Abstract: A method may include obtaining first sensor data captured by a first sensor system and second sensor data captured by a second sensor system of a different type from the first sensor system. The method may include detecting a first object included in the first sensor data and a second object included in the second sensor data. The method may include assigning a first label to the first object and a second label to the second object after comparing the first and the second sensor data. The first and second labels may indicate degrees to which the first and the second objects match. Responsive to the first and second labels indicating that the first and the second objects match, the method may include designating a matched object representative of the first object and the second object and sending the matched object to a downstream computing system of an autonomous vehicle.

Abstract: A method may include obtaining input information that describes a driving operation of a vehicle and obtaining a rule that indicates an approved driving operation of the vehicle. The method may include parsing the rule using a domain-specific language to generate rule conditions in which the domain-specific language is a programming language that is specifically designed for analyzing driving operations of vehicles. The method may include representing the input information as observations relating to the vehicle in which each of the observations is comparable to one or more of the rules. The method may include comparing the observations to one or more respective comparable rule conditions and generating a grading summary that evaluates how well the observations satisfy the respective comparable rule conditions based on the comparison. A future driving operation of the vehicle may be adjusted based on the grading summary.

Abstract: A method may include obtaining, by an autonomous driving system, input information relating to an autonomous vehicle (AV). The method may include determining, by the autonomous driving system, one or more driving signals that describe operations of the AV. The method may include instructing the AV to move according to the driving signals and simulating, by a virtual driving system, movement of the AV based on the driving signals, wherein the simulating the movement of the AV occurs concurrently with the AV moving according to the driving signals as instructed.

Abstract: A method may include identifying computing systems corresponding to an autonomous vehicle (AV) in which each computing system is configured to perform at least one operation relating to driving the AV. The method may include determining whether the computing systems use a first respective real-world parameter as an input to any of the operations or generate a second respective real-world parameter as an output of the operations. A respective operation corresponding to a respective computing system may be designated for accelerated performance based on the determination, and the operations of the computing systems may be performed. A first operation not designated for accelerated performance may include first computations corresponding to the first operation after waiting for a synchronization delay period, and a second operation designated for accelerated performance may include second computations corresponding to the second operation and performed without waiting for the synchronization delay period.

Abstract: A method may include determining an alignment time based on a zero-crossing point corresponding to a LiDAR sensor and a horizontal field of view corresponding to an image-capturing sensor. The method may include determining a delay timing for initiating image capturing by the image-capturing sensor in which the delay timing is based on at least one of: the alignment time, a packet capture timing corresponding to the LiDAR sensor, and an average frame exposure duration corresponding to the image-capturing sensor. The method may include initiating data capture by the LiDAR sensor, and after the initiating of data capture by the LiDAR sensor and after the delay timing has elapsed, initiating data capture by the image-capturing sensor.

Abstract: A method may include obtaining localization information and control signal information relating to an autonomous vehicle (AV). The method may include computing first kinematic characteristics of the AV based on the localization information and the control signal information and generating a first vehicle model based on the first kinematic characteristics. The method may include determining one or more first control commands that provide instructions for actuating a driving operation of the AV. The first control commands may be determined based on the first vehicle model. The method may include simulating first movement of the AV according to the first control commands and the first vehicle model.

Abstract: A method may include obtaining sensor data relating to an autonomous vehicle (AV) and a total measurable world around the AV. The method may include identifying an operating environment of the AV and determining a projected computational load for computing subsystems that facilitate a driving operation performable by the AV corresponding to the identified environment. The method may include off-loading first computing subsystems of the computing subsystems in which computations of the first computing subsystems may be processed by an off-board cloud computing system and processing computations associated with second computing subsystems of the computing subsystems by an on-board computing system.

Abstract: An autonomous driving computing system may include an on-board computing system that is configured to perform first operations that include obtaining sensor data relating to an autonomous vehicle (AV). The first operations may include sending the obtained sensor data to an off-board cloud computing system. The autonomous driving computing system may include the off-board cloud computing system, which may be configured to perform second operations that include receiving the sensor data and performing computations relating to the driving operation of the AV based on the obtained sensor data. The second operations may include determining a control signal corresponding to a driving operation and sending the control signal to the on-board computing system. The first operations may involve the on-board computing system receiving, from the off-board cloud computing system, the control signal corresponding to the driving operation of the AV and performing the driving operation by implementing the control signal.

Abstract: An autonomous driving validation system may include a primary advanced autonomy system located on board an autonomous vehicle (AV) that includes sensors, computing subsystems, and a drive-by-wire system. The primary advanced autonomy system may be configured to process first computations relating to a driving operation of the AV. The autonomous driving validation system may also include an off-board autonomous cloud computing system that includes cloud computing subsystems. The off-board autonomous cloud computing system may be configured to process second computations that are similar to the first computations processed by the primary advanced autonomy system.

Abstract: A method may include obtaining first sensor data from a first sensor system and second sensor data from a second sensor system. The first and the second sensor systems may capture sensor data from a total measurable world. The method may include identifying a first object included in the first sensor data and a second object included in the second sensor data and determining first parameters corresponding to the first object and second parameters corresponding to the second object. The first parameters may be compared with the second parameters and whether the first object and the second object are a same object may be determined based on the comparing the first parameters and the second parameters. Responsive to determining that the first object and the second object are the same object, a set of objects representative of objects in the total measurable world including the same object may be generated.

