Abstract: A method for operating a motor vehicle in the event of an unavoidable collision with a collision object, in particular another vehicle. Environment data relating to the collision object are determined by an environment sensor device including at least one environment sensor and are evaluated to determine at least one driving intervention information for reducing the consequences of a collision. The motor vehicle is automatically guided in accordance with the driving intervention information, and the evaluation of the environment data is carried out together with structural information of the own motor vehicle describing the vehicle structure, in particular including elements absorbing collision energy of the motor vehicle, in such a way that a changed collision point maximizing the deformation energy absorbed by the vehicle structure and to be produced by the driving intervention information is determined when the driving intervention information is determined.

Abstract: A vehicle includes: an instrument panel; and an ECU that controls the instrument panel so that the instrument panel displays an icon representing a status of a battery. The icon represents one of a first indication and a second indication by an increase or decrease of a scale in a display region of the icon, and the other one of the first indication and the second indication by an amount indicated by a size of a display area of the icon, the first indication representing SOC of the battery, the second indication representing a full charge capacity of the battery.

Abstract: A learning apparatus includes an input unit configured to input route information indicating a route including one or more paths of a plurality of paths and moving body number information indicating the number of moving bodies for a date and a time on a path to be observed among the plurality of paths, and a learning unit configured to learn a parameter of a model indicating a relationship between the number of moving bodies for each of the plurality of paths and the number of moving bodies for the route and a relationship between the numbers of moving bodies for the route at different dates and times by using the route information and the moving body number information.

Abstract: The invention relates to a method for operating a vehicle assistance or control system with a vehicle and with an offboard obstacle recognition sensor for recognizing obstacles for the vehicle, wherein the vehicle includes an onboard obstacle recognition sensor for recognizing obstacles to the vehicle, wherein a first grid map is generated relative to the offboard obstacle recognition sensor, a second grid map relative to the onboard obstacle recognition sensor, or respectively relative to the vehicle, and wherein a consolidated grid map is generated depending on the first grid map and the second grid map.

Abstract: A method includes identifying an object that is invading a lane that an autonomous vehicle is occupying, and generating a constraint about a point of crossing, where the constraint has a direction and a length, and the point of crossing represents a location of where the object and the autonomous vehicle will collide if the object maintains its current trajectory and the autonomous vehicle maintains its current trajectory. The method includes applying the constraint to a motion plan associated with the autonomous vehicle, and issuing one or more commands to adjust movement of the autonomous vehicle in response to encountering the constraint.

Abstract: Understanding the intent of human drivers and adapting to their driving styles is used to increased efficiency and safety of autonomous vehicles (AVs) by enabling them to behave in safe and predictable ways without requiring explicit inter-vehicle communication. A Social Value Orientation (SVO), which quantifies the degree of an agent's selfishness or altruism, is estimated by the AV for other vehicles to better predict how they will interact and cooperate with others. Interactions between agents are modeled as a best response game wherein each agent negotiates to maximize their own utility. A dynamic game solution uses the Nash equilibrium, yielding an online method of predicting multi-agent interactions given their SVOs. This approach allows autonomous vehicles to observe human drivers, estimate their SVOs, and generate an autonomous control policy in real time.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating candidate future trajectories for agents. One of the methods includes obtaining scene data characterizing a scene in an environment at a current time point; for each of a plurality of lane segments, processing a model input comprising (i) features of the lane segment and (ii) features of the target agent using a machine learning model that is configured to process the model input to generate a respective score for the lane segment that represents a likelihood that the lane segment will be a first lane segment traversed by the target agent after the current time point; selecting, as a set of seed lane segments, a proper subset of the plurality of lane segments based on the respective scores; and generating a plurality of candidate future trajectories for the target agent.

