According to one aspect, a vehicle includes hardware systems configured to support the autonomous or semi-autonomous operation of the vehicle. Hardware systems may include a main compute, a brain stem computer (BSC), and an aggregator/compute arrangement or redundant autonomy compute. Such hardware systems may cooperate to allow the vehicle to operate autonomously, and typically provide capabilities, e.g., redundant and/or backup capabilities, configured to enable the vehicle to continue to operate in the event that a primary system is not functioning as expected. The aggregator/compute arrangement may further be comprised of two substantially identical modules or computing assemblies, each configured to process sensor data and perform backup autonomy functionalities and/or teleoperations functionalities.

A vehicle positioning method via data fusion and a system using the same are disclosed. The method is performed in a processor electrically connected to a self-driving-vehicle controller and multiple electronic systems. The method is to perform a delay correction according to a first real-time coordinate, a second real-time coordinate, real-time lane recognition data, multiple vehicle dynamic parameters, and multiple vehicle information received from the multiple electronic systems with their weigh values, to generate a fusion positioning coordinate, and to determine confidence indexes. Then, the method is to output the first real-time coordinate, the second real-time coordinate, and the real-time lane recognition data that are processed by the delay correction, the fusion positioning coordinate, and the confidence indexes to the self-driving-vehicle controller for a self-driving operation.

An automotive sensor integration module including a plurality of sensors configured to detect an object outside a vehicle, and a signal processing unit configured to output, as sensing data, a plurality of pieces of detection data output from the plurality of sensors according to any one among the plurality of pieces of detection data at a substantially same timing based on a priority signal, or output, as the sensing data, the plurality of pieces detection data according to an external pulse at a substantially same timing.

A system for estimating a lateral velocity and a longitudinal velocity of a vehicle includes a plurality of sensors for monitoring data indicative of a travel state of the vehicle and one or more controllers in electronic communication with the plurality of sensors. The one or more controllers executes instructions to receive the data indicative of the travel state of the vehicle from the plurality of sensors. The one or more controllers estimate at least one initial estimated state of the vehicle based on the data indicative of the travel state of the vehicle. The one or more controllers fuse together the data indicative of the travel state of the vehicle with the at least one initial estimated state of the vehicle to determine the lateral velocity and a longitudinal velocity of the vehicle based on a single state estimation scheme.

Systems and methods are provided for constructing, using, and updating the sparse map for autonomous vehicle navigation. A system may comprise a processor and a memory. The memory may include instructions, which when executed on the processor, cause the processor to maintain a map; determine, based on analysis of image data, an existence of a non-transient condition that is inconsistent with the map, the image data from a camera integrated with the autonomous vehicle; and update the map.

The disclosure includes embodiments for a sensor system for multiple perspective sensor data sets. In some embodiments, a method executed by an ego vehicle includes receiving, from a set of remote vehicles, a set of sensor data describing a set of preliminary heatmaps for a roadway environment. The method includes reconciling discrepancies in the set of heatmaps and a preliminary heatmap of the ego vehicle to form a combined heatmap that describes the objects in the roadway environment as collectively observed by the onboard sensors of the set of remote vehicles and the ego vehicle. The method includes providing the combined heatmap to one or more of the remote vehicles included in the set of remote vehicles. The method includes modifying an operation of the ego vehicle based on the combined heatmap. For example, an operation of an autonomous driving system of the ego vehicle is modified.

To correct an axis deviation of a sensor. An aiming device, which calculates correction amounts of detection results of two or more sensors using the detection results of the sensors, includes: a sensor coordinate conversion unit that converts sensor data detected by the sensor from a coordinate system unique to the sensor into a predetermined unified coordinate system; a target selection unit that selects predetermined features from the sensor data detected by each of the sensors; a function fitting unit that defines functions each approximating an array state of the selected features for the respective sensors; a fitting result comparison unit that compares the functions each approximating the array state of the features detected by each of the sensors; and a correction value calculation unit that calculates a correction amount for converting coordinates of the features detected by the sensors from a result of the comparison of the functions.

Systems and methods are provided for autonomous navigation based on user intervention. In one implementation, a navigation system for a vehicle may include least one processor. The at least one processor may be programmed to receive from a camera, at least one environmental image associated with the vehicle, determine a navigational maneuver for the vehicle based on analysis of the at least one environmental image, cause the vehicle to initiate the navigational maneuver, receive a user input associated with a user's navigational response different from the initiated navigational maneuver, determine navigational situation information relating to the vehicle based on the received user input, and store the navigational situation information in association with information relating to the user input.

Systems and methods are provided for constructing, using, and updating the sparse map for autonomous vehicle navigation. A method may comprise processing, by a mapping server, collected navigation information from a plurality of vehicles obtained by sensors coupled to the plurality of vehicles, wherein the navigation information describes road lanes of a road segment; collecting data about landmarks identified proximate to the road segment, the landmarking including a traffic sign; generating, by the mapping server, an autonomous vehicle map for the road segment, wherein the autonomous vehicle map includes a spline corresponding to a lane in the road segment and the landmarks identified proximate to the road segment; and distributing, by the mapping server, the autonomous vehicle map to an autonomous vehicle for use in autonomous navigation over the road segment.

Systems and methods are provided for autonomous navigation based on user intervention. In one implementation, a navigation system for a vehicle may include least one processor. The at least one processor may be programmed to receive images acquired by a camera from an environment of a vehicle; determine a navigational maneuver for the vehicle based on analysis of one or more of the plurality of images; cause the vehicle to initiate the navigational maneuver; receive a user input causing an override to alter the initiated navigational maneuver; determine navigational situation information relating to the vehicle via analysis of the images; determine, based on the navigational situation information, whether the user input is associated with a transient condition; and when the user input is not associated with a transient condition, store the navigational situation information in association with information relating to the user input.