Abstract: In one embodiment, a method includes receiving a sequence of location data points associated with a vehicle from a first source and a sequence of motion data points associated with the vehicle from a second source. The method includes determining a first turn angle of the vehicle based on at least one location data point in the sequence of location data points associated with the first source. The method includes determining that an additional location data point in the sequence of location data points is inaccurate. The method includes determining a second turn angle of the vehicle by using at least one motion data point in the sequence of motion data points corresponding to the additional location data point that is inaccurate. The method includes determining a turn trajectory of the vehicle by using at least the first turn angle and the second turn angle.

Abstract: A target vehicle, for example a two-wheeled vehicle, for mounting onto an ADAS (Advanced Driver Assistance System) testing platform is provided. The target vehicle comprises one or more sensors and an actuation assembly comprising an actuator. The sensors are arranged to measure a parameter relating to the dynamics of the target vehicle and may for example comprise accelerometers. The actuation assembly adjusts the tilt of the target vehicle in dependence on the output of the sensor(s), for example by means of a control unit. The tilting of the vehicle during cornering may thus be simulated. The measuring of such a parameter and the adjusting of the tilt may be conducted remotely from the testing platform. The sensor(s), control unit and actuator assembly may be self-contained within the target vehicle. A method of modeling a VRU (Vulnerable Road User) for ADAS testing is also provided.

Abstract: A method for vehicle stabilization includes, in response to a determination that a fault occurred in a rear steering mechanism, identifying a fault type associated with the fault. The method also includes determining whether a position of a rack of the rear steering mechanism is controllable based on the fault type. The method also includes, in response to a determination that the position of the rack is controllable, selectively positioning the rack to a center position and holding, using a motor control system of the rear steering mechanism, the rack in the center position. The method also includes, in response to a determination that the position of the rack is not controllable, holding, using the motor control system of the rear steering mechanism, the rack in a current position.

Abstract: The disclosure includes embodiments for forming an on-demand stationary vehicular micro cloud. In some embodiments, a method for a connected vehicle includes receiving event data that describes a set of events configured for triggering a creation of the on-demand stationary vehicular micro cloud. The method includes receiving sensor data that describes a roadway environment. The method includes detecting, based at least in part on the sensor data, an occurrence of at least one event included in the set of events. The method includes modifying an operation of a communication unit of the connected vehicle to initiate the creation of the on-demand stationary vehicular micro cloud to address the occurrence of the at least one event.

Abstract: Systems and methods are disclosed for determining, and displaying, the regulatory compliance status of a motorized vehicle, a driver of a motorized vehicle, or a non-vehicle machine. An authorized agent, such as a law enforcement officer, can perform a remotely-initiated safe stop of a motorized vehicle to prevent a high-speed chase. A system management center can receive, store, and transmit regulatory compliance records indicating the regulatory compliance status of drivers, motorized vehicles, and non-vehicle machines. A motorized vehicle can detect, and report, a driver “tail-gating” the motorized vehicle. The regulatory compliance history of drivers, motorized vehicles, and non-vehicle machines can be queried by authorized users.

Abstract: The aircraft threat envelope protection system employs a threat envelope data structure in a computer-readable medium that stores at least one trigger condition for each of a plurality of different types of threats associated with the aircraft, and modeled using a common schema. A processor computes plural different projected trajectories representing different possible aircraft paths through spacetime. The processor associates at least some of the plurality of the threats to specific trigger points in spacetime along each of the projected trajectories. The processor will deprecate ones of the projected trajectories when they are deemed not viable to recover from a threat. The processor initiates an aircraft protective response when all projected trajectories but one have been deprecated and the aircraft is within a predetermined proximity to the closest trigger point in spacetime along the non-deprecated trajectory.

Abstract: Systems and methods for monitoring and analyzing vehicle data within a vehicle and providing analytical processing data to prospective users of vehicles are disclosed. In one embodiment, a method is disclosed comprising monitoring a communications bus installed within a vehicle, the communications bus transmitting data recorded by one or more sensors installed within the vehicle; detecting a message broadcast on the communications bus; extracting an event from the message, the extraction based on a pre-defined list of event types; storing the event in a secure storage device installed within the vehicle; determining that a transfer condition has occurred; and transferring the event data to a remote server in response to determining that the transfer condition has occurred.

Abstract: Information that identifies a location can be received. In response to a receipt of the information that identifies the location, a file can be retrieved. The file can be for the location. The file can include image data and a set of node data. The set of node data can include information that identifies nodes in a neural network, information that identifies inputs of the nodes, and values of weights to be applied to the inputs. In response to a retrieval of the file, the weights can be applied to the inputs of the nodes and the image data can be received for the neural network. In response to an application of the weights and a receipt of the image data, the neural network can be executed to produce a digital map for the location. The digital map for the location can be transmitted to an automotive navigation system.

Abstract: The invention relates to a system and method for navigating an autonomous driving vehicle (ADV) that utilizes an-onboard computer and/or one or more ADV control system nodes in an ADV network platform. The on-board computer receives battery monitoring and management data concerning a battery stack. The on-board computer, utilizing a battery management system, determines the current state of charge (SOC) and other information concerning the battery stack and determines if the estimated total amount of electrical power required to navigate an ADV along a generated route to reach the predetermined destination is available. In response to determining that the ADV cannot reach the predetermined destination, the on-board computer automatically initiates a dynamic routing algorithm, which utilizes artificial intelligence, to generate alternative routes in an effort to find a route that the ADV can navigate to reach the destination utilizing the current state of charge (SOC) of the battery stack.

