Abstract: Aspects of the disclosure provide for airbag systems suited for autonomous vehicles. Such an airbag system may include a first chamber, a second chamber, a dual-stage inflator, an orifice blocker mechanism and a control module. The dual-stage inflator may be in fluid communication with the first chamber via a first orifice and in fluid communication with the second chamber via a second orifice. The orifice blocker mechanism may be configured to block and unblock gas flow from the second orifice to the second chamber. The control module may be configured to activate the dual-stage inflator in order to inflate one or both of the first chamber and the second chamber with gas based on a position of the orifice blocker mechanism relative to the second orifice.

Abstract: This technology relates to a system for cooling sensor components. The cooling system may include a sensor which has a sensor housing, a motor, a main vent, and a side vent. Internal sensor components may be positioned within the sensor housing. The motor may be configured to rotate the sensor housing around an axis. The rotation of the sensor housing may pull air into an interior portion of the sensor housing through the main vent, and the air pulled into the interior portion of the sensor housing may be exhausted out of the interior portion of the sensor housing through the side vent.

Abstract: An improved, efficient method for mapping world points from an environment (e.g., points generated by a LIDAR sensor of an autonomous vehicle) to locations (e.g., pixels) within rolling-shutter images taken of the environment is provided. This improved method allows for accurate localization of the world point in a rolling-shutter image via an iterative process that converges in very few iterations. The method poses the localization process as an iterative process for determining the time, within the rolling-shutter exposure period of the image, at which the world point was imaged by the camera. The method reduces the number of times the world point is projected into the normalized space of the camera image, often converging in three or fewer iterations.

Abstract: Aspects of the disclosure provide for controlling an autonomous vehicle to respond to queuing behaviors at pickup or drop-off locations. As an example, a request to pick up or drop off a passenger at a location may be received. The location may be determined to likely have a queue for picking up and dropping off passengers. Based on sensor data received from a perception system, whether a queue exists at the location may be determined. Once it is determined that a queue exists, it may be determined whether to join the queue to avoid inconveniencing other road users. Based on the determination to join the queue, the vehicle may be controlled to join the queue.

Abstract: The disclosure provides for a system. The system may include a rotational control system configured to rotate a seat of a vehicle, and one or more computing devices. The one or more computing devices may have one or more processors that are configured to determine that an impact is imminent at a location on the vehicle along a collision axis. A most favorable orientation may be determined by the one or more processors based on the determined location and collision axis. Using the rotational control system, the one or more processors may rotate the seat of the vehicle to the most favorable orientation in order to reduce risks of serious injury to a passenger in the seat caused by the imminent impact. A translational control system may be used by the one or more processors to translate the seat of the vehicle to a position relative to the determined location.

Abstract: The technology relates to handling of self-reflections of sensor signals off of a portion of a vehicle that is operating in an autonomous driving mode. Vehicle pose information and sensor pose information are determined at a given point in time while operating in the autonomous driving mode. Return signals from one or more scans of the environment are received from onboard sensors such as lidar sensors. The system evaluates, based on the vehicle and sensor pose information, whether a segment between a given one of the one or more sensors and a received point from a selected one of the return signals crosses any surface of a 3D model of the vehicle. The received point is identified as a self-return point. In response to identifying the received point as a self-return point, the vehicle is able to perform a driving operation in the autonomous driving mode.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for identifying high-priority agents in the vicinity of a vehicle. The high-priority agents can be identified based on a set of mutual importance scores in which each mutual importance score indicates an estimated mutual relevance between the vehicle and a different agent from a set of agents on planning decisions of the other. The mutual importance scores can be calculated based on importance scores assessed from the perspectives of both the vehicle and the agents.

Abstract: Example embodiments relate to controlling detection time in photodetectors. An example embodiment includes a device. The device includes a substrate. The device also includes a photodetector coupled to the substrate. The photodetector is arranged to detect light emitted from a light source that irradiates a top surface of the device. A depth of the substrate is at most 100 times a diffusion length of a minority carrier within the substrate so as to mitigate dark current arising from minority carriers photoexcited in the substrate based on the light emitted from the light source.

Abstract: Aspects of the disclosure relate to determining whether a vehicle should continue through an intersection. For example, the one or more of the vehicle's computers may identify a time when the traffic signal light will turn from yellow to red. The one or more computers may also estimate a location of a vehicle at the time when the traffic signal light will turn from yellow to red. A starting point of the intersection may be identified. Based on whether the estimated location of the vehicle is at least a threshold distance past the starting point at the time when the traffic signal light will turn from yellow to red, the computers can determine whether the vehicle should continue through the intersection.

Abstract: 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.

