Abstract: A method predicts the trajectory of an ego vehicle travelling in a main lane. A lane change by the ego vehicle from the main lane to an adjacent lane is determined according to an estimate of the dynamic behavior of a group of vehicles travelling in the adjacent lane. The group of vehicles includes at least one main vehicle which is located near the ego vehicle and a secondary vehicle which is located behind the ego vehicle.

Abstract: A system includes a processor configured to determine a driver identity. The processor is also configured to receive a request for a change to a driving mode and responsive to the request, enable or deny the driving mode based on mode-correlation to one of a predefined set of permissible driving modes pre-associated with the driver identity.

Abstract: A traffic monitoring apparatus (10) includes a vehicle information acquisition unit (11), a congestion determination unit (13), and a priority calculation unit (16). The vehicle information acquisition unit (11) acquires, from data received from each of a plurality of detection apparatuses (20), vehicle information regarding travelling states of vehicles that are present in the vicinity of a plurality of respective intersections. The congestion determination unit (13) determines, for each of the plurality of intersections, whether or not congestion is occurring based on the vehicle information and determines an intersection where the congestion has occurred to be a congested intersection. The priority calculation unit (16) calculates, for each of the plurality of continuous congestion paths, a priority level for implementing measures to eliminate congestion based on at least the vehicle travelling direction in the continuous congestion path.

Abstract: Systems and methods of using a common control scheme to autonomously controlling a vehicle during semi-autonomous and fully autonomous driving modes are provided. In particular, embodiments of the presently disclosed technology incorporate reference tracking for driving input and vehicle state into this common control scheme. In some embodiments, this common control scheme may be implemented using Model Predictive Control (MPC).

Abstract: The present disclosure provides a method of estimating a weight of a vehicle, including determining whether a current state is an enable state in which a vehicle stoppage event due to brake occurs from vehicle driving information, by a weight estimating system, when the current state is the enable state, removing noise by filtering an acceleration signal input from an acceleration sensor at a moment when the vehicle is stopped, by the weight estimating system, determining a period value of an acceleration signal from the acceleration signal from which noise is removed, by the weight estimating system, and estimating the weight of the vehicle using information on the determined period value, by the weight estimating system.

Abstract: A method of managing operator take-over of autonomous vehicle. The method includes gathering information on an external surrounding of the autonomous vehicle; analyzing the gathered information on the external surrounding of the autonomous vehicle to determine an upcoming traffic pattern; gathering information on an operator of the autonomous vehicle; analyzing the gathered information on the operator of the autonomous vehicle to determine an operator behavior; predicting an operator action based on the determined upcoming traffic pattern and the determined operator behavior; and initiating a predetermined vehicle response based on the predicted operator action. The predicting the operator action includes comparing the determined upcoming traffic pattern with a similar historic traffic pattern and retrieving a historical operator action in response to the similar historical pattern.

Abstract: Systems and methods for implementing one or more autonomous features for autonomous and semi-autonomous control of one or more vehicles are provided. More specifically, image data is obtained from an image acquisition device and processed utilizing one or more machine learning models to identify, track, and extract one or more features of the image utilized in decision making processes for providing steering angle and/or acceleration/deceleration input to one or more vehicle controllers. In some instances, techniques are employed such that the autonomous and semi-autonomous control of a vehicle changes between lane follow and vehicle follow modes. Further, a vehicle may determine whether to continue to follow a vehicle after the followed vehicle moves in relation to the following vehicle.

Abstract: A control device to be mounted on a vehicle includes a memory configured to store information related to priority levels of a plurality of applications that implements driving assistance functions and a processor. The processor is configured to receive, from the applications, application requests and identifiers that identify the respective applications. The processor is configured to arbitrate a plurality of the application requests based on the information and the identifiers. The processor is configured to output a request to an actuator based on a result of arbitration of the application requests.

Abstract: Systems and methods for implementing one or more autonomous features for autonomous and semi-autonomous control of one or more vehicles are provided. More specifically, image data may be obtained from an image acquisition device and processed utilizing one or more machine learning models to identify, track, and extract one or more features of the image utilized in decision making processes for providing steering angle and/or acceleration/deceleration input to one or more vehicle controllers. In some instances, techniques may be employed such that the autonomous and semi-autonomous control of a vehicle may change between vehicle follow and lane follow modes. In some instances, at least a portion of the machine learning model may be updated based on one or more conditions.

Abstract: Systems and methods for implementing one or more autonomous features for autonomous and semi-autonomous control of one or more vehicles are provided. More specifically, image data may be obtained from an image acquisition device and processed utilizing one or more machine learning models to identify, track, and extract one or more features of the image utilized in decision making processes for providing steering angle and/or acceleration/deceleration input to one or more vehicle controllers. In some instances, techniques may be employed such that the autonomous and semi-autonomous control of a vehicle may change between vehicle follow and lane follow modes. In some instances, at least a portion of the machine learning model may be updated based on one or more conditions.

Abstract: A projected recovery trajectory for an aircraft autopilot system is precomputed by providing a stored set of predefined recovery mode segments, including: a mode 1 segment that models the aircraft coasting; a mode 2 segment that models the aircraft executing a nose high recovery; a mode 3 segment that models the aircraft executing a nose low recovery; a mode 4 segment that models the aircraft executing a throttle only recovery; and a mode 5 segment that models the aircraft executing a terrain avoidance recovery. A processor generates at least one projected recovery trajectory based on a current state of the aircraft, where the processor selectively concatenates selected ones of the predefined recovery mode segments into a sequence and uses that sequence to generate the projected trajectory.

