Abstract: A network system, such as a transport management system, efficiently allocates resources by monitoring the geospatial and topological locations of a rider responsive to receiving a trip request. A trip management module matches a rider with an available driver based in part on an comparison of the estimated times of arrival of the rider and the driver at the pickup location. A client positioning module monitors the rider's progress through nodes and edges in a topological graph associated with the origin location based on radio signatures received at the rider client device. A client ETA module calculates a rider ETA based on the rider's rate of travel through the origin location represented by nodes in the topological graph. Responsive to determining that the rider ETA and the driver ETA vary by over a threshold amount of time, the trip management module matches the rider with a second available driver.

Abstract: In a method for predicting a turbine outlet temperature at a future use of a gas turbine based on a past use thereof, the turbine outlet temperature (objective variable) at the future use is predicted by a turbine outlet temperature model by using a parameter (explanatory variable) in environmental and operational conditions planned for the future use and a rotating speed of a fan (explanatory variable) planned for the future use, and coefficients with respect to the explanatory variables are identified through a learning. In the learning, the coefficients are identified based on a regression learning of the explanatory variables and the objective variable of the turbine outlet temperature model made by using the parameter, the rotating speed of the fan and the turbine outlet temperature at the past use of the gas turbine.

Abstract: An automated driving system and method of autonomously driving a vehicle. The system includes for a vehicle including at least one sensor device configured to detect the vehicle position and sense environment characteristics of the vehicle, an electronic control device configured to control autonomous driving of the vehicle based on an output of the sensor device, in which the controlling of autonomous driving includes an autonomous overtaking functionality for overtaking by changing the lane, and disable the autonomous overtaking functionality, in case at least one of a set of predetermined overtaking conditions is not satisfied.

Abstract: A vehicle system comprising a plurality of subsystems, each of the plurality of subsystems configured to perform at least a portion of at least one of a plurality of functions. The plurality of functions are organized in a hierarchy of functions including complex higher order functions and simpler lower order functions. The vehicle system further comprises an advanced computing module configured to control the plurality of subsystems in order to perform a higher order function and a lower order function that supports the higher order function. The advanced computing module comprises software instructions including a first gate point. The first gate point may be activated to prevent the advanced computing module from performing the higher order function.

Abstract: A lane changing method, device for a driverless vehicle and a computer-readable storage medium are provided. The lane changing method includes: determining candidate lanes, to which a lane changing is to be performed, based on a travelling intention of a main vehicle; screening the candidate lanes based on lane changing conditions of the candidate lanes; selecting, from the screened candidate lanes, a target lane, to which the lane changing of the main vehicle is to be performed; determining whether the lane changing of the main vehicle from a current lane to the target lane is safe; and performing the lane changing, if the lane changing is determined to be safe.

Abstract: Example implementations described herein involve a system for predicting vehicle behavior with a higher efficiency which can reduce the number of sensors and the amount of data needed for vehicle control systems. Example implementations described herein can be used for any type of vehicle control system, such as a powertrain control system, an adaptive cruise control system, an autonomous driving system, because the target vehicle to be predicted can be replaced to a preceding vehicle, a surrounding vehicle, and an ego vehicle.

Abstract: A travel assistance device configured to execute travel assistance when a plurality of vehicles form a vehicle group and travel in a line. The travel assistance device includes: a first control unit configured to, if there is lane-change instruction for the vehicle group, cause an end-of-line vehicle to change lanes, such vehicle being the vehicle from among the plurality of vehicles forming the vehicle group that is positioned at the end of the line; and a second control unit configured to, following the lane change of the end-of-line vehicle, allow a lane change for at least one vehicle that is other than the end-of-line vehicle and that is one of the vehicles forming the vehicle group.

Abstract: A moving body behavior prediction device predicts the behavior of a moving body comprising: a travel environment recognition unit that acquires external information DS and recognizes the travel environment; a prediction model evaluation value storage unit that stores an evaluation value for each travel environment in relation to prediction models prepared in advance; a prediction model determination unit 112 that determines the prediction model corresponding to the travel environment recognized by the travel environment recognition unit from among the prediction models, the determination being performed on the basis of the travel environment recognized by the travel environment recognition unit and the evaluation value stored in the prediction model evaluation value storage unit; and a behavior prediction unit 113 that predicts the behavior of the moving body using the prediction model determined by the prediction model determination unit.

Abstract: The target setting unit 7 sets a target state of the self-driving vehicle 10. The planned route creating unit 3 creates a planned route of the self-driving vehicle 10 for realizing the target state. The transmission unit 6 transmits the planned route to another vehicle. The response receiving unit 9 receives, from another vehicle, a notification indicating agreement with the planned route or disagreement with the planned route as a response to the planned route. The traveling control unit 8 controls the self-driving vehicle 10 so as to cause the self-driving vehicle 10 to travel along the planned route when the response receiving unit 9 has received the notification indicating agreement with the planned route.

