Systems and methods of determining a risk distribution associated with a multiplicity of coverage zones covered by a multiplicity of sensors of an autonomous driving vehicle (ADV) are disclosed. The method includes for each coverage zone covered by at least one sensor of the ADV, obtaining MTBF data of the sensor(s) covering the coverage zone. The method further includes determining a mean time between failure (MTBF) of the coverage zone based on the MTBF data of the sensor(s). The method further includes computing a performance risk associated with the coverage zone based on the determined MTBF of the coverage zone. The method further includes determining a risk distribution based on the computed performance risks associated with the multiplicity of coverage zones.

A method for detecting a parallax problem in sensor data from two sensors spaced apart from each other and at least partly capturing the same environment, wherein at least one of the sensors provides distance information. The method includes obtaining the acquired sensor data from the sensors; assigning measured values in the acquired sensor data of one sensor to corresponding measured values in the acquired sensor data of the other sensor, wherein the assignment takes the respective imaging conditions of the two sensors into account; consecutively numbering the measured values in the sensor data; checking whether a sorting order of the numbering of the measured values that correspond to each other matches, a parallax problem being determined in response to a sorting order not matching; and outputting a test result. Also disclosed is an apparatus for detecting a parallax problem in sensor data from two sensors and a transportation vehicle.

A method and an apparatus for obtaining information are disclosed. The method includes: determining that a first sensor in environment sensing sensors in a vehicle fails; determining a first detection area of the first sensor, where the first detection area includes a first angle range, and the first angle range is an angle range of a detection angle, of the first sensor, that covers a driving environment around the vehicle; adjusting a second detection area of a dynamic sensor in the vehicle, so that an angle range of the second detection area covers the first angle range, where the angle range of the second detection area is a range of a detection angle, of the dynamic sensor, that covers a driving environment around the vehicle; and obtaining environment information by using the dynamic sensor.

A machine-learning architecture may be trained to determine point cloud data associated with different types of sensors with an object detected in an image and/or generate a three-dimensional region of interest (ROI) associated with the object. In some examples, the point cloud data may be associated with sensors such as, for example, a lidar device, radar device, etc.

In an embodiment, a method for estimating a composition of food includes: receiving a first three-dimensional (3D) image; identifying food in the first 3D image; determining a volume of the identified food based on the first 3D image; and estimating a composition of the identified food using a millimeter-wave radar.

Techniques are provided for determining a location of a mobile device based on visual positioning solution (VPS). An example method for determining a position estimate of a mobile device includes obtaining sensor information, detecting one or more identifiable features in the sensor information, determining a range to at least one of the one or more identifiable features, obtaining coarse map information, determining a location of the at least one of the one or more identifiable features based on the coarse map information, and determining the position estimate for the mobile device based at least in part on the range to the at least one of the one or more identifiable features.

Provided are methods for automatically adjusting a vehicle seating area based on the characteristics of a passenger. In an example method, a seat adjustment system of a vehicle receives sensor data representing at least one measurement of a user exterior to the vehicle, determines at least one characteristic of the based on the sensor data, determines at least one modification to a seating area of the vehicle based on the at least one characteristic of the user, and causes the seating area to be adjusted in accordance with the at least one modification. Systems and computer program products are also provided.

A method for recognizing image artifacts in a chronological sequence of recordings recorded by means of a lighting device and an optical sensor is disclosed. A difference movement field is obtained by removing from a movement field all movement field vectors to be expected due to inherent movement of a lighting device and an optical sensor. The movement field vectors in the difference movement field are combined into one or more objects according to at least one grouping criterion. The objects in the image are classified as plausible or as implausible pursuant to a movement plausibility test. An object classified as implausible is recognized as an image artifact.

Techniques and apparatuses are described that implement motion classification using low-level detections. In particular, a radar system identifies fused detections associated with an object and determines whether the fused detections indicate that the object is moving. If it is determined to be moving or moving perpendicular to the host vehicle, a current motion counter or perpendicular motion counter is incremented, respectively. A current motion flag and/or a perpendicular motion flag are set as true if the current motion counter or the perpendicular motion counter has a value greater than a threshold value, respectively. In response to setting either flag as true, the radar system increments a historical motion counter as true. The host vehicle is then operated based on the current motion flag, the perpendicular motion flag, and the historical motion counter. In this way, the radar system introduces hysteresis to improve the reliability and stability of motion classification.

A method may include obtaining first sensor data from a first sensor system and second sensor data from a second sensor system. The first and the second sensor systems may capture sensor data from a total measurable world. The method may include identifying a first object included in the first sensor data and a second object included in the second sensor data and determining first parameters corresponding to the first object and second parameters corresponding to the second object. The first parameters may be compared with the second parameters and whether the first object and the second object are a same object may be determined based on the comparing the first parameters and the second parameters. Responsive to determining that the first object and the second object are the same object, a set of objects representative of objects in the total measurable world including the same object may be generated.