Waymo LLC

Brandon Douglas Luders

Nathaniel Fairfield

Vadim Furman

Aishwarya Parasuram

Xin Liu

Luis Torres

Tim Campbell

Luyuan Lin

David Ian Ferguson

David Harrison Silver

David Ian Franklin Ferguson

Andreas Wendel

Zhinan Xu

Jennifer Taylor

Samuel Liu

Enrique Fernandez Gonzalez

Nayun Xu

The described aspects and implementations support sound-based emergency vehicle detection, localization, and tracking for autonomous vehicle and driver-assist systems. The techniques include obtaining, using one or more audio detectors of a vehicle, a sound recording that includes a sound emitted by an emergency vehicle (EV). The techniques further include applying a sound localization (SL) model to the sound recording to obtain a SL output, which includes a first map of possible locations of the EV in a driving environment of the vehicle and can further include a second map of possible velocities of the EV. The techniques further include simulating, using the SL output, trajectories of simulated EV(s) in the driving environment of the vehicle, and causing, responsive to proximity of the simulated trajectories to a driving path of the vehicle, modification of the driving path of the vehicle.

Autonomous vehicle driving path selection in the presence of emergency vehicle sounds

Sensor data identifying an emergency vehicle approaching the autonomous vehicle may be received. A predicted trajectory for the emergency vehicle may be received. Whether the autonomous vehicle is impeding the emergency vehicle may be determined based on the predicted trajectory and map information identifying a road on which the autonomous vehicle is currently traveling. Based on a determination that the autonomous vehicle is impeding the emergency vehicle, the autonomous vehicle may be controlled in an autonomous driving mode in order to respond to the emergency vehicle.

Responding to emergency vehicles for autonomous vehicles

The technology relates to identifying sensor occlusions due to the limits of the ranges of a vehicle's sensors and using this information to maneuver the vehicle. As an example, the vehicle is maneuvered along a route that includes traveling on a first roadway and crossing over a lane of a second roadway. A trajectory is identified from the lane that will cross with the route during the crossing at a first point. A second point beyond a range of the vehicle's sensors is selected. The second point corresponds to a hypothetical vehicle moving towards the route along the lane. A distance between the first point and the second point is determined. An amount of time that it would take the hypothetical vehicle to travel the distance is determined and compared to a threshold amount of time. The vehicle is maneuvered based on the comparison to complete the crossing.

Handling sensor occlusions for autonomous vehicles

Aspects of the disclosure relate to methods for controlling a vehicle having an autonomous driving mode. For instance, sensor data may be received from one or more sensors of the perception system of the vehicle, the sensor data identifying characteristics of an object perceived by the perception system. When it is determined that the object is no longer being perceived by the one or more sensors of the perception system, predicted characteristics for the object may be generated based on one or more of the identified characteristics. The predicted characteristics of the object may be used to control the vehicle in the autonomous driving mode such that the vehicle is able to respond to the object when it is determined that the object is no longer being perceived by the one or more sensors of the perception system.

Tracking vanished objects for autonomous vehicles

A system and method are provided for early detection of objects by a perception system of a vehicle, and triggering a precautionary action by the vehicle in response without waiting for a more precise detection. The vehicle has a multi-level sensor range, wherein a first level of the sensor range is adjacent an outer bounds of the sensor range and has a first confidence value, and a second level of the sensor range is within the first range and has a second, higher confidence value. In situations when oncoming traffic is traveling at a high rate of speed, the vehicle responds to noisier detections, or objects perceived with a lower degree of confidence, rather than waiting for verification which may come too late.

Early object detection for unprotected turns

Aspects of the disclosure relate to a method of controlling a vehicle having an autonomous driving mode at a multi-way stop intersection. For instance, that the vehicle has come to a stop at a multi-stop intersection may be determined. At least one road user at the intersection may be identified. Based on the determination that the vehicle has come to the stop, a period of time to wait for the at least one road user to proceed through the intersection may be determined. After waiting the determined period of time, that the at least one road user has not begun to proceed through the intersection may be determined. Based on the determination that the at least one user has not begun to proceed through the intersection, the vehicle may be controlled in the autonomous driving mode in order to proceed through the intersection.

Multi-way stop intersection precedence for autonomous vehicles

A system and method is provided for early detection of objects by a perception system of a vehicle, and triggering a precautionary action by the vehicle in response without waiting for a more precise detection. The vehicle has a multi-level sensor range, wherein a first level of the sensor range is adjacent an outer bounds of the sensor range and has a first confidence value, and a second level of the sensor range is within the first range and has a second higher confidence value. In situations when oncoming traffic is traveling at a high rate of speed, the vehicle responds to noisier detections, or objects perceived with a lower degree of confidence, rather than waiting for verification which may come too late.

A method is provided that involves identifying a target region of an environment of an autonomous vehicle to be monitored for presence of moving objects. The method also involves operating a first sensor to obtain a scan of a portion of the environment that includes at least a portion of the target region and an intermediate region between the autonomous vehicle and the target region. The method also involves determining whether a second sensor has a sufficiently clear view of the target region based on at least the scan obtained by the first sensor. The method also involves operating the second sensor to monitor the target region for presence of moving objects based on at least a determination that the second sensor has a sufficiently clear view of the target region. Also provided is an autonomous vehicle configured to perform the method.

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Sunnyvale, CA, United States of America

Brandon Douglas Luders