A system includes a tracking system, a controller-circuit, and a device. The tracking system is configured to detect and track an object, and includes one or more of a computer vision system, a radar system, and a LIDAR system. The controller-circuit is disposed in a host vehicle, and is configured to receive detection signals from the tracking system, process the detection signals, determine, whether an object is detected based on the processed detecting signals, and in accordance with a determination that an object is detected, output command signals. The device is adapted to be mounted to the host vehicle, and is configured to receive the command signals and thereby provide a dynamic visual indication adapted to change in accordance with orientation changes between the host vehicle and the object. The dynamic visual indication is viewable from outside of the host vehicle.

Aspects of the present disclosure involve systems, methods, and devices for fault detection in a Lidar system. A fault detection system obtains incoming Lidar data output by a Lidar system during operation of an AV system. The incoming Lidar data includes one or more data points corresponding to a fault detection target on an exterior of a vehicle of the AV system. The fault detection system accesses historical Lidar data that is based on data previously output by the Lidar system. The historical Lidar data corresponds to the fault detection target. The fault detection system performs a comparison of the incoming Lidar data with the historical Lidar data to identify any differences between the two sets of data. The fault detection system detects a fault condition occurring at the Lidar system based on the comparison.

In an external sensor attachment portion structure of the present invention, an external sensor includes: a sensor main body including a detection unit that detects external information; a sensor attachment bracket used to attach the sensor main body to a vehicle body frame member; and a sensor garnish including a window portion through which the detection unit is exposed in front view. The sensor garnish is provided on an outer side of the host vehicle so as to expose the detection unit of the external sensor and cover the sensor main body and the sensor attachment bracket excluding the detection unit. Small gaps are provided between the sensor main body and a window frame of the window portion in the sensor garnish. The window frame includes a noise suppression portion that suppresses wind noise due to airflow passing through the gaps along a rearward direction of the host vehicle.

An autonomous vehicle (AV) includes a radar sensor system and a computing system that computes velocities of an object in a driving environment of the AV based upon radar data that is representative of radar returns received by the radar sensor system. The AV can be configured to compute a first velocity of the object based upon first radar data that is representative of the radar return from a first time to a second time. The AV can further be configured to compute a second velocity of the object based upon second radar data that includes at least a portion of the first radar data and further includes additional radar data representative of a radar return received subsequent to the second time. The AV can further be configured to control one of a propulsion system, a steering system, or a braking system to effectuate motion of the AV based upon the computed velocities.

A communication device includes a target locating unit configured to locate a position of a target having a risk of approaching a moving body. The communication device includes a transmission unit configured to transmit request information including positional information of an external terminal, for which the positional information is requested, based on the position of the target located by the target locating unit. The communication device includes a reception unit configured to receive response information with respect to the request information. The transmission unit is configured to transmit warning information based on the positional information of the external terminal included in the response information.

Enclosed are embodiments for authenticating point cloud data. In an embodiment, a method of authenticating point cloud data comprises: generating, with at least one processor, a point cloud packet, the point cloud packet comprising a header portion and a data section, the data section comprising a plurality of blocks, each block comprising point cloud data; generating, with the at least one processor, a message sequence number (MSN); storing, with the at least one processor, the MSN in the data section; generating, with the at least one processor, a message authentication code (MAC) on the data section; storing the MAC in the point cloud packet; and transmitting, with the at least one processor, the point cloud packet to a receiving device.

A self-calibrating scanning system and method provides a novel way to eliminate errors in scanning systems, such as lidar or radar detection, using an inertial measurement unit. The system includes an energy transmission source configured to transmit an energy signal through a transmittal area. A detector receives a return energy signal of at least one target object of the energy transmitter source within the transmittal area. The system calculates at least one of the range and position of an object from information relating to at least one of the time and phase of the return energy signal relative to the transmittal energy signal. The spatial or angular displacement of the detector relative to the light source is measured using data from the inertial measurement unit, and at least one of calculated range and position of the object is adjusted based on the spatial or angular displacement of the detector.

A sensor calibration method and apparatus, and a storage medium are provided. In the method, multiple scene images and multiple first point clouds of a target scene are acquired by an image sensor and a radar sensor. A second point cloud of the target scene is constructed according to the multiple scene images. A first distance error between the image sensor and radar sensor is determined according to first feature point sets and a second feature point set. A second distance error of the radar sensor is determined according to multiple first feature point sets. A reprojection error of the image sensor is determined according to a first global position of the second feature point set in the global coordinate system and first image positions of pixel points corresponding to the second feature point set in the scene image. The radar sensor and image sensor are calibrated.

An obstacle positioning method, device and terminal are provided. The method includes determining installation positions of at least two detectors on a vehicle, and respective detection areas of the detectors, determining an overlapping area of the detection areas of the detectors, and if determining that an obstacle is located in the overlapping area, determining a position of the obstacle according to the installation positions of the detectors forming the overlapping area. By changing the number and positions of detectors installed on an unmanned vehicle, a plurality of overlapping areas of the detection areas of the detectors are obtained, the distribution of obstacles around the vehicle are optimally identified, so that the unmanned vehicle makes reasonable driving plans based on an accurate surrounding obstacle environment.

A method and apparatus for modeling the environment proximate an autonomous system. The method and apparatus accesses vision data, assigns semantic labels to points in the vision data, processes points that are identified as being a drivable surface (ground) and performs an optimization over the identified points to form a surface model. The model is subsequently used for detecting objects, planning, and mapping.