The disclosed subject matter includes a method and system for navigating an unmanned ground vehicle (UGV), that include: generating, based on the scanning output data, a first map comprising a first group of cells and characterized by a first size; generating, based on the scanning output data, a second map representing an area smaller than that of the first map comprising a second group of cells, which are characterized by a second size being smaller than the first size; wherein each cell in the first group of cells and the second group of cells is classified to a class selected from at least two classes, comprising traversable and non-traversable, wherein the second part at least partly overlaps the first part; navigating the UGV based on data deduced from crossing between cells in the first map and second map.

Provided is a management device for managing an operation of a package delivery vehicle configured to travel autonomously on a road without accommodating a driver, and capable of storing a package into each of a plurality of storages each covered by an openable and closable door, the management device including: a storage device that stores a program; and a hardware processor, in which the hardware processor is configured to execute the program stored in the storage device to: receive application information on delivery of the package from a user; and determine an operation of the package delivery vehicle, in which the hardware processor is configured to cause the package delivery vehicle during delivery of a package to collect a new package.

Disclosed are apparatuses and methods for controlling the steering of an autonomous vehicle. The apparatus including an electric power steering device to generate an assist torque for a steering wheel, an autonomous driving position controller to control a steering position according to a command steering angle input from an autonomous driving module, a driver steering intervention judger to judge whether a driver intervenes in steering based on a column torque and a vehicle speed, a weight detector to detect a weight for integrating output of an electric power steering device and output of an autonomous driving position controller based on judging whether the driver intervenes in steering, and an output controller to apply the weight to the output of the electric power steering device and the output of the autonomous driving position controller to integrate the output of the electric power steering device and the output of the autonomous driving position controller, wherein the autonomous driving position controller is further configured to adjust a gain value for controlling the steering position by applying the weight.

A vehicle remote instruction system 100 transmits a remote instruction request from an autonomous driving vehicle 2 to a remote instruction apparatus 1, and controls travel of the autonomous driving vehicle 2 based on a remote instruction transmitted from the remote instruction apparatus 1 in response to the remote instruction request. The vehicle remote instruction system 100 includes a delay determination unit 39 configured to determine whether or not a communication delay occurs between the remote instruction apparatus 1 and the autonomous driving vehicle 2, and a rejection unit 40 configured to reject the remote instruction transmitted in response to the remote instruction request if it is determined by the delay determination unit 39 that the communication delay occurs.

A plurality of fault conditions are detected on a communication network onboard a vehicle. The detected fault conditions, a fault condition importance, environment conditions, and a vehicle operation mode are input to a neural network that outputs rankings for respective detected fault conditions. The neural network is trained by determining a loss function based on a maximum likelihood principle that determines a probability distribution that ranks the detected fault conditions. The vehicle is operated based on the rankings of the fault conditions.

A method includes detecting, via a processor of an autonomous vehicle, an upcoming downhill road segment of a route on which the autonomous vehicle is currently travelling. The detection is based on map data, camera data, and/or inertial measurement unit (IMU) data. In response to detecting the upcoming downhill road segment, a descent plan is generated for the autonomous vehicle. The descent plan includes a speed profile and a brake usage plan. The brake usage plan specifies a non-zero amount of retarder usage and an amount of foundation brake usage for a predefined time period. The method also includes autonomously controlling the autonomous vehicle, based on the descent plan, while the autonomous vehicle descends the downhill road segment.

Systems and methods for perceiving a field around a mobile device include a sensor system has a distance sensor arranged on a mobile device. The sensor system captures distance measurements of a field of view. The distance measurements are captured at a unique set of angular scan positions per revolution of the distance sensor over a sequence of scan rotations. A perception system generates a three-dimensional point cloud representation of the field of view based on the distance measurements for each scan rotation in the sequence of scan rotations. The perception system generates a composite three-dimensional depth map of the field of view by compiling each of the three-dimensional point cloud representations for the sequence of scan rotations. Each of the three-dimensional point cloud representations has a resolution that is lower than a resolution of the composite three-dimensional depth map of the field of view.

A method of parking a vehicle includes creating a map of a parking environment utilizing data from at least one sensor coupled to a vehicle. The method further includes storing the map in a data storage device. The method also includes receiving a first learn signal indicating that the vehicle is located in a first parking position. The method further includes determining a first set of spatial data indicative of the location of the vehicle relative to the map in response to receiving the first learn signal. The method also includes storing the first set of spatial data in the data storage device.

Vehicle advanced driver assistance (ADAS) and autonomous driving feature simulation and verification systems and method receive a driving log file and map data corresponding to an on-road driving scenario by a vehicle and overlay the driving log file and the map data to generate combined data that localizes a position of the vehicle. A whitelist comprising a set of included objects for an ADAS/autonomous driving feature simulation and (ii) a blacklist comprising a set of excluded objects for the ADAS/autonomous driving feature simulation and a template for the ADAS/autonomous driving feature simulation are then obtained. Finally, an ADAS/autonomous driving feature simulation scenario is generated in a desired format using the white and blacklists and the template and executed using a corresponding simulation tool and a result of the executed simulation of the ADAS/autonomous driving feature simulation scenario is verified.

A method includes receiving first driving data associated with a first vehicle, receiving second driving data associated with one or more vehicles around the first vehicle, creating training data by labeling the first driving data as positive data and treating the second driving data as unlabeled, and using the training data to train a classifier to predict whether driving data input to the classifier is positive or unlabeled.