Abstract: Systems and methods for onboard vehicle certificate distribution are provided. A system can include a plurality of devices including a master device for authenticating processes and one or more requesting devices. The master device can include a master host security service configured to authenticate the one or more processes of the system. The master host security service can run a certificate authority to generate a root certificate and a private root key corresponding to the root certificate. A respective host security service can receive a request for a process manifest for a requesting process of a respective device from a respective orchestration service. The respective host security service can generate the process manifest for the requesting process and provide the process manifest to the requesting process. The requesting process can use the process manifest to communicate with the certificate authority to obtain an operational certificate based on the root certificate.

Abstract: A system and method for combining multiple functions of a light detection and ranging (LIDAR) system includes receiving a second optical beam generated by the laser source or a second laser source, wherein the second optical beam is associated with a second local oscillator (LO); splitting the second optical beam into a third split optical beam and a fourth split optical beam; transmitting, to the optical device, the third split optical beam and the fourth split optical beam; receiving, from the optical device, a third reflected beam that is associated with the third split optical beam and a fourth reflected beam that is associated with the fourth split optical beam; and pairing the third reflected beam with the second LO signal and the fourth reflected beam with the second LO signal.

Abstract: The present disclosure is directed to deviating from a planned path for an autonomous vehicle. In particular, a computing system comprising one or more computing devices physically located onboard an autonomous vehicle can identify one or more boundaries at least in part defining a lane in which the autonomous vehicle is traveling along a path of a planned route. Responsive to identifying one or more obstructions ahead of the autonomous vehicle along the path, the computing system can: determine one or more deviations from the path that would result in the autonomous vehicle avoiding the obstruction(s) and at least partially crossing at least one of the one or more boundaries; and generate, based at least in part on the deviation(s), a motion plan instructing the autonomous vehicle to deviate from the path such that it avoids the obstruction(s) and continues traveling along the planned route.

Abstract: Systems and methods for generating attention masks are provided. In particular, a computing system can access sensor data and map data for an area around an autonomous vehicle. The computing system can generate a voxel grid representation of the sensor data and map data. The computing system can generate an attention mask based on the voxel grid representation. The computing system can generate, by using the voxel grid representation and the attention mask as input to a machine-learned model, an attention weighted feature map. The computing system can determine using the attention weighted feature map, a planning cost volume for an area around the autonomous vehicle. The computing system can select a trajectory for the autonomous vehicle based, at least in part, on the planning cost volume.

Abstract: A LIDAR sensor for an autonomous vehicle (AV) can include one or more lasers outputting one or more laser beams, one or more non-mechanical optical components to (i) receive the one or more laser beams, (ii) configure a field of view of the LIDAR sensor, and (iii) output modulated frequencies from the one or more laser beams, and one or more photodetectors to detect return signals based on the outputted modulated frequencies from the one or more laser beams.

Abstract: A light detection and ranging (LIDAR) sensor system includes a dual-polarization optical antenna, a single-polarization optical antenna, a first receiver, and a second receiver. The dual-polarization optical antenna is configured to (i) emit a transmit beam with a first polarization orientation and (ii) and detect a return beam having a second polarization orientation. The single-polarization optical antenna is configured to detect the return beam having the second polarization orientation.

Abstract: A light detection and ranging (LIDAR) system includes a transmitter, a first receiver, a second receiver, and one or more processors. The transmitter is configured to output a transmit beam. The first receiver is positioned on a first side of the transmitter and is configured to receive a first component of a return beam from reflection of the transmit beam by an object. The second receiver is positioned on a second side of the transmitter and is configured to receive a second component of the return beam. The one or more processors are configured to determine at least one of a range to the object or a velocity of the object and control operation of the autonomous vehicle based on the at least one of the range or the velocity.

Abstract: A method includes obtaining a first track associated with an object. A first set of parameters is generated based on the first track. Measurement data are obtained from one or more sensors. A first set of features are extracted from the measurement data. Based on the first set of parameters and the first set of features, a second set of parameters are generated by a machine learning model. The second set of parameters represent an adjustment to the first set of parameters. Based on the second set of parameters, the first track is adjusted to generate a second track associated with the object. The second track is provided to an autonomous vehicle control system for autonomous control of a vehicle.

Abstract: Systems, methods, tangible non-transitory computer-readable media, and devices for operating an autonomous vehicle are provided. For example, the disclosed technology can include receiving state data that includes information associated with states of an autonomous vehicle and an environment external to the autonomous vehicle. Responsive to the state data satisfying vehicle stoppage criteria, vehicle stoppage conditions can be determined to have occurred. A severity level of the vehicle stoppage conditions can be selected from a plurality of available severity levels respectively associated with a plurality of different sets of constraints. A motion plan can be generated based on the state data. The motion plan can include information associated with locations for the autonomous vehicle to traverse at time intervals corresponding to the locations. Further, the locations can include a current location of the autonomous vehicle and a destination location at which the autonomous vehicle stops traveling.

