Waymo LLC

Waymo L Lc

Benjamin Pitzer

William Grossman

Mingcheng Chen

Nathaniel Fairfield

Pierre-yves Droz

Pierre-Yves Droz

Christian Lauterbach

Peter Morton

Nolan Shenai

Michael R James

Austin Daniel Abrams

Christopher Bowen

Boris Sofman

The technology relates to autonomous vehicles having articulating sections such as the trailer of a tractor-trailer. Aspects include approaches for tracking the pose of the trailer, including its orientation relative to the tractor unit. Sensor data is analyzed from one or more onboard sensors to identify and track the pose. The pose information is usable by on-board perception and/or planning systems when driving the vehicle in an autonomous mode. By way of example, on-board sensors such as Lidar sensors are used to detect the real-time pose of the trailer based on Lidar point cloud data. The orientation of the trailer is estimated based on the point cloud data, and the pose is determined according to the orientation and other information about the trailer. Aspects also include determining which side of the trailer the sensor data is coming from. A camera may also detect trailer marking information to supplement the analysis.

Lidar-based trailer tracking

The technology relates to autonomous vehicles for transporting cargo and/or people between locations. Distributed sensor arrangements may not be suitable for vehicles such as large trucks, busses or construction vehicles. Side view mirror assemblies are provided that include a sensor suite of different types of sensors, including LIDAR, radar, cameras, etc. Each side assembly is rigidly secured to the vehicle by a mounting element. The sensors within the assembly may be aligned or arranged relative to a common axis or physical point of the housing. This enables self-referenced calibration of all sensors in the housing. Vehicle-level calibration can also be performed between the sensors on the left and right sides of the vehicle. Each side view mirror assembly may include a conduit that provides one or more of power, data and cooling to the sensors in the housing.

Sensor integration for large autonomous vehicles

The technology relates to fine maneuver control of large autonomous vehicles that employ multiple sets of independently actuated wheels. The control is able to optimize the turning radius, effectively negotiate curves, turns, and clear static objects of varying heights. Each wheel or wheel set is configured to adjust individually via control of an on-board computer system. Received sensor data and a physical model of the vehicle can be used for route planning and selecting maneuver operations in accordance with the additional degrees of freedom provided by the independently actuated wheels. This can include making turns, moving into or out of parking spaces, driving along narrow or congested roads, construction zones, loading docks, etc. A given maneuver may include maintaining a minimum threshold distance from a neighboring vehicle or other object.

Independently actuated wheel sets for large autonomous self-driving vehicles

An example method involves identifying one or more potential route segments that collectively connect at least two geographical points, receiving spatiotemporal weather information that predicts future weather conditions along each of the potential segments, and, for each potential segment, evaluating a partial cost function that comprises a summation of a set of segment-weighted cost factors, where at least one segment-weighted cost factor comprises an adverse weather risk factor based on the future weather conditions along the potential segment. The method also involves selecting, based on a minimization of a total cost function, a set of selected segments and corresponding segment target speeds for the vehicle to utilize while traversing between the at least two geographical points so as to avoid adverse weather conditions, the total cost function being the sum of partial cost functions associated with a set of segments that collectively connect the at least two geographical points.

Speed and route planning in view of weather

The technology relates to route planning and performing driving operations in autonomous vehicles, such as cargo trucks, articulating buses, as well as other vehicles. A detailed kinematic model of the vehicle in evaluated in conjunction with roadgraph and other information to determine whether a route or driving operation is feasible for the vehicle. This can include evaluating a hierarchical set of driving rules and whether current driving conditions impact any of the rules. Driving trajectories and cost can be evaluated when pre-planning a route for the vehicle to follow. This can include determining an ideal trajectory for the vehicle to take a particular driving action. Pre-planned routes may be shared with a fleet of vehicles, and can be modified based on information obtained by different vehicles of the fleet.

Kinematic model for autonomous truck routing

The technology relates to enhancing or extending the field of view of sensors for vehicles configured to operate in an autonomous driving mode. One or more mirrors are used to reflect or redirect beams emitted from onboard sensors that would otherwise be wasted, for instance due to obstruction by a portion of the vehicle or because they are emitted at high pitch angles to the side. The mirrors are also used to redirect incoming beams from the external environment toward one or more of the onboard sensors. Using mirrors for such redirection can reduce or eliminate blind spots around the vehicle. A calibration system may be employed to account for mirror movement due to vibration or wind drag. Each mirror may be a front surface mirror. The mirrors may be positioned on the vehicle body, on a faring, or extending from a sensor housing on the vehicle.

Mirrors to extend sensor field of view in self-driving vehicles

Example embodiments relate to measuring rotational speeds of trailer wheels using radar. A computing device may cause a vehicle radar unit to transmit radar signals toward a wheel of trailer being pulled by the vehicle. The computing device may receive radar reflections corresponding to radar signals that reflected off the wheel and determine a rotational speed of the wheel based on the radar reflections. For instance, the computing device may identify the highest or lowest frequency in the frequency spectrum of the radar reflections and use the frequency and the wheel's radius to calculate the rotational speed of the wheel. The computing device can use rotational speed measurements for trailer wheels to monitor performance of the trailer and adjust vehicle navigation accordingly. In some instances, the computing device may determine that one of the trailer wheels requires servicing based on monitoring the rotational speeds of the trailer wheels.

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East Palo Alto, CA, United States of America

Benjamin Pitzer