Abstract: This vehicle control device is provided with: an environment recognition unit for recognizing the surrounding environment of a vehicle; a parking route generation unit for generating a travel route to a parking position which is determined on the basis of the recognized surrounding environment; a signal input unit to which a parking command signal is input; and a travel control unit for causing the vehicle to travel along the travel route to the parking position on the basis of the input parking command signal. The vehicle control device drives the vehicle to travel to the parking position for automatic parking while behaving differently depending on whether the parking command signal input to the signal input unit is a first parking command signal or a second parking command signal.

Abstract: In a localization apparatus for a vehicle, a landmark detector detects a landmark from camera information acquired from a camera mounted to the vehicle. An associator associates the landmark with a specific set of radar information by setting an error region in which the landmark may exist, selecting, from a radar observation point cluster formed of a set of radar observation points corresponding to a set of radar information acquired from a radar mounted to the vehicle, a radar observation point cluster formed of a set of radar observation points included in the error region, and associating the landmark with the specific set of radar information corresponding to the selected radar observation point cluster. A positional relationship calculator calculates a positional relationship between the landmark and the vehicle based on the specific set of radar information associated with the landmark.

Abstract: A vehicle computer system implements techniques to predict behavior of objects detected by a vehicle operating in the environment. The techniques include using a model to determine a first object trajectory for an object (e.g., a predicted object trajectory) and/or a potential object in an occluded area, as well as a second object trajectory for the object or potential object (e.g., an adverse object trajectory). The model is configured to use one or more algorithms, classifiers, and/or computational resources to predict candidate trajectories for the vehicle based on at least one of the first object trajectory or the second object trajectory. Based on the predicted behavior of the object (or potential object) and the predicted candidate trajectories for the vehicle, a vehicle computer system controls operation of the vehicle.

Abstract: A method for operating a motor vehicle brake system having hydraulic wheel brakes; an atmospheric pressure pressure medium reservoir; a pressure modulation unit setting wheel-specific brake pressures, an electrohydraulic pressure provision device connected to the pressure modulation unit and the pressure medium reservoir which outputs brake system pressure to the pressure modulation unit and draws in pressure medium from the reservoir; a first and second pressure activation valve connecting the pressure provision device to the pressure modulation device. A piston of the piston-cylinder arrangement separates two pressure spaces from one another. A pressure build-up occurs in the first pressure space during a backward movement of the piston and in the second pressure space during a forward movement of the piston. During a brake pressure build-up by a forward stroke of the piston, the activation valve is opened and the first pressure space is hydraulically separated from the pressure medium reservoir.

Abstract: A steering control system for a commercial vehicle having a braking system to brake dissymmetrically wheels. The steering control system, including a sensor unit and a control module, is configured to detect a braking request or deceleration of the vehicle and/or to detect a lateral offset and to generate a brake indication signal and/or steering demand. The control module is configured to receive the brake indication signal and/or steering demand. If the steering demand is below a predetermined threshold value, the control module is configured to generate the steering signal only if the brake indication signal indicates a braking request. If the steering demand exceeds the predetermined threshold value, the control module is configured to generate the steering signal even if the brake indication signal indicates no braking request. The control module provides the steering signal to the braking system to brake the vehicle dissymmetrically to steer the vehicle.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that obtain scene features in an environment that includes an autonomous vehicle and a target agent at a current time point; determine whether the target agent is an emergency vehicle that is active at the current time point; generate from the scene features, an input (i) that includes the scene features and (ii) that indicates whether the target vehicle is an emergency vehicle that is active at the current time point; and process the input using a machine learning model that is configured to generate a behavior prediction output for the target agent that characterizes predicted future behavior of the target agent after the current time point.

Abstract: A method of mitigating thermal rotor bow in a rotor assembly of a turbine engine may include performing a plurality of motoring cycles. The plurality of motoring cycles may include receiving feedback on a temperature within a turbine engine in a post-shutdown state, actuating a starter motor when the temperature is greater than a predetermined threshold, operating the starter motor for a motoring time to exhaust some residual heat from the turbine engine, and shutting down the starter motor after the motoring time.

Abstract: A stability control system of a vehicle utilizing an electronic control unit that minimizes rollback of a vehicle as a result of wheel slip immediately following a hill start assist operation. The electronic braking control module controls actuation and de-actuation of vehicle brakes on an inclined surface. Immediately following a hill start assist operation on the inclined surface after each wheel brake is de-actuated for allowing forward movement of the vehicle up the hill, a split-mu road surface condition is detected in response to sensing wheel slip for each of the wheels. The electronic control unit determines a respective undriven, or non-dominant driven, wheel having the highest coefficient of friction among the undriven, or less dominant driven wheels, as determined by the wheel speeds.

Abstract: Methods, systems, and devices for wireless communications are described. In some systems, a user equipment (UE) (e.g., an unmanned aerial vehicle (UAV)) may receive an approved flight plan including approved flight plan sectors. The UE may also receive a query from a network node, the query including an indication of a subset of the plurality of approved flight plan sectors and a request for a plurality of waypoints of the UE within the indicated subset of the plurality of approved flight plan sectors. The UE may further determine, in response to receiving the query from the network node, a flight path including the plurality of waypoints of the UE for the indicated subset of the plurality of approved flight plan sectors based on the received approved flight plan, and the UE may transmit, to the network node, a flight declaration message including the waypoints of the UE.