Abstract: Systems and methods are disclosed for detecting and predicting the motion of objects within the surrounding environment of a system such as an autonomous vehicle. For example, an autonomous vehicle can obtain sensor data from a plurality of sensors comprising at least two different sensor modalities (e.g., RADAR, LIDAR, camera) and fused together to create a fused sensor sample. The fused sensor sample can then be provided as input to a machine learning model (e.g., a machine learning model for object detection and/or motion prediction). The machine learning model can have been trained by independently applying sensor dropout to the at least two different sensor modalities. Outputs received from the machine learning model in response to receipt of the fused sensor samples are characterized by improved generalization performance over multiple sensor modalities, thus yielding improved performance in detecting objects and predicting their future locations, as well as improved navigation performance.

Abstract: A light detection and ranging (LIDAR) sensor system includes a plurality of LIDAR pixels and a local oscillator module. The local oscillator module is coupled to the plurality of LIDAR pixels. The local oscillator module includes a first local oscillator input configured to receive a first local oscillator signal and a second local oscillator input configured to receive a second local oscillator signal. The local oscillator module is configured to provide the first local oscillator signal or the second local oscillator signal to a first LIDAR pixel of the plurality of LIDAR pixels.

Abstract: In some implementations, a light detection and ranging (LIDAR) system includes a laser source configured to provide an optical signal at a first signal power, an amplifier having a plurality of gain levels, at which the amplifier is configured to amplify the optical signal, and one or more processors. The one or more processors are configured to, based on the first signal power and a duty cycle of the optical signal, vary a gain level of the amplifier from the plurality of gain levels to generate a pulse signal, transmit the pulse signal from the amplifier to an environment, receive a reflected signal that is reflected from an object, responsive to transmitting the pulse signal, and determine a range to the object based on an electrical signal associated with the reflected signal.

Abstract: Systems and methods for identifying travel way features in real time are provided. A method can include receiving two-dimensional and three-dimensional data associated with the surrounding environment of a vehicle. The method can include providing the two-dimensional data as one or more input into a machine-learned segmentation model to output a two-dimensional segmentation. The method can include fusing the two-dimensional segmentation with the three-dimensional data to generate a three-dimensional segmentation. The method can include storing the three-dimensional segmentation in a classification database with data indicative of one or more previously generated three-dimensional segmentations. The method can include providing one or more datapoint sets from the classification database as one or more inputs into a machine-learned enhancing model to obtain an enhanced three-dimensional segmentation.

Abstract: An autonomous vehicle control system may include one or more processors configured to receive an electrical signal generated based on a returned optical signal that is reflected from an object. The one or more processors may determine a Doppler frequency shift of the returned optical signal over a first duration of the electrical signal. The one or more processors may generate a corrected electrical signal based on the Doppler frequency shift. The one or more processors may determine a range to the object based on the corrected electrical signal over a second duration that is shorter than the first duration. The one or more processors may control at least one of a steering system or a braking system based on the range.

Abstract: Map data describing various geometric elements in a map used in autonomous vehicle localization is selectively weighted to vary the relative contributions of different geometric elements to an alignment operation. By doing so, the alignment operation may be biased to emphasize geometric elements associated with objects in an environment that are relatively more stable, e.g., roadways, walls, buildings, etc., over objects that are relatively less stable, e.g., vegetation, thereby in many instances improving alignment operation performance and/or availability.

Abstract: A light detection and ranging (LIDAR) system include one or more LIDAR pixels including a transmit optical antenna, a receive optical antenna, a first receiver, and a second receiver. The transmit optical antenna is configured to emit a transmit beam. The receive optical antenna is configured to detect (i) a first polarization orientation of a returning beam and (ii) a second polarization orientation of the returning beam.

Abstract: An example method includes (a) obtaining sensor data descriptive of an environment of an autonomous vehicle; (b) obtaining a plurality of travel way markers from map data descriptive of the environment; (c) determining, using a machine-learned object detection model and based on the sensor data, an association between one or more travel way markers of the plurality of travel way markers and an object in the environment; and (d) generating, using the machine-learned object detection model, an offset with respect to the one or more travel way markers of a spatial region of the environment associated with the object.

Abstract: A light detection and ranging (LIDAR) system for a vehicle can include: a light source configured to output a beam; a photonics integrated circuit (PIC) including a semiconductor die, the semiconductor die comprising a substrate having two or more semiconductor devices formed on the substrate, the two or more semiconductor devices configured to receive the beam from the light source and modify the beam and at least one photonics die coupled to the semiconductor die, the at least one photonics die comprising at least a transmitter configured to receive the beam from the semiconductor die; and one or more optics configured to receive the beam from the transmitter and emit the beam towards an object in an environment of the vehicle.