Abstract: Aerodynamic aquatic weed removal and decontamination devices and decontamination methods are disclosed. In an embodiment, the aerodynamic aquatic weed removal and decontamination device includes a boat body and a shore-based device for remotely controlling the boat body. The boat body includes a power unit, a weed removal and decontamination unit, a positioning system, an environment perception system, and a shipborne controller. The power unit includes an engine, a propeller connected to the engine, and a rudder servo motor configured to control a heading of the boat body. The weed removal and decontamination unit includes a mesh conveyor. The positioning system includes a positioner configured to obtain real-time location information and heading information of the boat body. The environment perception system includes a wind sensor and a visual sensor. The shipborne controller is configured to control a navigation state of the boat body.

Abstract: Reckless behavior of drivers like, speeding, sudden acceleration and swerving through lanes can cause fatality and financial loss. Conventional methods mainly focus on driving style classification. The conventional methods mainly focus on driver classification and are not able to provide trip classification of a driver. Hence there is a challenge in trip classification of the driver based on acceleration data. The present disclosure for trip classification addresses the problem of end to end trip classification based on the acceleration data. Here, a journey is segmented into a plurality of sub-journey segments and each sub-journey segment is associated with a plurality of driving events. An event score is calculated for each sub-journey and a normalization is performed on the event score. Further, the journey is classified into at least one of good, average or bad based on the normalized data by utilizing a fuzzy based classification.

Abstract: The present disclosure relates to systems and methods for planning transportation routes. In one implementation, a method for simulating vehicle ridesharing is provided. The method may include receiving a first input of a geographical area and accessing map information of roadways in the geographical area. The method may also include receiving a second input indicative of at least one scenario of ridesharing demand in the geographical area and receiving a third input indicative of virtual vehicles designated to transport virtual passengers associated with the scenario. The method may further include initiating a transportation simulation of scenario to simulate rides of the virtual vehicles transporting the virtual passengers along the roadways. The method may also include determining, based on the transportation simulation, a performance level associated with the virtual vehicles and providing an output representative of the determined performance level.

Abstract: Systems, methods, and devices are disclosed for mapping historical information about behaviors of objects (vehicles, bicycles, pedestrians, etc.) at a location. Based on the mapped historical information, a prediction is determined about a behavior of an object proximate to an autonomous vehicle at the location, where the prediction is based on a statistical analysis of the historical information that is applied to the object. One or more behaviors of the AV are affected based on the prediction.

Abstract: Aspects of this disclosure relate to a system that uses images of a load handled by a crane as captured by cameras to monitor the load. The images may include different sets of outer perimeters of the load. The system may identify the outer perimeters and then define a safety zone that extends beyond these outer perimeters. In response to identifying an object within the safety zone, the system may execute a remedial action.

Abstract: Techniques for estimating a height range of an object in an environment are discussed herein. For example, a sensor, such as a lidar sensor, can capture three-dimensional data of an environment. The sensor data can be associated with a two-dimensional representation. A ground surface can be removed from the sensor data, and clustering techniques can be used to cluster remaining sensor data provided in a two-dimensional representation to determine object(s) represented therein. A height of a sensor object can be represented as a first height based on an extent of the sensor data associated with the object and can be represented as a second height based on beam spreading aspects of the sensor data and/or sensor data associated with additional objects. Thus, a minimum and/or maximum height of an object can be determined in a robust manner. Such height ranges can be used to control an autonomous vehicle.

Abstract: A vehicle includes a LIDAR. An object recognition device: sets a space having a height from an absolute position of a road surface as a noise space; classifies a LIDAR point cloud into a first point cloud included in the noise space and a second point cloud outside the noise space; extracts a fallen object candidate being a candidate for a fallen object on the road surface based on the first point cloud; extracts a tracking target candidate being a candidate for a tracking target based on the second point cloud; determines whether horizontal positions of the fallen object candidate and the tracking target candidate are consistent with each other; integrates the fallen object candidate and the tracking target candidate whose horizontal positions are consistent with each other, to be the tracking target; and recognizes the fallen object candidate not integrated into the tracking target as the fallen object.

