Abstract: Aspects of the present disclosure relate generally to limiting the use of an autonomous or semi-autonomous vehicle by particular occupants based on permission data. More specifically, permission data may include destinations, routes, and/or other information that is predefined or set by a third party. The vehicle may then access the permission data in order to transport the particular occupant to the predefined destination, for example, without deviation from the predefined route. The vehicle may drop the particular occupant off at the destination and may wait until the passenger is ready to move to another predefined destination. The permission data may be used to limit the ability of the particular occupant to change the route of the vehicle completely or by some maximum deviation value. For example, the vehicle may be able to deviate from the route up to a particular distance from or along the route.

Abstract: A system that includes a vehicle horn can reduce noise pollution caused by vehicles. The system can include a vehicle horn that has a signal-based mode and a sound-based mode. In the signal-based mode, the vehicle horn can output an electronic signal. In the sound-based mode, the vehicle horn can output an audible signal. The system can select one of the signal-based mode and the sound-based mode for the vehicle horn based on the driving environment data acquired by one or more sensors. Upon receiving a horn command, the system can cause the vehicle horn to output a signal based on the selected mode.

Abstract: An apparatus for controlling a host vehicle may include: a processor configured to calculate a cut-in possibility in which a nearby vehicle cuts into a lane on which the host vehicle travels ahead of the host vehicle, to determine a plurality of cut-in steps of the calculated cut-in possibility, and to control operation of the host vehicle so as to perform an inter-vehicle distance control operation or to provide a warning to a user of the host vehicle based on a state of the user in each of the plurality of cut-in steps; and storage configured to store the calculated cut-in possibility.

Abstract: A system and method for classifying driving behavior. The method includes training, via a supervised machine learning process, a classifier using a labeled training data set including at least one set of training features and corresponding training labels, wherein the classifier is trained to classify driving behavior, wherein the training features include training vehicle telemetries, wherein the training labels include at least one of driver labels and vehicle labels; and applying the classifier to an application data set including a plurality of application features to output a classification of driving behavior based on the application features, wherein the application features are extracted from application data including application vehicle telemetries for a vehicle.

Abstract: A method for correcting speed estimates for route planning using a machine-learned speed correction model trained on aggregated road data. Location and movement data collected from a plurality of mobile computing devices is aggregated on a server computer and used to train a speed correction model to correct estimated speeds corresponding to roads in one or more geographic regions. Speeds estimates for a road segment in a geographic region are corrected using a speed correction model trained on road data describing road segments in the same geographic region. In some embodiments, road data corresponding to one or more geographic regions is assigned to groups in training the speed correction model. The road data may be anonymized or segmented such that an originating device or route is unidentifiable. More fine-grained speed correction models may also be trained for different or additional factors than geographic region, such as day and/or time.

Abstract: A rotation detector has a first controller that monitors abnormality of a rotation number calculated by a rotation number calculator based on the calculated rotation number and a preset rotation angle. The first controller in a first system is configured to output rotation information calculated by a second controller in a second system upon having determination that the rotation number of the first system has abnormality, for a continuation of the rotation information calculation.

Abstract: Example techniques are described for determining a dynamic traffic-management plan based on factors such as predicted increases in traffic and known-structurally-deficient transportation infrastructure. Traffic patterns may be re-routed, particularly during high-congestion events, to reduce or avoid excessive weight loads travelling across weakened sections of roads, bridges, or the like.

Abstract: From a set of point data, a set of scattered rays is constructed. From the set of scattered rays, a set of ray slopes is computed. The set of ray slopes is mapped to a corresponding set of trigonometric functions. Using an optimization method, a parameter of the set of trigonometric functions is selected. Using an inverse of the set trigonometric functions, a vehicle mass corresponding to the set of point data is computed. Based on the vehicle mass, a threshold braking distance of a collision avoidance system of the vehicle is adjusted, the threshold braking distance comprising a distance from an object predicted to collide with the vehicle. By braking the vehicle at least the threshold braking distance from the object, a predicted collision between the vehicle and the object is avoided.

Abstract: Disclosed is an autonomous mobile robot including a main body, and a driving part positioned below the main body and configured to move the main body, wherein the driving part includes a rotation part that is rotatably provided and is configured to dispose a sensor module including one or more sensors outwards, a base positioned below the rotation part, and a driving wheel installed on the base, thereby embodying a sensing system with low cost and high efficiency.

Abstract: A method for autonomously maneuvering a vehicle in a rearward direction towards a point of interest is provided. The method includes receiving one or more images from a camera positioned on a back portion of the vehicle. The method includes overlaying a path on the one or more images. In addition, the method includes receiving a command by way of a user interface. The command includes instructions to adjust the path. The method includes adjusting the path based on the received command. The method also includes transmitting a drive command to a drive system supported by the vehicle. The drive command causes the vehicle to autonomously maneuver along the adjusted path in a rearward direction.

Abstract: A method in which a continuous estimation of the intermediate friction rate is carried out, allowing the integration of the method into a general friction compensation method so as to continuously improve the feel on the steering wheel, particularly for speeds below a determined threshold. Also, a method for friction compensation in an electrical power steering system, characterised in that the compensation method takes into account a continuous estimation of the intermediate friction rate obtained by the estimation method.

