises: generating first output using a first sensor of a vehicle comprising an infrared camera or an event-based sensor, the first output indicating a portion of surroundings of the vehicle; providing the first output to a LiDAR of the vehicle having a field of view (FOV); configuring a resolution of the LiDAR based at least in part on the first output; generating a representation of at least part of the surroundings of the vehicle using the LiDAR; providing, to a perception component of the vehicle, second output of a second sensor of the vehicle and third output of the LiDAR, the perception component configured to perform object detection, sensor fusion, and object tracking regarding the second and third outputs, wherein the first output bypasses at least part of the perception component; and performing motion control of the vehicle using a fourth output of the perception component.

The offline map generation process may collect multiple point cloud data of the same area. A perception algorithm may operate on the point cloud data to detect static objects, which may be fixed road features that do not change among the point cloud data, allowing the perception algorithm to more accurately detect the static objects. During online operation of the ADV through the area, the ADV may trim regions-of-interest (ROI) of the area to exclude the predefined static objects. The perception algorithm may execute the sensor data of the ROI in real-time to detect objects in the ROI. The may be added back to the output of the perception algorithm to complete the perception output.

A light detection and ranging (LIDAR) system includes an automatic gain control (AGC) unit to reduce the dynamic range, reducing processing power and saving circuit area and cost. The system detects a return beam of a light signal transmitted to a target, having a first dynamic range in a time domain. An analog to digital converter (ADC) generates a digital signal based on the return beam. A processor can perform time domain processing on the digital signal, convert the digital signal from the time domain to a frequency domain, and perform frequency domain processing on the digital signal in the frequency domain. The AGC unit can measure a power of the return beam, and apply variable gain in the frequency domain to reduce a dynamic range of the return beam to a second dynamic range lower than the first dynamic range.

A point cloud data processing apparatus 11 includes a processor configured to acquire first form information that indicates a feature of a form of a first object, specify an object region of a second object that is identified from an image and that corresponds to the first form information, select second-object point cloud data, in point cloud data, that corresponds to the object region, on the basis of the object region, acquire second form information that indicates a feature of a form of the second object, on the basis of the second-object point cloud data, and compare the first form information with the second form information and perform determination as to whether the second object is the first object.

An electronic apparatus is provided. The electronic apparatus includes: a memory configured to store an image; a sensor part including at least one sensor; a projection part including a projection lens configured to output the image to a projection surface; and a processor configured to acquire distance information from the electronic apparatus to the projection surface through the sensor part, acquire output size information of the image based on the acquired distance information, acquire projection surface information corresponding to a bend of the projection surface through the sensor part, acquire movement information of the projection lens based on the output size information of the image and the projection surface information, and control the projection part to output the image based on the movement information.