Abstract: A image sensor includes a first integrated circuit layer including pixel sensors that are grouped based on position into pixel sensor groups, a second integrated circuit layer in electrical communication with the first integrated circuit layer, the second integrated circuit layer including image processing circuitry groups that are configured to each receive pixel information from a corresponding pixel sensor group, the image processing circuitry groups further configured to perform image processing operations on the pixel information to provide processed pixel information during operation of the image sensor, a third integrated circuit layer in electrical communication with the second integrated circuit layer, and the third integrated circuit layer including neural network circuitry groups that are configured to each receive the processed pixel information from a corresponding image processing circuitry group and perform analysis for object detection on the processed pixel information during operation of the

Abstract: The technology includes a roof pod system for a vehicle configured to operate in one or more partly or fully autonomous self-driving modes. The roof pod system is arranged to sit above the roof of the vehicle, for instance at least 10-50 mm above the roof surface. The roof pod may be supported by a set of legs such as cross-rails. The roof pod system incorporates various sensors and related equipment to assist with self-driving operation. Some sensors may be arranged in the main housing of the roof pod, while others can be located in a dome-type structure extending above the main housing. A cabling harness assembly runs wiring and other links between the roof pod and the vehicle chassis.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for estimating a 3-D pose of an object of interest from image and point cloud data. In one aspect, a method includes obtaining an image of an environment; obtaining a point cloud of a three-dimensional region of the environment; generating a fused representation of the image and the point cloud; and processing the fused representation using a pose estimation neural network and in accordance with current values of a plurality of pose estimation network parameters to generate a pose estimation network output that specifies, for each of multiple keypoints, a respective estimated position in the three-dimensional region of the environment.

Abstract: The technology relates to localizing a vehicle. As one approach, a first LIDAR sensor scan data of an environment of the vehicle and localization data for the vehicle at a location where the first LIDAR sensor scan was captured are stored. Thereafter, the computing device is suspended and subsequently unsuspended. After the computing device is unsuspended, second LIDAR sensor scan data of the vehicle's environment is received. The first LIDAR sensor scan data is compared to the second LIDAR sensor scan data to determine whether the vehicle has moved. Based on the determination of whether the vehicle has moved from the location, the stored localization data is used to localize the vehicle. Other approaches are also described.

Abstract: A control command is provided to a vehicle control module that identifies one or more vehicle actions to be performed by the vehicle control module to control a position of an autonomous vehicle (AV) with respect to an external environment. Whether one or more conditions pertaining to the position of the AV with respect to the external environment are satisfied is determined. Responsive to determining that the one or more conditions pertaining to the position of the AV with respect to the external environment are satisfied, a first instruction is provide to the vehicle control module that permits the vehicle control module to deviate from the one or more vehicle actions identified in the control command and to perform an energy saving action with respect to the AV.

Abstract: Example embodiments relate to a substrate integrated waveguide (SIW) with dual circular polarizations. An example SIW may include a dielectric substrate and a first metallic layer coupled to a top surface of the dielectric substrate with a through-hole extending through the dielectric substrate and the first metallic layer. The SIW also includes a dielectric layer coupled to a top surface of the first metallic layer. A second metallic layer is coupled to a top surface of the dielectric layer. The second metallic layer includes a non-conductive opening, a plurality of feeds with a first end in the non-conductive opening and a second end including a single-ended termination, and an impedance transformer. The SIW also includes a third metallic layer coupled to a bottom of the dielectric substrate, and a set of metallic via-holes proximate the non-conductive opening and coupling the second metallic layer to the third metallic layer.

Abstract: A light detection and ranging (LIDAR) system can emit light toward an environment and detect responsively reflected light to determine a distance to one or more points in the environment. The reflected light can be detected by a plurality of plurality of photodiodes that are reverse-biased using a high voltage. Signals from the plurality of reverse-biased photodiodes can be amplified by respective transistors and applied to an analog-to-digital converter (ADC). The signal from a particular photodiode can be applied to the ADC by biasing a respective transistor corresponding to the particular photodiode while not biasing transistors corresponding to other photodiodes. The gain of each photodiode/transistor pair can be controlled by adjusting the bias voltage applied to each photodiode using a digital-to-analog converter. The gain of each photodiode/transistor pair can be controlled based on the detected temperature of each photodiode.

Abstract: The technology relates to detecting water or fouling on the exterior surface of a sensor cover, for instance due to precipitation or debris (fouling). Such objects on the sensor cover surface may degrade operation of the sensor, which can be problematic for vehicles operating in an autonomous driving mode. According to an aspect of the technology, a waveguide layer is provided on the sensor cover. A laser emits light at a selected wavelength along one side of the waveguide layer. The light waveform propagating along the waveguide layer is affected (distorted) by precipitation and/or fouling on the surface of this layer. A detector receives the distorted waveform. The system determines whether water and/or fouling is present based on the received waveform. This allows the system to determine whether to activate a cleaning module or to factor in the information when processing received sensor data.