A vehicle includes a sensor device configured to collect data on regions around the vehicle, and an automatic parking controller configured to generate a folding signal or an unfolding signal for a side mirror based on the data collected by the sensor device and a state of the side mirror when performing automatic parking.

Techniques for determining a classification probability of an object in an environment are discussed herein. Techniques may include analyzing sensor data associated with an environment from a perspective, such as a top-down perspective, using multi-channel data. From this perspective, techniques may determine channels of multi-channel input data and additional feature data. Channels corresponding to spatial features may be included in the multi-channel input data and data corresponding to non-spatial features may be included in the additional feature data. The multi-channel input data may be input to a first portion of a machine-learned (ML) model, and the additional feature data may be concatenated with intermediate output data from the first portion of the ML model, and input into a second portion of the ML model for subsequent processing and to determine the classification probabilities. Additionally, techniques may be performed on a multi-resolution voxel space representing the environment.

A driver management system, method, and computer readable medium capable of operating the same. The driver management system includes: a monitoring part configured to determine a level of driving attention of a driver of a vehicle; and a driving guide part configured to receive the level of driving attention from the monitoring part, determine a level of necessary attention required according to an autonomous driving level of a vehicle, and provide the driver with information on the level of necessary attention such that the level of driving attention satisfies the level of necessary attention.

In the present invention, when a road surface condition is determined based on information acquired by a camera installed in a vehicle, a route of a host vehicle is predicted and the is determined. A road surface condition determination method and device determines a road surface condition of a predicted route based on information acquired by a camera installed in a host vehicle. A controller predicts a route of the host vehicle by determining a road surface friction coefficient of the predicted route based on information acquired by the camera. The determining of the road surface friction coefficient of the predicted route includes: dividing an ahead-of-vehicle image acquired by the camera in a left-right direction and determining a road surface condition for each of the determination areas, and determining the road surface friction coefficient in the determination areas through which the predicted route will pass.

A method autonomously controls a mobility of an automotive apparatus, which mobility is such as to have an influence on the path of the apparatus. The method includes steps of: acquiring parameters relative to the path of the apparatus, and of computing a new control setpoint for the mobility of the apparatus depending on said parameters, this new control setpoint being determined by means of a controller that respects a model that limits the variation in the control setpoint.

A method for assisting a vision of a driver includes: receiving a signal indicating that weather information is emergency weather information from a navigation device according to setting by a vehicle driver, and turning on a vision assisting device included in the vehicle in response to the signal; obtaining an image of an infrared thermal camera of the vision assisting device, which photographs a front of the vehicle when the vehicle travels, and obtaining an image of a camera of the vision assisting device, which photographs the front of the vehicle when the vehicle travels; controlling an image processor of the vision assisting device to determine whether a matching rate between image data of the infrared thermal camera and image data of the camera is equal to or less than a first threshold; and, when the matching rate is equal to or less than the first threshold, using distances between the vehicle and respective objects located at the front, a rear, and sides of the traveling vehicle, speeds of the respective objects, which are detected by a radar sensor of the vision assisting device, and the images of the infrared thermal camera photographing the front of the vehicle to generate a surrounding state image of the vehicle, which includes the distances and the speeds.

A vehicle computing device may implement techniques to predict behavior of objects or predicted objects in an environment. The techniques may include using a model to determine whether a potential object will emerge from an occluded region in the environment. The model may be configured to use one or more algorithms, classifiers, and/or computational resources to predict an intersection point and/or an intersection time between the potential object and the vehicle. Based on the predicted intersection point and/or the predicted intersection time, the vehicle computing device may control operation of the vehicle.

An information processing apparatus comprising, at least one first processor configured to carry out a first process on data input from at least one sensor to produce first processed data, a selector configured to select, according to a first predetermined condition, at least one of a plurality of second processes, and at least one second processor configured to receive the first processed data from the at least one first processor and to carry out the selected at least one of the plurality of second processes on the first processed data to produce second processed data, each of the plurality of second processes having a lower processing load than the first process.

A method of predictive navigation control for an ego vehicle includes: comparing a cue node to each of a plurality of episodic memory nodes in an episodic memory structure, wherein the cue node represents a new event representing distances, speeds and headings of one or more newly observed objects about the ego vehicle, and wherein the episodic memory structure includes a network of nodes each representing a respective previously existing event and having a respective node risk and likelihood; determining which of the nodes has a smallest respective difference metric, thus defining a best matching node; consolidating the cue node with the best matching node if the smallest difference metric is less than a match tolerance, else adding a new node corresponding to the cue node to the episodic memory structure; and identifying a likeliest next node and/or a riskiest next node.

This document describes Kurtosis based pruning for sensor-fusion systems. Kurtosis based pruning minimizes a total quantity of comparisons performed when fusing together large sets of data. Multiple candidate radar tracks may possibly align with one of multiple candidate visual tracks. For each candidate vision track, a weight or other evidence of matching is assigned to each candidate radar track. An inverse of matching errors between each candidate vision and each candidate radar track contributes to this evidence, which may be normalized to produce, for each candidate vision track, a distribution associated with all candidate radar tracks. A Kurtosis or shape of this distribution is calculated. Based on the Kurtosis values, some candidate radar tracks are selected for matching and other remaining candidate radar tracks are pruned. The Kurtosis aids in determining how many candidates to retain and how many to prune. In this way, Kurtosis based pruning can prevent combinatorial explosions due to large-scale matching.