Abstract: An illicit signal is detected on an in-vehicle communication network of an autonomous vehicle. A severity level corresponding to the illicit signal is identified, among multiple severity levels, based on one or more characteristics associated with the illicit signal. The severity level is indicative of a level of adverse impact on safety related to an autonomous vehicle environment. The adverse impact is to be caused by the autonomous vehicle when the autonomous vehicle is compromised by the illicit signal. A security operation is selected from multiple security operations based on the identified severity level. The security operation is performed to mitigate the adverse impact on safety related to the autonomous vehicle environment.

Abstract: 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.

Abstract: Example embodiments relate to methods and systems for automatic problematic maneuver detection and adapted motion planning. A computing device may obtain a route for navigation by a vehicle and a set of vehicle parameters corresponding to the vehicle. Each vehicle parameter can represent a physical attribute of the vehicle. The computing device may generate a virtual vehicle that represents the vehicle based on the set of vehicle parameters and perform a simulation that involves the virtual vehicle navigating the route. Based on the results of the simulation, the computing device may provide the original route or a modified route to the vehicle for the vehicle to subsequently navigate to its destination. In some cases, the simulation may further factor additional conditions, such as potential weather and traffic conditions that are likely to occur during the time when the vehicle plans on navigating the route.

Abstract: The present disclosure relates to systems and methods for occlusion detection. One example method involves a light detection and ranging (LIDAR) device scanning at least a portion of an external structure within a field-of-view (FOV) of the LIDAR device. The LIDAR device is physically coupled to the external structure. The scanning comprises transmitting light pulses toward the external structure through an optical window, and receiving reflected light pulses through the optical window. The reflected light pulses comprise reflections of the transmitted light pulses returning back to the LIDAR device from the external structure. The method also involves detecting presence of an occlusion that at least partially occludes the LIDAR device from scanning the FOV based on at least the scan of the at least portion of the external structure.

Abstract: 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.

Abstract: Aspects of the disclosure relate to generating assets for a simulation in order to test autonomous vehicle control software. For instance, data identifying a vehicle object, a polygon for the vehicle object, and a polygon for an open-door object associated with the vehicle object may be received. A rectangle may be fit to the polygon for the vehicle object. A polygon for the vehicle object without the open-door object may be determined based on the rectangle. A vehicle asset representing a vehicle may be adjusted based on the polygon for the vehicle object without the open-door object. A position and rotation of the rectangle may be used to position an open-door asset representing an open door for the simulation. The open-door asset is adjusted based on the adjustment to the vehicle asset. The simulation may be run with the positioned and adjusted open door asset in order to test the autonomous vehicle control software.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that determine yield behavior for an autonomous vehicle. An agent that is in a vicinity of an autonomous vehicle can be identified. An obtained crossing intent prediction characterizes a predicted likelihood that the agent intends to cross a roadway during a future time period. First features of the agent and of the autonomous vehicle are obtained. An input that includes the first features and the crossing intent prediction is processed using a machine learning model to generate an intent yielding score that represents a likelihood that the autonomous vehicle should perform a yielding behavior due to the intent of the agent to cross the roadway. From at least the intent yielding score, an intent yield behavior signal is determined and indicates whether the autonomous vehicle should perform the yielding behavior prior to reaching the first crossing region.

Abstract: A system includes a memory device, and a processing device, operatively coupled to the memory device, to receive a set of input data including a representation of a drivable space for an autonomous vehicle (AV), generate, based on the representation of the drivable space, a motion planning blueprint from a flow field modeling the drivable space, and identify, using the motion planning blueprint, a driving path of the AV within the drivable space.

Abstract: The present disclosure relates to optical systems and vehicles, which may incorporate lidar sensors. An example optical system includes a light-emitter device configured to emit emission light. The optical system also includes an optical element including a first surface and an opposing second surface. The first surface includes a diffusing surface configured to diffuse the emission light to form diffused light. The second surface includes a focusing surface configured to focus the diffused light to provide an intensity profile of light emitted within a field of view of the optical system.

Abstract: Aspects of the technology relate to assisting a passenger in an autonomous vehicle without a driver. For instance, after a door of the vehicle is opened, a predetermined period of time may be waited by processors of computing devices of the vehicle. After waiting the predetermined period of time and when the vehicle's door remains open, a set of instructions for closing the vehicle's door may be played by the processors through a speaker of the vehicle. Once the door of the vehicle is closed, an announcement may be played by the processors through the speaker requesting that the passenger press a first button to initiate a ride to a destination. In response to the first button being pressed, the ride to the destination may be initiated by the processors by maneuvering the vehicle autonomously to the destination.

Abstract: The technology involves identifying suitable pickup and drop-off locations based on detected pedestrian walking paths. Mapped areas have specific physical configurations, which may suggest places to pick up or drop off a rider (or a delivery). A walking path heatmap can be generated based on obtained historical and/or real-time pedestrian-related information, which can be obtained by autonomous vehicles driving in areas of interest. Incorporating heatmap information into the evaluation, the system identifies locations for optimized pickup or drop-off in accordance with where pedestrians would likely go. One aspect involves classifying different objects, for instance identifying one or more objects as people who may be walking versus riding a bicycle. Once classified, information about the paths is used to obtain a the heatmap associated with the walking paths.