Abstract: A method, computer system, and a computer program product for activity-based mapping and navigation is provided. The present invention may include receiving a number of reports indicating a tethered device charging event in proximity of a particular location in a venue. The present invention may further include, in response to the received number of reports satisfying a threshold number of reports, determining that the particular location includes at least one power source.

Abstract: A vehicular monitoring system (5) has a plurality of sensors (20, 30) that are used to sense the presence of objects (15) around a vehicle (10, 52) for detecting collision threats. At least one of the sensors is positioned such that a portion of the vehicle is at a predefined location relative to the sensor and is within the sensor's field of view. As an example, for an aircraft, a sensor may be positioned such that a portion of the aircraft's wing, aerospike, or other structure is within the sensor's field of view. The system is configured to automatically calibrate the sensor and, if desired, other sensors using the portion of the vehicle at the predefined location.

Abstract: An unmanned aerial vehicle (UAV) control system includes a first UAV, a second UAV that flies near the first UAV during a flight of the first UAV and is configured to obtain wind information about wind, and flight control means for controlling the flight of the first UAV based on the wind information obtained by the second UAV.

Abstract: A system and method for dynamic allocation of delivery resources is described. In an example implementation, the system may determine floating and static geographic areas based on delivery data, generate an initial mapping associating the static geographic area with addresses and/or routes, and generate one or more periodic routes based on the initial mapping. In some instances, the system may compute a route-value target for each of the periodic routes based on a load of the periodic routes and may then assign one or more floating addresses located in the one or more geographic regions to the periodic routes based on the route-value target. In some instances, the system may determine a delivery vehicle load plan based on the assignment to the routes and may automatically perform a defined operation using the delivery vehicle load plan.

Abstract: Embodiments described herein improve fuel economy by controlling a vehicle powertrain based on a predicted vehicle velocity. The vehicle velocity is predicted based on vehicle-to-vehicle data when a prediction horizon is a longer prediction horizon and the vehicle velocity is predicted based on historical drive cycle data when the prediction horizon is a shorter prediction horizon. A time duration of the shorter prediction horizon is shorter than the time duration of the longer prediction horizon. A plurality of drive cycles are established for both the longer and the shorter prediction horizons using a neural network. A shorter prediction horizon drive cycle uses nonlinear autoregressive exogenous model neural networks and the longer prediction horizon drive cycle uses two layer feedforward neural networks. The predicted vehicle velocity is determined from a similar drive cycle of the plurality of drive cycles of either the shorter and/or the longer prediction horizon drive cycles.

Abstract: Methods and systems are provided for controlling an engine idle-stop based on upcoming traffic and road conditions. In one example, a method may include receiving data including traffic information and road characteristics immediately ahead of a vehicle from one or more remote sources, and adjusting one or more vehicle thresholds based on the received data. A duration of a prospective engine idle-stop may be estimated based on the received data and an engine idle-stop may be initiated based on the duration of the prospective engine idle-stop and the adjusted one or more vehicle threshold.

Abstract: A method for operating a safety system of a motor vehicle includes correcting an output signal of a measuring sensor as a function of a current temperature value measured by a temperature sensor. The motor vehicle includes a sensor device with the measuring sensor to detect a collision and the temperature sensor. The method further includes determining an intrinsic heat of the measuring sensor generated by the operation of the measuring sensor and correcting the output signal as a function of the determined intrinsic heat.

Abstract: In one embodiment, a computer-implemented method of operating an autonomous driving vehicle (ADV) includes perceiving a driving environment surrounding the ADV based on sensor data obtained from one or more sensors mounted on the ADV, determining a driving scenario, in response to a driving decision based on the driving environment, applying a predetermined machine-learning model to data representing the driving environment and the driving scenario to generate a set of one or more driving parameters, and planning a trajectory to navigate the ADV using the set of the driving parameters according to the driving scenario through the driving environment.

Abstract: Techniques for multi-factor pathfinding are disclosed. A system receives multiple data sets that represent multiple physical properties associated with multiple three-dimensional coordinates of a physical environment. The system generates multiple weighted data layers corresponding to the physical properties, at least by applying weights to the data sets. The system generates a multi-factor costmap for the three-dimensional environment, based at least on the weighted data layers. The system determines a path through the three-dimensional environment, based at least on the multi-factor costmap.

Abstract: A drive mode switch control device acquires operation information. The drive mode switch control device switches a drive state among at least an autonomous drive state, a manual drive state, and a coordination drive state. The operation detection unit detects a first operation and a second operation based on the operation information when the drive state is not in the manual drive state. The second operation is the drive operation different from the first operation and input after the input of the first operation. The drive mode switch control device switches the drive state from the autonomous drive state to the coordination drive state based on a detection determination of the first operation. The drive mode switch control device switches the drive state from the coordination drive state to the manual drive state based on a detection determination of the first operation.

Abstract: Systems and methods are disclosed for determining, and displaying, the regulatory compliance status of a motorized vehicle, a driver of a motorized vehicle, or a non-vehicle machine. An authorized agent, such as a law enforcement officer, can perform a remotely-initiated safe stop of a motorized vehicle to prevent a high-speed chase. A system management center can receive, store, and transmit regulatory compliance records indicating the regulatory compliance status of drivers, motorized vehicles, and non-vehicle machines. A motorized vehicle can detect, and report, a driver “tail-gating” the motorized vehicle. The regulatory compliance history of drivers, motorized vehicles, and non-vehicle machines can be queried by authorized users.