Abstract: The present invention provides a chimeric antigen receptor (CAR) which binds a target antigen having a bulky extracellular domain, wherein the CAR comprises a Fab antigen binding domain. The present invention also provides nucleic acid sequences and constructs encoding such a CAR, cells expressing such a CAR and their therapeutic uses.

Abstract: A method may include obtaining input information relating to an environment in which an autonomous vehicle (AV) operates. The input information may describe a state of the AV, an operation of the AV within the environment, a property of the environment, or an object included in the environment. The method may include setting up a traffic rule profile for the AV that specifies societal traffic practices corresponding to a location of the environment. The method may include identifying a first traffic rule relevant to the AV based on the obtained input information and determining a first decision corresponding to the traffic rule profile and the first traffic rule. The method may include sending an instruction to a control system of the AV, the instruction describing a given operation of the AV responsive to the traffic rule profile and the first traffic rule according to the first decision.

Abstract: A method may include obtaining input information relating to an environment in which an autonomous vehicle (AV) operates, the input information describing at least one of: a state of the AV, an operation of the AV within the environment, a property of the environment, or an object included in the environment. The method may include identifying a first object in the vicinity of the AV based on the obtained input information. The method may include determining a first object rule corresponding to the first object, the first object rule indicating suggested driving behavior for interacting with the first object. The method may include determining a first decision that follows the first object rule and sending an instruction to a control system of the AV, the instruction describing a given operation of the AV responsive to the first object rule according to the first decision.

Abstract: A method may include obtaining input information relating to an environment in which an autonomous vehicle (AV) operates in which the input information describes at least one of: a state of the AV, an operation of the AV within the environment, a property of the environment, or an object included in the environment. The method may include identifying a region of interest that represents a section of the environment based on the input information and identifying a portion of the environment based on the identified region of interest. The portion of the environment may include an object that affects operation of the AV. The method may include determining a first decision that relates to the object and sending an instruction to a control system of the AV describing a given operation of the AV responsive to the object according to the first decision.

Abstract: A method may include obtaining one or more inputs in which each of the inputs describes at least one of: a state of an autonomous vehicle (AV) or a state of an object; and identifying a prediction context of the AV based on the inputs. The method may also include determining a relevancy of each object of a plurality of objects to the AV in relation to the prediction context; and outputting a set of relevant objects based on the relevancy determination for each of the plurality of objects. Another method may include obtaining a set of objects designated as relevant to operation of an AV; selecting a trajectory prediction approach for a given object based on context of the AV and characteristics of the given object; predicting a trajectory of the given object using the selected trajectory prediction approach; and outputting the given object and the predicted trajectory.

Abstract: A method may include obtaining one or more inputs in which each of the inputs describes at least one of: a state of an autonomous vehicle (AV) or a state of an object; and identifying a prediction context of the AV based on the inputs. The method may also include determining a relevancy of each object of a plurality of objects to the AV in relation to the prediction context; and outputting a set of relevant objects based on the relevancy determination for each of the plurality of objects. Another method may include obtaining a set of objects designated as relevant to operation of an AV; selecting a trajectory prediction approach for a given object based on context of the AV and characteristics of the given object; predicting a trajectory of the given object using the selected trajectory prediction approach; and outputting the given object and the predicted trajectory.

Abstract: A method may include obtaining sensor data about a total measurable world around an autonomous vehicle. The sensor data may be captured by sensor units co-located with the autonomous vehicle. The method may include generating a mapping dataset including the obtained sensor data and identifying data elements that each represents a point in the mapping dataset. The method may include sorting the data elements according to a structural data categorization that is a template for a high-definition map of the total measurable world and determining a mapping trajectory of the autonomous vehicle. The mapping trajectory may describe a localization and a path of motion of the autonomous vehicle. The method may include generating the high-definition map based on the structural data categorization and relative to the mapping trajectory of the autonomous vehicle, and the high-definition map may be updated based on the path of motion of the autonomous vehicle.

Abstract: A method may include obtaining first sensor data from a first sensor system and second sensor data from a second sensor system. The first and the second sensor systems may capture sensor data from a total measurable world. The method may include identifying a first object included in the first sensor data and a second object included in the second sensor data and determining first parameters corresponding to the first object and second parameters corresponding to the second object. The first parameters may be compared with the second parameters and whether the first object and the second object are a same object may be determined based on the comparing the first parameters and the second parameters. Responsive to determining that the first object and the second object are the same object, a set of objects representative of objects in the total measurable world including the same object may be generated.

Abstract: A method may include obtaining sensor data from one or more LiDAR units and determining a point-cloud corresponding to the sensor data obtained from each respective LiDAR unit. The method may include aggregating the point-clouds as an aggregated point-cloud and generating an initial proposal for a two-dimensional ground model made of multiple grid blocks. The method may include filtering out unrelated raw data points from each grid block of the plurality of grid blocks to generate a filtered point-cloud matrix. The method may include identifying one or more surface-points and one or more object-points included in the filtered point-cloud matrix and generating an array of extracted objects based on the object-points.

Abstract: A method may include obtaining sensor data describing a total measurable world around a motion sensor. The method may include processing the sensor data to generate a pre-compensation scan of the total measurable world around the motion sensor based on the sensor data. The method may include determining a delay between the obtaining the sensor data and the generation of the pre-compensation scan. The method may include obtaining motion data corresponding to motion of the motion sensor and generating a motion model of the motion sensor based on the motion data. The method may include generating an after-compensation scan of the motion sensor using the delay and the motion model to compensate for continued motion during the delay.