Sensors coupled to a vehicle are calibrated, optionally using a dynamic scene with sensor targets around a motorized turntable that rotates the vehicle to different orientations. One vehicle sensor captures a representation of one feature of a sensor target, while another vehicle sensor captures a representation of a different feature of the sensor target, the two features of the sensor target having known relative positioning on the target. The vehicle generates a transformation that maps the captured representations of the two features to positions around the vehicle based on the known relative positioning of the two features on the target.

A sensor evaluation device evaluates a first sensor mounted on a sensor-mounting object. The sensor evaluation device is provided with a specific event detecting unit, and a recording unit. The specific event detecting unit detects a specific event which is at least one of (a) an unrecognized event where a second sensor mounted on the sensor-mounting object recognizes a first target whereas the first sensor does not recognize the first target, and (b) a misrecognized event where the first sensor recognizes a second target whereas the second sensor does not recognize the second target. The recording unit records information on the specific event when detecting the specific event.

A system can include a computer including a processor and a memory, the memory storing instructions executable by the processor to receive point cloud data. The instructions further include instructions to generate a plurality of feature maps based on the point cloud data, each feature map of the plurality of feature maps corresponding to a parameter of the point cloud data. The instructions further include instructions to aggregate the plurality of feature maps into an aggregated feature map. The instructions further include instructions to generate, via a feedforward neural network, at least one of a segmentation output or a classification output based on the aggregated feature map.

This document describes “Multi-domain Neighborhood Embedding and Weighting” (MNEW) for use in processing point cloud data, including sparsely populated data obtained from a lidar, a camera, a radar, or combination thereof. MNEW is a process based on a dilation architecture that captures pointwise and global features of the point cloud data involving multi-scale local semantics adopted from a hierarchical encoder-decoder structure. Neighborhood information is embedded in both static geometric and dynamic feature domains. A geometric distance, feature similarity, and local sparsity can be computed and transformed into adaptive weighting factors that are reapplied to the point cloud data. This enables an automotive system to obtain outstanding performance with sparse and dense point cloud data. Processing point cloud data via the MNEW techniques promotes greater adoption of sensor-based autonomous driving and perception-based systems.

A computer-implemented method for detecting an obstacle on a route ahead of a first vehicle. In the method, information on a second vehicle driving ahead on the route is recorded in the first vehicle by at least one sensor of the first vehicle. In the first vehicle, depending on the recorded information, a computer detects an avoidance maneuver of the second vehicle due to an obstacle or detects that the second vehicle has driven over an obstacle. An obstacle is detected on the route depending on the detected avoidance maneuver or the detection that the vehicle has driven over an obstacle. A measure for protecting the vehicle and/or the obstacle is initiated depending on the detected obstacle.

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.

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.

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.