An automated storage and retrieval system includes a grid-based rail structure and a plurality of remotely operated vehicles arranged to operate on the grid-based rail structure. The automated storage and retrieval system includes a locking device arranged in a zone of the grid-based rail structure where a human and/or a robotic operator is permitted to interact with the remotely operated vehicle or contents of a storage container that the remotely operated vehicle is carrying. The locking device is arranged to lock the remotely operated vehicle against accidental displacement prior to interaction with the human and/or robotic operator, and wherein the locking device being arranged to unlock the remotely operated vehicle once interaction with the human and/or robotic operator is no longer required.

ADAS includes a processing circuit and a memory which stores instructions executable by the processing circuit. The processing circuit executes the instructions to cause the ADAS to receive, from a vehicle that is in motion, a video sequence, generate a position image including at least one object included in the stereo image, generate a second position information associated with the at least one object based on reflected signals received from the vehicle, determine regions each including at least a portion of the at least one object as candidate bounding boxes based on the stereo image and the position image, and selectively adjusting class scores of respective ones of the candidate bounding boxes associated with the at least one object based on whether a respective first position information of the respective ones of the candidate bounding boxes matches the second position information.

ADAS includes a processing circuit and a memory which stores instructions executable by the processing circuit. The processing circuit executes the instructions to cause the ADAS to receive, from a vehicle that is in motion, a video sequence, generate a position image including at least one object included in the stereo image, generate a second position information associated with the at least one object based on reflected signals received from the vehicle, determine regions each including at least a portion of the at least one object as candidate bounding boxes based on the stereo image and the position image, and selectively adjusting class scores of respective ones of the candidate bounding boxes associated with the at least one object based on whether a respective first position information of the respective ones of the candidate bounding boxes matches the second position information.

A system is provided generating a context-dependent experience for a vehicle driver, e.g., to relax or enhance the driver's mental state. A system controller is configured to receive driving context data regarding a driving situation from various driving context data sources. The driving context data may include vehicle operation data, e.g., generated by vehicle-based sensors, and environmental data regarding an environment external to the vehicle, e.g., received wirelessly from a remote server. The system controller may identify content triggering events based on (a) the received driver context data and (b) a set of content triggering rules. For each content triggering event, the system controller may select one or more human-perceivable contents elements (e.g., audio clips, seat massage settings, or air conditioner settings), and control one or more content output devices (e.g., speakers, seat massage system, or vehicle HVAC system) to output the selected content element(s) to the driver.

An active noise control device includes a first adaptive filter configured to generate a control signal by performing a filtering process on a reference signal corresponding to noise, and a first filter coefficient updating unit configured to update a filter coefficient of the first adaptive filter based on based on the reference signal and an added error signal acquired by adding a first error signal acquired by detecting residual noise by a first microphone and a second error signal acquired by detecting residual noise by a second microphone.

A simulating method for an electric vehicle (EV) includes: receiving a request from a driver of the EV for performing a simulation of a target vehicle on the EV; obtaining a plurality of configuration parameters for simulating behavior of the target vehicle on the EV, at least including information of the target vehicle and settings of the target vehicle for the driver; obtaining a plurality of vehicle parameters of the EV, at least including run-time parameters of the EV and driving conditions of the EV; and performing a simulating process to obtain one or more control parameters to control the EV to realize behaviors, actions, and/or characteristics of the target vehicle on the EV based on the plurality of vehicle parameters and the plurality of configuration parameters using a vehicle simulator model. The vehicle simulator model is a neural network.

This application is directed to on-vehicle behavior modeling of vehicles. A vehicle has one or more processors, memory, a plurality of sensors, and a vehicle control system. The vehicle collects training data via the plurality of sensors, and the training data include data for one or more vehicles during a collection period. The vehicle locally applies machine learning to train a vehicle driving behavior model using the collected training data. The vehicle driving behavior model is configured to predict a behavior of one or more vehicles. The vehicle subsequently collecting sensor data from the plurality of sensors and drives the vehicle by applying the vehicle driving behavior model to predict vehicle behavior based on the collected sensor data. The vehicle driving behavior model is configured to predict behavior of an ego vehicle and/or a distinct vehicle that appears near the ego vehicle.

Various systems and methods are provided for determining a posture of an occupant of a vehicle. In one embodiment, a method comprises capturing images of an occupant in a vehicle via a vehicle camera, determining a current posture of the occupant and a recommended posture for the occupant based on the captured images and body measurements of the occupant, and outputting a guidance based on a difference between the current posture and the recommended posture. In this way, a comfort of the occupant may be increased by guiding the occupant toward a more ergonomic posture.

A control unit of an information processing device is configured to perform selecting a first vehicle that is to download first data in place of a second vehicle while the first vehicle is traveling and instructing the first vehicle to download the first data in place of the second vehicle while the first vehicle is traveling and to transmit the first data to the second vehicle. The control unit is configured to select the first vehicle that is to download the first data out of a plurality of vehicles in place of the second vehicle based on a data rate on a scheduled travel route.

A control unit of an information processing device is configured to perform selecting a first vehicle that is to download first data in place of a second vehicle while the first vehicle is traveling and instructing the first vehicle to download the first data in place of the second vehicle while the first vehicle is traveling and to transmit the first data to the second vehicle. The control unit is configured to select the first vehicle that is to download the first data out of a plurality of vehicles in place of the second vehicle based on a data rate on a scheduled travel route.