The invention discloses a house structure with expandable function, comprising a house body (20), wherein an elevator shaft (21) is provided in the house body (20), and a carrying elevator (22) is provided in the elevator shaft (21); a multifunctional balcony (23) is further provided on both sides of the elevator shaft (21), and the multifunctional balcony (23) and the elevator shaft (21) are connected via a landing door (24); the car of the carrying elevator (22) is provided with a car front door, a car left door and a car right door, respectively; a multifunctional cabin (1) can be detachably connected to an automatic carrying system (35) capable of autonomous driving. The house structure provided by the invention can take into account the owner's demand for various aspects of the house, and improves the functionality of the house.

The systems and methods described herein disclose detecting obstacles in a vehicular environment using host vehicle input and associated trust levels. As described here, measured vehicles, either manual or autonomous, that detect an obstacle in the environment will operate to respond to the obstacle. As such, those movements can be used to determine if an obstacle exists in the environment, even if the obstacle cannot be detected directly. The systems and methods can include a host vehicle receiving prediction data about an evasive behavior from one or more measured vehicles in a vehicular environment. A trust level can then be established for the measured vehicles. An obscured obstacle can be determined using the evasive behavior and the trust level which can then be mapped in the vehicular environment. A guidance input can then be created for the host vehicle using the obscured obstacle and the trust level.

A moving object that travels from a current position toward a target position includes a first section candidate searching unit configured to search for a plurality of first section candidates in which the moving object is capable of traveling from the current position, a second section candidate searching unit configured to, for each ending point of the first section candidates, search for a plurality of second section candidates in which the moving object is capable of traveling from the ending points of the first section candidates, an obstacle position recognition unit configured to detect a position of an obstacle around the moving object, and a route decision unit configured to decide a first section and a second section used as a traveling route of the moving object from the plurality of first section candidates and the plurality of second section candidates.

Systems and techniques for compensating for the forces exerted on the autonomous lawn mower exerted by operating on a sloped region to be mowed are provided herein. In some examples, such systems and techniques may include receiving a coverage plan of an area to be mowed that includes a sloped region, determining, based on data for the one or more sensors, an orientation of the autonomous lawn mower and determining a slope force to compensate for the slope on which the autonomous lawn mower is operating. The slope force is then converted into signals to generate torques at one or more wheels to compensate for the slope.

A system includes at least partially autonomous vehicles, at least partially separated interconnected roadways, and a management system. Each of the vehicles is configured to cooperate with another vehicle or an area controller. The management system is configured to receive requests to transport, which may have respective start points and respective destinations. Additionally, the management system is configured, responsive to receiving the request, to assign a vehicle to fulfill the request. The assigned vehicle is configured to transport a person from the respective start point, at least in part via the interconnected roadways, to the respective destination.

The present disclosure provides a perception system for a vehicle. The perception system includes a perception filter for determining a region of interest (“ROI”) for the vehicle based on an intent of the vehicle and a current state of the vehicle; and a perception module for perceiving an environment of the vehicle based on the ROI; wherein the vehicle is caused to take appropriate action based on the perceived environment, the current state of the vehicle, and the intent of the vehicle.

A distributed integrated sensing and communication (DISC) module for an autonomous vehicle is described herein. The DISC module includes at least a first sensor system, a second sensor system, a processor, and memory. The first sensor system and the second sensor system are different types of sensor systems. The first sensor system outputs a first sensor signal, and the second sensor system outputs a second sensor signal. The DISC module can detect and track an object (or objects) based on the first sensor signal and the second sensor signal. Moreover, the DISC module can output data indicative of the object as detected. In the DISC module, the first sensor system, at least a portion of the second sensor system, the processor, and the memory are integrated on a single substrate.

Provided is a method of sensing a three-dimensional (3D) space using at least one sensor. The method can include acquiring spatial information over time for the sensed 3D space, applying a neural network based object classification model to the acquired spatial information over time to identify at least one object in the sensed 3D space. The method can also include tracking the sensed 3D space including the identified at least one object, and using information related to the tracked 3D space.

Feedback for map information is based on an integrated navigation solution for a device within a moving platform using obtained motion sensor data from a sensor assembly of the device, obtained radar measurements for the platform and obtained map information for an environment encompassing the platform. An integrated navigation solution is generated based at least in part on the obtained motion sensor data using a nonlinear state estimation technique that uses a nonlinear measurement model for radar measurements. The map information is assessed based at least in part on the integrated navigation solution and radar measurements so that feedback for the map information can be provided.

The technology relates to using on-board sensor data, off-board information and a deep learning model to classify road wemess and/or to perform a regression analysis on road wetness based on a set of input information. Such information includes on-board and/or off-board signals obtained from one or more sources including on-board perception sensors, other on-board modules. external weather measurement, external weather services, etc. The ground truth includes measurements of water film thickness and/or ice coverage on road surfaces. The ground truth, on-board and off-board signals are used to build the model. The constructed model can be deployed in autonomous vehicles for classifying/regressing the road wetness with on-board and/or off-board signals as the input, without referring to the ground truth. The model can be applied in a variety of ways to enhance autonomous vehicle operation, for instance by altering current driving actions, modifying planned routes or trajectories, activating on-board cleaning systems, etc.