Abstract: Aspects of the disclosure relate to generating a model to assess maximum numbers of concurrent trips for an autonomous vehicle transportation service. For instance, historical trip data, including when requests for assistance were made, response times for those requests for assistance, and a number of available resources when each of the requests for assistance were made may be received. In addition, a number of concurrent trips, or trips that overlap in time, occurring when each of the requests for assistance were made may be received. The model may be trained using the historical trip data and the numbers of concurrent trips. The model may be configured to provide a maximum number of concurrent trips given a period of time, a number of available resources, and a response time requirement.

Abstract: Example embodiments relate to methods for localizing light detection and ranging (lidar) calibration targets. An example method includes generating a point cloud of a region based on data from a light detection and ranging (lidar) device. The point cloud may include points representing at least a portion of a calibration target. The method also includes determining a presumed location of the calibration target. Further, the method includes identifying, within the point cloud, a location of a first edge of the calibration target. In addition, the method includes performing a comparison between the identified location of the first edge of the calibration target and a hypothetical location of the first edge of the calibration target within the point cloud if the calibration target were positioned at the presumed location. Still further, the method includes revising the presumed location of the calibration target based on at least the comparison.

Abstract: The technology relates to enhancing the operation of autonomous vehicles. Extendible sensors are deployed based on detected or predicted conditions around a vehicle while operating in a self-driving mode. When not needed, the sensors are fully retracted into the vehicle to reduce drag and increase fuel economy. When the onboard system determines that there is a need for a deployable sensor, such as to enhance the field of view of the perception system, the sensor is extended in a predetermined manner. The deployment may depend on one or more operating conditions and/or particular driving scenarios. These and other sensors of the vehicle may be protected with a rugged housing, for instance to protect against damage from the elements. And in other situations, deployable foils may extend from the vehicle's chassis to increase drag and enhance braking. This may be helpful for large trucks in steep descent situations.

Abstract: Aspects of the disclosure relate to training and using a model for verifying accuracy of ICP alignments or alignments between data points using an iterative closest point algorithm. For instance, a model may be trained using ICP alignment data, including alignments between an object appearing in LIDAR sensor frames. The training may also include setting a definition for a trusted ICP alignment. In this regard, the model may be trained such that, n response to receiving additional LIDAR sensor frames and corresponding additional ICP alignment data, output a value indicative of whether the additional ICP alignment data is trusted according to the definition. The model may then be used to control a vehicle in an autonomous driving mode by determining whether alignment data for object determined using the ICP algorithm should be trusted.

Abstract: Disclosed are devices and methods that may be used for the calibration of sensors of a vehicle. A calibration system disclosed herein includes a trigger device configured to detect a first sensing event. The calibration system further includes a plurality of sensor targets. Additionally, the calibration system includes a processor configured to reveal the plurality of sensor targets within a given region in response to the first sensing event. The calibration system may be configured to detect light from a LIDAR striking a light sensors. In response to detecting the light from the LIDAR, a plurality of lights, both visible and infrared, may be illuminated to correspond with a position and timing of the LIDAR pulse.

Abstract: Aspects of the disclosure relate to providing transportation services with autonomous vehicles. For instance, a first route to a first destination may be determined. The first route may have a first cost. Weather information for the first destination may be received. A characteristic is determined based on the weather information. A second destination having the characteristic may be selected. The second destination may be different from the first destination. A second route to the second destination may be determined. The second route may have a second cost. The first cost may be compared to the second cost, and the vehicle may use the comparison to set one of the first destination or the second destination as a current destination for a vehicle to cause the vehicle to control itself in an autonomous driving mode to the current destination.

Abstract: The present disclosure relates to optical transmitter modules, lidar systems, and methods of their manufacture. An example optical transmitter module includes a transparent substrate and a plurality of wires disposed along the transparent substrate. The optical transmitter module includes driver circuitry electrically-coupled to at least a portion of the plurality of wires and one or more light-emitter devices electrically-coupled to at least a portion of the plurality of wires. The light-emitter device(s) are configured to emit light pulses. The optical transmitter module also includes a fast axis collimation lens disposed along the transparent substrate. The fast axis collimation lens is configured to collimate the light pulses so as to provide collimated light. The optical transmitter module also includes one or more waveguide structures disposed along the transparent substrate within an optical region. The optical transmitter module also includes a lid configured to provide a sealed interior volume.

Abstract: The present disclosure relates to limitation of noise on light detectors using an aperture. One example implementation includes a system. The system includes a lens disposed relative to a scene. The lens focuses light from the scene. The system also includes an aperture defined within an opaque material. The system also includes a waveguide having a first side that receives light focused by the lens and transmitted through the aperture. The waveguide guides the received light toward a second side of the waveguide opposite to the first side. The waveguide has a third side extending between the first side and the second side. The system also includes an array of light detectors that intercepts and detects light propagating out of the third side of the waveguide.