Waymo LLC

Guillaume Dupre

Joshua Seth Herbach

Michael Epstein

Mishika Vora

Kevin Rawlings

Jared Stephen Russell

John Wesley Dyer

Austin Daniel Abrams

Luis Torres

Benjamin Charrow

Eric Schoenfeld

Brian Cohen

Aspects of the disclosure relate to routing an autonomous vehicle. For instance, the vehicle may be maneuvered along a route in a first lane using map information identifying a first plurality of nodes representing locations within the first lane and a second plurality of nodes representing locations within a second lane different from the first lane. While maneuvering, when the vehicle should make a lane change may be determined by assessing a cost of connecting a first node of the first plurality of nodes with a second node of a second plurality of nodes. The assessment may be used to make the lane change from the first lane to the second lane.

Real-time lane change selection for autonomous vehicles

Aspects of the disclosure provide for generation of trajectories for a vehicle driving in an autonomous driving mode. In one instance, a default number of trajectories to be generated may be identified. A set of maneuvering options may be selected from a set of predetermined maneuvering options based on the number of trajectories. The set of maneuvering options may be filtered based on the default number of trajectories. A set of trajectories may be generated based on the filtered set of maneuvering option such that each trajectory of the set corresponds to a different maneuvering behavior. A cost for each trajectory of the set of trajectories may be determined, and one of the trajectories of the set of trajectories may be selected based on the determined costs. The vehicle may be maneuvered in the autonomous driving mode according to the selected one of the trajectories.

Generating trajectories for autonomous vehicles

Aspects of the disclosure relate to controlling a vehicle in an autonomous driving mode. For instance, a first location corresponding to a location where the vehicle is to pick up or drop off a passenger is received. A first cost for the vehicle to reach the first location is determined. A second location based on the first location is identified, and a second cost is determined based on a cost for the vehicle to reach the second location and a cost for the passenger to reach the second location. The first cost is compared to the second cost, and a notification is sent based on the notification. In response to sending the notification, instructions to proceed to the second location are received, and in response to receiving the instructions, the vehicle is controlled in the autonomous driving mode to the second location to pick up or drop off the passenger.

Suggesting alternative pickup and drop off locations for autonomous vehicles

A simulation may be used to determine a difference between progress of a manually-driven vehicle and progress of a simulated autonomous vehicle. The method includes retrieving log data collected for the manually-driven vehicle driving along a route, generating a plurality of path segments for a portion of the route. The plurality of path segments corresponds to points in a lane that the manually-driven vehicle traveled through on the portion of the route. The method also includes running, using a software of the autonomous vehicle, a simulation of the autonomous vehicle driving along the plurality of path segments, extracting metrics from the log data and the simulation, and determining the difference between a first progress of the manually-driven vehicle and a second progress of the simulated autonomous vehicle based on the metrics.

Using simulations to identify differences between behaviors of manually-driven and autonomous vehicles

The technology relates to adjusting a trajectory of an autonomous vehicle. In one example, one or more processors are configured to determine a current trajectory and an alternate trajectory of the autonomous vehicle. The one or more processors select a segment of the current trajectory and a segment of the alternate trajectory and determine, for each of the segments, a total expected value of an estimated length of time to traverse the respective segment. The one or more processors may then select one of the segments having the lowest expected value and maneuver the autonomous vehicle along at least a portion of the selected segment.

Biased trajectory progress metric

Aspects of the present disclosure relate to context aware stopping of a vehicle without a driver. As an example, after a passenger has entered the vehicle, the vehicle is maneuvered by one or more processors in an autonomous driving mode towards a destination location along a route. The route is divided into two or more stages. A signal is received by the one or more processors. The signal indicates that the passenger is requesting that the vehicle stop or pull over. In response to the signal, the one or more processors determine a current stage of the route based on a current distance of the vehicle from a pickup location where the passenger entered the vehicle or a current distance of the vehicle from the destination location. The one or more processors then stop the vehicle in accordance with the determined current stage.

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San Francisco, CA, United States of America

Guillaume Dupre