Zoox, Inc.

Cooper Stokes Sloan

Chuang Wang

Noureldin Ehab Hendy

James William Vaisey Philbin

Feng Tian

Yi Xu

Sarah Tariq

Joshua Kriser Cohen

Sabeek Mani Pradhan

Kai Zhenyu Wang

Balazs Kovacs

Pengfei Duan

Nicholas George Charchut

Jifei Qian

Yassen Ivanchev Dobrev

Anton Mario Bongio Karrman

William Harland Montgomery, Iv

Li Yon Tan

A vehicle safety system of an autonomous vehicle may determine predicted velocity vectors for a potential collision, and use the velocity vectors to determine a trajectory for the autonomous vehicle to traverse the environment. The vehicle safety system may analyze sensor data to determine a likelihood of a potential collision with a dynamic object in the environment. Predicted velocity vectors may be determined for the autonomous vehicle and the dynamic object at a time and/or location associated with the potential collision. The predicted velocity vectors may be used to determine a point of impact, relative angle, and/or relative velocity between the autonomous vehicle and dynamic object at the potential collision. The likelihood of the potential collision and the predicted velocity vectors may be used to determine a trajectory for the autonomous vehicle, which may include following a current trajectory or transitioning to one or more contingent trajectories.

Collision avoidance and mitigation in autonomous vehicles using predicted trajectories

Techniques for verifying a reliability of map data are discussed herein. In some examples, map data can be used by a vehicle, such as an autonomous vehicle, to traverse an environment. Sensor data (e.g., image data, lidar data, etc.) can be received from a sensor associated with a vehicle and may be used to generate an estimated map and confidence values associated with the estimated map. When the sensor data is image data, images data from multiple perspectives or different time instances may be combined to generate the estimated map. The estimated map may be compared to a stored map or to a proposed vehicle trajectory or corridor to determine a reliability of the stored map data.

Verifying reliability of data used for autonomous driving

Techniques relating to monitoring map consistency are described. In an example, a monitoring component associated with a vehicle can receive sensor data associated with an environment in which the vehicle is positioned. The monitoring component can generate, based at least in part on the sensor data, an estimated map of the environment, wherein the estimated map is encoded with policy information for driving within the environment. The monitoring component can then compare first information associated with a stored map of the environment with second information associated with the estimated map to determine whether the estimated map and the stored map are consistent. Component(s) associated with the vehicle can then control the object based at least in part on results of the comparing.

Map consistency checker

A collision avoidance system may validate, reject, or replace a trajectory generated to control a vehicle. The collision avoidance system may comprise a secondary perception component that may receive sensor data, receive and/or determine a corridor associated with operation of a vehicle, classify a portion of the sensor data associated with the corridor as either ground or an object, determine a position and/or velocity of at least the nearest object, determine a threshold distance associated with the vehicle, and control the vehicle based at least in part on the position and/or velocity of the nearest object and the threshold distance.

Collision avoidance perception system

Techniques for accurately predicting and avoiding collisions with objects detected in an environment of a vehicle are discussed herein. A vehicle safety system can implement a model to output data indicating an intersection probability between the object and a portion of the vehicle in the future. The model may employ a rear collision filter, a distance filter, and a time to stop filter to determine whether a predicted collision may be a false positive, in which case the techniques may include refraining from reporting such predicted collision to other another vehicle computing device to control the vehicle.

Velocity regression safety system

A collision avoidance system may validate, reject, or replace a trajectory generated to control a vehicle. The collision avoidance system may comprise a secondary perception component that may comprise one or more machine learned models, each of which may be trained to output one or more occupancy maps based at least in part on sensor data of different types. The occupancy maps may include a prediction of whether at least a portion of an environment is occupied at a future time by any one of multiple object types. Occupancy maps associated with a same time may be aggregated into a data structure that may be used to validate, reject, or replace the trajectory.

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.

Multi-modal sensor data association architecture

Ground truth data may be too sparse to supervise training of a machine-learned (ML) model enough to achieve an ML model with sufficient accuracy/recall. For example, in some cases, ground truth data may only be available for every third, tenth, or hundredth frame of raw data. Training an ML model to detect a velocity of an object when ground truth data for training is sparse may comprise training the ML model to predict a future position of the object based at least in part on image, radar, and/or lidar data (e.g., for which no ground truth may be available). The ML model may be altered based at least in part on a difference between ground truth data associated with a future time and the future position.

Object velocity detection from multi-modal sensor data

Techniques for updating data operations in a perception system are discussed herein. A vehicle may use a perception system to capture data about an environment proximate to the vehicle. The perception system may receive state data stored in cyclic buffer of globally registered detection and occasionally converted to gridded point cloud in a local reference frame. The two-dimensional gridded point cloud may be processed using one or more neural networks to generate semantic data associated with a scene or physical environment surrounding the vehicle such that the vehicle can make environment aware operational decisions, which may improve reaction time(s) and/or safety outcomes of the autonomous vehicle.

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Cooper Stokes Sloan