Abstract: Onboard engine control for vehicles is disclosed herein. An example method includes determining a moving average of duty cycle periods for a power component of a vehicle for recent power load events, each of the duty cycle periods having both on-time portions and off-time portions; and when the on-time portions of the moving average are above a predetermined percentage, an engine of the vehicle remains in an on-state condition, further wherein when a current off-time period of the power component exceeds a value equal to a predetermined multiplier applied to the off-time portions, the engine is placed in an off-state condition.

Abstract: To generate a machine learning model for controlling autonomous vehicles, training sensor data is obtained from sensors associated with one or more vehicles, the sensor data indicative of physical conditions of an environment in which the one or more vehicles operate, and a machine learning (ML) model is trained using the training sensor data. The ML model generates parameters of the environment in response to input sensor data. A controller in an autonomous vehicle receives sensor data from one or more sensors operating in the autonomous vehicle, applies the received sensor data to the ML model to obtain parameters of an environment in which the autonomous vehicle operates, provides the generated parameters to a motion planner component to generate decisions for controlling the autonomous vehicle, and causes the autonomous vehicle to maneuver in accordance with the generated decisions.

Abstract: A method includes receiving a worksite plan to be executed by at least one machine at a worksite from a computing device of a supervising entity, the controller being located at a non-line-of-sight (NLOS) location with respect to the worksite. The worksite plan may include a boundary of the worksite at which the worksite plan is implemented, at least one task defining the worksite plan, and a selection of at least one machine to perform the task. The method may include receiving a validation signal from a device located at the worksite, the validation indicating that the worksite is ready for implementation of the worksite plan based on at least one parameter of worksite readiness. The method may include selecting a first mode of operation of the machine to perform the task and transmitting first instructions to the machine to perform the task based on the first mode of operation.

Abstract: The disclosed systems can regulate access to an online mode for a dynamic transportation matching system. For example, based on a provider efficiency parameter associated with the dynamic transportation matching system, the disclosed systems can prevent a transportation provider device from switching to the online mode within a geographic area. In addition, the disclosed systems can detect a pattern of behavior and, based on a comparison between the pattern of behavior and a behavioral threshold, cause a transportation provider device to switch from the online mode to an offline mode. Further, the disclosed systems can provide a map interface that indicates where a transportation provider device can switch from the offline mode to the online mode. Additionally, the disclosed systems can determine priorities associated with transportation provider devices and, based on the prioritization, selectively allow the transportation provider devices to switch from the offline mode to the online mode.

Abstract: A safe exiting assistance system includes an object detector that detects an object which approaches from a rear side of a vehicle, a body detector that detects physical information of a passenger in the vehicle, a door opening/closing detector that detects door opening or closing information of the vehicle, a determiner that determines whether a situation requires alert to the passenger in response to detection results of the object detector and the door opening/closing detector and calculates a value sensed by the body detector in response to vehicle information set in advance, and a controller that differently controls a position and an irradiation angle of a light source in response to the physical information when information in which the alert is necessary is received from the determiner.

Abstract: Autonomous driving is one of the world's most challenging computational problems. Very large amounts of data from cameras, RADARs, LIDARs, and HD-Maps must be processed to generate commands to control the car safely and comfortably in real-time. This challenging task requires a dedicated supercomputer that is energy-efficient and low-power, complex high-performance software, and breakthroughs in deep learning AI algorithms. To meet this task, the present technology provides advanced systems and methods that facilitate autonomous driving functionality, including a platform for autonomous driving Levels 3, 4, and/or 5. In preferred embodiments, the technology provides an end-to-end platform with a flexible architecture, including an architecture for autonomous vehicles that leverages computer vision and known ADAS techniques, providing diversity and redundancy, and meeting functional safety standards.

Abstract: A method for fusing various sensor data of a vehicle within the scope of an object identification (OI). At least one identification feature (IF) detected by a first sensor (FS) and at least one IF detected by a second sensor (SS) for identifying at least one object in the vehicle's surroundings are received. In the task, at least one IF detected by the SS for inspecting the OI is received, the IF detected by the FS and the IF detected by the SS each representing a first measured variable (MV) and the IF detected by the SS representing a second MV independent of the first MV. In a further task, the IF detected by the FS is linked to the IF detected by the SS to generate a feature linkage. In a third task, the plausibility of the feature linkage is checked using the IF detected by the SS.

Abstract: A vehicle includes a controller powered by a controller power source independent from a vehicle power supply, programmed to responsive to detecting an in-vehicle network wakeup initiated by an electronic controller unit (ECU) requesting to communicate during a vehicle OFF state, record an ECU communication via the in-vehicle network; and responsive to detecting a vehicle ON state, send the recorded ECU communication to a server.

Abstract: A method for “hands-on” identification on a steering system having two subsystems connected to one another by an elastic connection. The elastic connection has a static friction state and a sliding friction state for a respective set of external state variables. The steering system is excited by an excitation vibration, which is generated by a controllable vibration generator and has a respective excitation amplitude and a respective excitation frequency, for a respective set of external state variables, in which the respective excitation amplitude and the respective excitation frequency for the currently present set of external state variables are taken from a prescribed table and the vibration generator is controlled with them. A reaction torque to the excitation vibration is measured using a sensor. A phase difference between the excitation vibration and the reaction torque is calculated to identify a “hands-off” state as well as a “hands-on” state.