Zoox, Inc.

Badeea Ferdaous Alferdaous Alazem

Samantha Marie Ting

Chuang Wang

Venkata Subrahmanyam Chandra Sekhar Chebiyyam

Subasingha Shaminda Subasingha

Patrick Blaes

Joshua Kriser Cohen

Omar Abdeldayem

Techniques for estimating a ground plane based on lidar data and/or attributes of the ground plane are discussed herein. A vehicle captures radar data, e.g., 4D radar data including height information, as it traverses an environment. The radar data can include direct returns from an object and reflected or multipath returns, e.g., that reflect off a ground surface and the object. A position of the ground plane can be estimated based at least in part on a distance between direct returns and the reflected returns. Attributes of the ground plane may be determined from differences between the direct returns and the reflected returns.

Radar-based ground plane characteristic determination

This disclosure describes techniques for detecting multipath radar returns and modifying radar data. A vehicle may use radar devices to receive radar data while traversing within an environment. The vehicle may process the radar data using a virtual array based on an arraignment of the antennae within an aperture of the radar device. Using the virtual array, the vehicle may determine an elevated noise level that may be indicative of a multipath radar return. Based on the elevated noise level, the vehicle may determine a second virtual array associated with multipath radar returns, and may process the radar data using the second virtual array. Based on determining that the noise level associated with the second virtual array is lower than the initial noise level, the vehicle may determine that the radar data includes a multipath radar return, and may modify the radar data to correct or mitigate the error caused by the multipath return.

Radar multipath detection based on changing virtual arrays

This disclosure describes techniques for using radar cross-section (RCS) data to classify objects detected by autonomous vehicles within driving environments. In some examples, the variance of the RCS data associated with an object may be evaluated to determine signal interference caused by multipath fading. The variance of the RCS data may be used to classify the object and to determine whether the autonomous vehicle can safely drive over the object. For instance, objects such as manhole covers, storm drains, and expansion joints may provide a significant radar signal, but low RCS variance indicating that they can be driven over by the vehicle. Based on the classification of the object, the autonomous vehicle may determine a trajectory around the object or directly over the object.

Radar object classification based on radar cross-section data

Techniques for detecting and modifying radar data anomalies using multistage clustering are described herein. In some examples, radar data including a set of radar points may be captured by a vehicle operating within an environment. The vehicle may determine a radar point subset by clustering the radar data based on the range and doppler values of the radar points. The vehicle may analyze the azimuth values associated with the radar point subset, including evaluating the azimuth values of the radar point subset relative to one or more azimuth error ranges determined based on the radar antenna configuration and/or multipath detections. When detecting anomalous and/or invalid radar points having azimuth values within an azimuth error range, the vehicle may modify the subset of radar points to mitigate the anomalous or invalid radar points.

Radar anomaly detection based on multistage clustering

Techniques are discussed herein for analyzing radar data to determine that radar noise from one or more target detections potentially conceals additional objects near the target detection. Determining whether an object may be concealed can be based at least in part on a radar noise level based on a target detection, as well as distributions of radar cross sections and/or doppler data associated with particular object types. For a location near a target detection, a radar system may determine estimated noise levels, and compare the estimated noise levels to radar cross section probabilities associated with object types to determine the likelihood that an object of the object type could be concealed at the location. Based on the analysis, the system may determine a vehicle trajectory or otherwise may control a vehicle based on the likelihood that an object may be concealed at the location.

Radar data analysis and concealed object detection

Techniques for determining a probability of a false negative associated with a location of an environment are discussed herein. Data from a sensor, such as a radar sensor, can be received that includes point cloud data, which includes first and second data points. The first data point has a first attribute and the second data point has a second attribute. A difference between the first and second attributes is determined such that a frequency distribution may be determined. The frequency distribution may then be used to determine a distribution function, which allows for the determination of a resolution function that is associated with the sensor. The resolution function may then be used to determine a probability of a false negative at a location in an environment. The probability can be used to control a vehicle in a safe and reliable manner.

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Redwood City, CA, United States of America

Badeea Ferdaous Alferdaous Alazem