Abstract: A control system for a machine includes a machine, a plurality of proximity sensors coupled to the machine, and a controller in communication with the plurality of proximity sensors. The controller is configured to activate and monitor proximity information from the proximity sensors during a containerization mode and indicate an alert if the proximity information is below a threshold distance.

Abstract: A method for obstacle detection and navigation of a vehicle using resolution-adaptive fusion includes performing, by a processor, a resolution-adaptive fusion of at least a first three-dimensional (3D) point cloud and a second 3D point cloud to generate a fused, denoised, and resolution-optimized 3D point cloud that represents an environment associated with the vehicle. The first 3D point cloud is generated by a first-type 3D scanning sensor, and the second 3D point cloud is generated by a second-type 3D scanning sensor. The second-type 3D scanning sensor includes a different resolution in each of a plurality of different measurement dimensions relative to the first-type 3D scanning sensor. The method also includes detecting obstacles and navigating the vehicle using the fused, denoised, and resolution-optimized 3D point cloud.

Abstract: A drive mode switch control device controls switching of driving between a driver and an autonomous driving function. The drive mode switch control device includes an operation information acquisition unit, a drive state switch unit, an attitude determination unit, and an approved target setting unit. The operation information acquisition unit acquires an operation information item associated with the driving operation input to at least one of a plurality of operation targets. The drive state switch unit executes an override that switches from an autonomous driving state to another driving state. The attitude determination unit acquires a plurality of detection information items related to driving attitudes of the driver, and determine whether each of the plurality of detection information items is appropriate for the driving operation. The approved target setting unit sets an approved operation target or a disapproved operation target to each of the plurality of operation targets.

Abstract: A combinable and detachable vehicle includes: a front connecting portion and a rear connecting portion provided at a front portion and a rear portion of a vehicle, respectively, and connected to another vehicle when the vehicle is combined with the other vehicle; a front monitoring portion and a rear monitoring portion monitoring a forward area and a rearward area of the vehicle, respectively; and a controller determining whether the other vehicle is connected to the front connecting portion or the rear connecting portion of the vehicle based on information received from the front connecting portion or the rear connecting portion, or the front monitoring portion or the rear monitoring portion, and controlling whether to operate each of a front component and a rear component of the vehicle depending on a direction of the vehicle is connected to the other vehicle.

Abstract: A method for perception-sharing between similarly-situated vehicles that are traveling on a portion of a roadway that is equipped with an intelligent vehicle highway system is described, and includes executing, in a multi-access edge computing cluster in communication with a roadside unit disposed to monitor a roadway, an application-layer routine. The application-layer routine includes collecting real-time data associated with a plurality of objects from each of the similarly-situated vehicles, predicting motion of each of the plurality of objects based upon the real-time data, object-matching the motion of each of the plurality of objects, and executing fusion of the plurality of objects based upon the object-matching of the motion of each of the plurality of objects. Locations of the similarly-situated vehicles traveling on the roadway are identified based upon the fusion of the plurality of objects. The locations are communicated to one of the similarly-situated vehicles.

Abstract: Some embodiments provide a commute application that provides a first presentation of several stops along a route. The commute application also receives a selection of a stop from the several stops along the route. The commute application further provides a second presentation for displaying several different routes that traverse through the selected stop.

Abstract: Devices, systems, and methods are provided for compressive sensing using photodiode data. A device may identify light detecting and ranging (LIDAR) data detected by a sensor, the LIDAR data including a first time-of-flight (ToF) and a second ToF. The device may generate, based on the first ToF, a first frequency value. The device may generate, based on the second ToF, a second frequency value. The device may generate, based on the first value, a first sinusoid. The device may generate, based on the second value, a second sinusoid. The device may generate, based on the first sinusoid and the second sinusoid, a compressed signal in a domain incoherent with the time domain. The device may extract range information based on the compressed signal, and may control operation of a vehicle based on the range information.

