Abstract: A robot control device, abnormality diagnosis method, and non-transitory computer readable medium are provided. The robot control device (300) performs abnormality diagnosis of a robot body (200) including a motor (201) that rotates, from a starting angle position to a target angle position, a rotational shaft (204) for transmitting power to an arm (203) so that the arm performs a predetermined operation. The robot control device includes: a drive control unit (304) driving the motor so that the rotational shaft rotates from the starting angle position to the target angle position within a rotational speed range of the rotational shaft in which it is possible to detect a vibration component caused by an abnormality from among the vibration components generated along with the rotation of the rotational shaft; a vibration detection unit (305) detecting the vibration component; and a diagnosis unit performing abnormality diagnosis based on the detected vibration component.

Abstract: An information processing apparatus manages the operation of an alternative vehicle when a vehicle recovers from a failure. The information processing apparatus includes a controller. The controller is configured to determine, when it is judged that the vehicle is not operational due to failure of the vehicle, to introduce an alternative vehicle to replace the vehicle, and when recovery information indicating that the vehicle has recovered from the failure is acquired, maintain the determination to introduce the alternative vehicle or cancel the introduction of the alternative vehicle and redetermine the recovered vehicle as being operational.

Abstract: The embodiments of the present disclosure relate to a steering control device and method of a vehicle, specifically, may provide a steering control device and method of a vehicle capable of securing the driving stability by controlling the alignment of the vehicle wheels by rapidly detecting a misalignment between the steering angle of the steering wheel and the steering angle of the vehicle wheel.

Abstract: Systems, methods, and computer-readable media are disclosed for an enhanced valet mode for vehicles. An example method may include receiving a request for a temporary valet passcode. The example method may also include generating a temporary valet passcode. The example method may also include displaying a request for entry of the temporary valet passcode. The example method may also include determining that an input has been received. The example method may also include determining that a mobile device is located within the vehicle or within a threshold distance from the vehicle. The example method may also include preventing the vehicle from starting based on authorization of the mobile device for a period of time subsequent to the input being received.

Abstract: A method performed by a behavioral analyzing system for detection of an emergency vehicle approaching a vehicle from behind. The system derives a road configuration of a road along which the vehicle, based on ego-vehicle position obtained with support from localization sensors on-board the vehicle, is or was positioned. The system further determines unconventional driving criteria in view of the road configuration. The system determines based on surrounding vehicle detection data of a first and at least a second surrounding vehicle, obtained with support from surrounding detecting sensors on-board the vehicle, a respective first and at least a second surrounding vehicle trajectory along at least the road section. The system determines, when the first and the at least second surrounding vehicle trajectory fulfill at least a portion of the unconventional driving criteria, that an emergency vehicle is deemed to be approaching the vehicle from behind.

Abstract: A vehicle control apparatus for controlling a vehicle based on surrounding area information of the vehicle, comprising: a control unit capable of controlling the vehicle in a first state in which gripping of a steering apparatus by a driver of the vehicle is needed and a second state in which the gripping is not needed, based on the surrounding area information; and a warning output unit configured to output a deviation warning when the vehicle has approached a boundary of a travel path, wherein if the vehicle is in the second state, the control unit suppresses output of the deviation warning performed by the warning output unit compared to a case where the vehicle is in the first state.

Abstract: The disclosed computer-implemented method may include identifying a transportation task within a dynamic transportation network, accessing a database of door closing event locations, wherein the database is populated from observed door closing events within the dynamic transportation network, determining location information specifying at least one of a pickup location and a drop-off location for the transportation task based at least in part on the database of door closing event locations, and providing the location information to a transportation provider computing device, wherein a transportation provider performs the transportation task that comprises at least one of picking up a transportation requestor at the pickup location and dropping off the transportation requestor at the drop-off location. Other methods, systems, and computer-readable media are disclosed.

Abstract: An information processing server is configured to acquire target vehicle data having traveling conditions of target vehicles and position information of the target vehicles on a map, recognize, based on the target vehicle data, an unstable behavior position on the map where at least one of the target vehicles exhibits unstable behavior, measure an occurrence count of the unstable behavior positions within a predetermined period in a mesh preset on the map, enlarge the mesh to include the unstable behavior positions within the predetermined period such that the occurrence count of the unstable behavior positions is equal to or larger than a first threshold, and store the mesh and the unstable behavior positions of the mesh in a storage database related to each other.

Abstract: An energy management system for an electric autonomous vehicle comprises a battery and a controller comprising a processor and a non-transitory computer-readable medium. The system comprises a pre-allocation input mechanism transmitting an input signal to the processor relating to a travel destination for the vehicle. The system comprises a navigation module receiving a wireless signal from a satellite network relating to a current location of the vehicle. The system comprises an autonomous input mechanism powered by the battery and transmitting an autonomous signal to the processor relating to dynamic conditions for autonomous operation of the vehicle. The processor determines a route between the current location and the travel destination, performs autonomous operation of the vehicle along the route, and selectively powers or tunes the usage of the at least one autonomous input mechanism with the battery during autonomous operation of the vehicle.

Abstract: A transportation system includes an artificial intelligence system for processing a sensory input from a wearable device in a self-driving vehicle to determine an emotional state of a rider and optimizing a vehicle operating parameter to improve the rider emotional state. The artificial intelligence system detects the rider emotional state in the self-driving vehicle by recognition of patterns of emotional state indicative data from a set of wearable sensors worn by the rider. The patterns are indicative of at least one of a favorable emotional state and an unfavorable emotional state of the rider. The artificial intelligence system is to optimize, for achieving at least one of maintaining a detected favorable emotional state of the rider and achieving a favorable emotional state of a rider subsequent to a detection of an unfavorable emotional state, the operating parameter of the vehicle in response to the detected emotional state of the rider.