Abstract: A vehicle controller includes a first detector configured to detect a traveling state of a host vehicle, a second detector configured to detect a traveling state of an other vehicle that travels along a main lane when the host vehicle travels along a merging road and configured to detect a traffic volume of the main lane, and a merging controller configured to control merging of the host vehicle regardless of a lane changing of the other vehicle when a distance in a forward/rearward direction of the host vehicle with respect to the other vehicle is equal to or greater than a predetermined amount, wherein the merging controller facilitates starting of merging control of the host vehicle when it is detected that a traffic volume of the main lane by the second detector is equal to or greater than a predetermined amount.

Abstract: A control device that controls a vehicle includes: an input unit to which an image including another vehicle or a driver of another vehicle is input; and a control unit that outputs a signal for controlling the vehicle generated based on danger in the another vehicle determined based on the image.

Abstract: Methods, devices and systems enable controlling an autonomous vehicle by identifying vehicles that are within a threshold distance of the autonomous vehicle, determining an autonomous capability metric of each of the identified vehicles, and adjusting a driving parameter of the autonomous vehicle based on the determined autonomous capability metric of each of the identified vehicles. Adjusting a driving parameter may include adjusting one or more of a minimum separation distance, a minimum following distance, a speed parameter, or an acceleration rate parameter.

Abstract: A method of controlling a movable object includes receiving, from a user interface of a terminal, selection of a mode from a plurality of modes that include at least one of an angle-to-angle control mode, an angle-to-speed control mode, or an angle-to-acceleration control mode; detecting, by a sensor of the terminal, an attitude of the terminal; and generating, at the terminal, a control signal configured to control a rotational attribute of the movable object based on the selected mode and the detected attitude of the terminal.

Abstract: A vehicle control device includes a driving controller configured to control a speed and steering of a vehicle to perform an automatic lane change, in which the driving controller limits the automatic lane change when it is detected that the vehicle is in a first area having a length of a first distance in a longitudinal direction of a road with a starting point of a specific road structure set as a reference or in a second area having a length of a second distance in the longitudinal direction of the road with an end point of the specific road structure set as a reference on the basis of information of at least one of an external recognition result and map information.

Abstract: The movement of the vehicle is controlled so that the travel time does not become long while reducing the fuel consumption of the vehicle. A vehicle control device 1 that automatically controls a speed of a vehicle is configured to include an acceleration/deceleration pattern setting unit 22 that sets a plurality of acceleration/deceleration patterns including acceleration and deceleration in a traveling scheduled zone and sets the speed to be the same in a region between an acceleration region and a deceleration region in each acceleration/deceleration pattern based on traveling history in a traveling scheduled zone.

Abstract: Systems, methods, and other embodiments described herein relate to generating depth estimates of an environment depicted in a monocular image. In one embodiment, a method includes identifying semantic features in the monocular image according to a semantic model. The method includes injecting the semantic features into a depth model using pixel-adaptive convolutions. The method includes generating a depth map from the monocular image using the depth model that is guided by the semantic features. The pixel-adaptive convolutions are integrated into a decoder of the depth model. The method includes providing the depth map as the depth estimates for the monocular image.

Abstract: A travel control device whereby wasted energy consumption can be suppressed. The travel control device (100) has: a leading vehicle detection unit (110) that, once a leading vehicle that has been followed at no more than a set vehicle speed has gone, determines whether or not a new leading vehicle has been detected within a prescribed time; a speed determination unit (120) that, if a new leading vehicle has been detected, determines whether or not the speed difference between the speed of a host vehicle and the speed of the leading vehicle is within a set range having zero as the reference therefor; and a travel control unit (130) that, if the speed difference is within the set range, controls the host vehicle so as to prohibit acceleration for reducing the distance between the host vehicle and the new leading vehicle to a set inter-vehicle distance.

Abstract: A device may receive an architectural floor plan of an interior of a building, and may process the architectural floor plan, with a vectorization model, to generate a vectorized floor plan of polygons. The device may process the vectorized floor plan, with a convex hull model, to create convex hull polygons around the polygons of the vectorized floor plan, and may reduce a quantity of vertices associated with the convex hull polygons to generate simplified convex hull polygons. The device may generate, based on the simplified convex hull polygons, one of a visibility graph that identifies potential paths through the interior of the building, or a walking path network through the interior. The device may process the one of the visibility graph or the walking path network, with a pathfinding model, to identify paths through the interior of the building, and may perform actions based on the identified paths.

Abstract: A collision avoidance control method for a vehicle, through which a collision avoidance control apparatus controls a vehicle to avoid a collision. The collision avoidance control method includes: calculating a TTC (Time To Collision) between the vehicle and a rearward vehicle, when the rearward vehicle approaching the rear of the vehicle is sensed; determining whether the vehicle and the rearward vehicle are likely to collide with each other, by comparing the TTC to a preset reference TTC; and performing a collision avoidance function when it is determined that the vehicle and the rearward vehicle are likely to collide with each other, the collision avoidance function including one or more of a collision risk warning signal output function, a forward acceleration control function and a lane change control function.

Abstract: A plurality of candidate estimated paths (301) for a vehicle (100) to travel to an intermediate destination (300) while avoiding a moving object present by estimation time is generated depending on cost information of lanes (200, 201), and an estimated path selected from the plurality of candidate estimated paths (301) is set as a path of the vehicle (100) for each estimation time.