Abstract: A driving assistance apparatus predicts an appropriate operation amount which is an operation amount performed by a driver in correspondence with the external environment, and sets the appropriate operation amount range including the appropriate operation amount, an appropriate operation amount range including the appropriate operation amount, and changes the reaction force characteristics of the operation device when it is determined that the operation amount of the driver corresponding to the prediction time point of the appropriate operation amount is not included in the appropriate operation amount range.

Abstract: Method for improving global positioning performance of a first road vehicle (10), the method comprising, by means of a data server (3, 4, 4?): acquiring data from onboard sensors (2a, 2b, 2c, 2d, 2e, 2f, 2g) arranged on the first road vehicle (10) and on at least two neighbouring road vehicles (10?, 10?, 10??), the data comprising data on relative positions and data on heading angle and velocity of the road vehicles (10, 10?, 10?, 10??), and acquiring global positioning data of at least two of the road vehicles (10, 10?, 10?, 10??), processing (102) data comprising the global positioning data, the data, with corresponding timestamp, acquired from the onboard sensors (2a, 2b, 2c, 2d, 2e, 2f, 2g), and a motion model for each of the first road vehicle (10) and the at least two neighbouring road vehicles (10?, 10?, 10??) using a data fusion algorithm, calculating adjusted global positioning data for the first road vehicle (10) and communicating (104) the adjusted global positioning data to a positioning system (6

Abstract: Examples for flight navigation determination are presented herein. An example may involve obtaining a target destination for an aircraft and determining an initial flight path between a current location and the target destination. The flight path may include a series of waypoints for guiding navigation. The example may further involve obtaining terrain information that represents elevations of obstacles along the initial flight path and modifying the initial flight path to generate a revised flight path using the terrain information. The revised flight path may include modifications to the series of waypoints of the initial flight path such that navigation of the revised flight path avoids obstacles positioned along the initial flight path. The obstacles may have an elevation that exceeds an adjustable threshold elevation that depends on the initial flight path. The example may further involve providing the revised flight path to a navigation system of the aircraft.

Abstract: The present disclosure relates to a computer system, configured to receive two or more instances of a first route object comprising a geotemporal attribute comprising geospatial and temporal data to define an instance of the first route object; map the two or more instances of the first route object on to digital map data to define respective first route vertices in the digital map; and receive user input defining an interval and applying the defined interval intermediate chronologically adjacent respective first route vertices to define first waypoints between the respective first route vertices.

Abstract: A system includes a tracking system, a controller-circuit, and a device. The tracking system is configured to detect and track an object, and includes one or more of a computer vision system, a radar system, and a LIDAR system. The controller-circuit is disposed in a host vehicle, and is configured to receive detection signals from the tracking system, process the detection signals, determine, whether an object is detected based on the processed detecting signals, and in accordance with a determination that an object is detected, output command signals. The device is adapted to be mounted to the host vehicle, and is configured to receive the command signals and thereby provide a dynamic visual indication adapted to change in accordance with orientation changes between the host vehicle and the object. The dynamic visual indication is viewable from outside of the host vehicle.

Abstract: A processor-implemented method includes storing, for each of multiple road sections, section-specific road-nonlinearity values relating to road-nonlinearity characteristics of the respective road section. The method includes receiving, from a user, user-desired route-specific road-nonlinearity values relating to the road-nonlinearity characteristics. The method further includes calculating, from the stored section-specific road-nonlinearity values, for a candidate route, one or one route-specific road-nonlinearity values relating to the road-nonlinearity characteristics. The candidate route's route-specific road-nonlinearity values are compared to the user-desired route-specific road-nonlinearity values. Based on results of the comparison, the candidate route is select to be a recommended route. The recommended route is communicated to the user.