Abstract: A method of determining a flight path for an aerial vehicle, includes controlling the aerial vehicle to fly along a first route, identifying, during the flight along the first route and with aid of one or more processors, a change in a state of signal transmission occurring at a first location, in response to identifying the change, determining, by the one or more processors, a second location different from the first location, determining a second route to the second location, and controlling, by the one or more processors, the aerial vehicle to fly to and land at the second location. The change of the state of signal transmission indicates an abnormal state in a signal transmission between the aerial vehicle and a control device.

Abstract: A transportation system includes an artificial intelligence (AI) system for processing a sensory input from a wearable device in a self-driving vehicle to determine an emotional state of a rider and optimizing a vehicle operating parameter to improve the rider emotional state. The AI system includes a recurrent neural network to indicate a change in the emotional state of the rider by a recognition of patterns of emotional state indicative wearable sensor data from a set of wearable sensors worn by the rider. The patterns are indicative of a first degree of a favorable emotional state of the rider and/or a second degree of an unfavorable emotional state of the rider. The AI system further includes a radial basis function neural network to optimize, for achieving a target emotional state of the rider, the vehicle operating parameter in response to the indication of the change in the rider emotional state.

Abstract: An electronic device and an operating method thereof may be configured to detect input data having a first time interval, detect first prediction data having a second time interval based on the input data using a preset recursive network, and detect second prediction data having a third time interval based on the input data and the first prediction data using the recursive network. The recursive network may include an encoder configured to detect each of a plurality of feature vectors based on at least one of the input data or the first prediction data, an attention module configured to calculate each of pieces of importance of the feature vectors by calculating the importance of each feature vector, and a decoder configured to output at least one of the first prediction data or the second prediction data using the feature vectors based on the pieces of importance.

Abstract: A control device for a power steering device includes a low-pass filter unit including a cutoff frequency adjustment unit configured to adjust a cutoff frequency for partially attenuating a component of an input signal, set the cutoff frequency to a first cutoff frequency when a steering torque signal is lower than a first predetermined torque value, and to set the cutoff frequency to a second cutoff frequency lower than the first cutoff frequency when the steering torque signal is equal to or higher than the first predetermined torque value.

Abstract: A road condition estimation device 2 includes: a reception unit 21 that receives pieces of sensor information from automobiles respectively, each piece of sensor information including positional information that is information regarding a position at which an automobile travels, and spectral information obtained by converting an acoustic signal generated when the automobile travels at the position into information in a frequency domain; a first classification unit 22 that classifies the pieces of spectral information respectively for roads on which the automobiles travel, based on pieces of positional information included in the pieces of sensor information thus received; and an estimation unit 23 that estimates a condition of a road for each of the pieces of spectral information classified by the first classification unit.

Abstract: Sensor data and data from V2V messages are used to detect obstacles. Additional obstacles are identified in map data according to a vehicle's position. In response to detecting a turn intent, a controller calculates a turn path according to the detected obstacles and properties of the vehicle. The turn path is presented to a user, such as by display on a HUD or superimposing the turn path on an image from a forward facing camera. A desired steering angle to traverse the turn path may be displayed, such as relative to the current steering angle of the vehicle. Notifications regarding the path and turning intent of other vehicle may be displayed along with the turn path.

Abstract: A sensor controller, which controls a first optical sensor and a second optical sensor respectively having beam irradiation ranges, shifted from each other, for scanning a substantially front direction and a substantially side direction of a vehicle. The sensor controller: determines when an abnormality occurs in either the first optical sensor or the second optical sensor; identifies an abnormal sensor in which abnormality has occurred and a normal sensor that maintains normality; and controls a beam pattern in a beam irradiation range of the normal sensor to have a degeneration pattern with a high density in a region overlapping a beam irradiation range of the abnormal sensor.

Abstract: An apparatus for controlling platooning includes a recognizing device to recognize front information of a vehicle by using at least one sensor, a communication device to support vehicle to vehicle communication, and a processor connected with the recognizing device and the communication device. The processor acquires information on a first line in front of the vehicle, through the recognizing device, receives information on a second line, which is transmitted from a preceding vehicle, through the communication device, generates information on a third line by using the information on the first line and the information on the second line, based on information on the preceding vehicle, generates information on a final line by using the information on the first line, the information on the second line, and the information on the third line, and plans a path for the platooning by utilizing the information on the final line.

Abstract: A fleet management server is configured to receive, via a wireless transceiver, driver and vehicle information from a plurality of vehicles relating to a plurality of drivers. The server computes, based on the received driver and vehicle information, occurrence rates for predetermined vehicle events and predetermined vehicle error codes that are associated with possible vehicle tampering, and then compares, based on the received driver and vehicle information, an occurrence rate for the predetermined vehicle events and predetermined vehicle error codes of a first driver to occurrence rates for the predetermined vehicle events and predetermined vehicle error codes of one or more of the other plurality of drivers.

Abstract: Systems and methods are directed to an autonomy computing system for an autonomous vehicle. The autonomy computing system can include functional circuits associated with a first compute function of the autonomous vehicle. Each of the functional circuits can be configured to obtain sensor data that describes one or more aspects of an environment external to the autonomous vehicle at a current time. Each of the functional circuits can be configured to generate, over a time period and based on the sensor data, a respective output according to the specified order. The autonomy computing system can include monitoring circuits configured to evaluate an output consistency of the respective outputs, and in response to detecting an output inconsistency between two or more of the respective outputs, generate data indicative of a detected anomaly associated with the first autonomous compute function.