Abstract: A method of using perception-inspired event generation for situation awareness for a vehicle, including receiving perception input data from a sensor of the vehicle and processing the perception input data to classify and generate parameters related to an external entity in a vicinity of the vehicle. The method includes generating a hierarchical event structure that classifies and prioritizes the perception input data by classifying the external entity into an attention zone and prioritizing the external entity within the attention zone according to a risk level value for the external entity. A higher risk level value indicates a higher priority within the attention zone. The method further includes developing a behavior plan for the vehicle based on the hierarchical event structure.

Abstract: A vehicle dispatch support system includes a recording section at which comparison vehicle sensitivity information and candidate vehicle sensitivity information are recorded, the comparison vehicle sensitivity information being information relating to a driving sensitivity of a comparison vehicle that is a vehicle that a user has ridden in the past, and the candidate vehicle sensitivity information being information relating to a driving sensitivity of each of a plurality of candidate vehicles, a processor coupled to the recording section and configured to compute a sensitivity difference, which is a difference between the driving sensitivity expressed by the comparison vehicle sensitivity information and the driving sensitivity expressed by the candidate vehicle sensitivity information, and a display section configured to display sensitivity difference-related information, which is information based on the sensitivity difference.

Abstract: Methods for operating a vehicle in an environment include receiving light detection and ranging (LiDAR) data from a LiDAR of the vehicle. The LiDAR data represents objects located in the environment. A dynamic occupancy grid (DOG) is generated based on a semantic map. The DOG includes multiple grid cells. Each grid cell represents a portion of the environment. For each grid cell, a probability density function is generated based on the LiDAR data. The probability density function represents a probability that the portion of the environment represented by the grid cell is occupied by an object. A time-to-collision (TTC) of the vehicle and the object less than a threshold time is determined based on the probability density function. Responsive to determining that the TTC is less than the threshold time, a control circuit of the vehicle operates the vehicle to avoid a collision of the vehicle and the object.

Abstract: An automated driving trajectory generating device is configured to: recognize a mobile object which is located near a vehicle; calculate a host vehicle path for automated driving of the vehicle and a plurality of predicted paths of the mobile object based on a position of the vehicle on a map, a position of the mobile object on the map, and map information; calculate a predicted acceleration which is generated in the mobile object moving along a predicted path for each predicted path based on the plurality of predicted paths and a vehicle speed of the mobile object; identify a target path which is a predicted path used to generate the trajectory out of the plurality of predicted paths based on a result of comparison between the predicted acceleration and an acceleration threshold value; and generate the trajectory based on the host vehicle path and the target path.

Abstract: An information processing device for managing a vehicle including a toilet unit, a tank unit connected to the toilet unit for storing waste that flows from the toilet unit, and a traveling unit capable of moving the toilet unit and the tank unit. The information processing device performs control for disconnecting the traveling unit from the toilet unit and the tank unit when installing a toilet. The information processing device includes a control unit configured to acquire a storage amount of waste in the tank unit and to generate commands to the traveling unit to cause the traveling unit to replace the tank unit when the storage amount of waste becomes equal to or larger than a predetermined amount.

Abstract: According to one aspect, an overall airbag system of an autonomous vehicle uses a combination of sensors to deploy one or more external airbags in advance of a collision or as a collision occurs, for the purpose of protecting vulnerable persons. By using sensor data, combined with perception and prediction algorithms, an autonomous driving system may deploy a substantially optimal combination of external airbags for a given the size of a vulnerable person, vehicle speed, and/or collision timing. In addition to cushioning impact during a collision, the deployment of multiple external airbags may also control kinematics of vulnerable persons, as for example by addressing brain injuries in pedestrians due to rotational kinematics.

Abstract: The present disclosure provides a method, an apparatus, a computer device and a computer-readable storage medium for positioning, and relates to the field of autonomous driving. The method obtains inertial measurement data of a device to be positioned at a current time and point cloud data collected by a LiDAR on the device at the current time; determines, by integrating the inertial measurement data, inertial positioning information of the device in an inertial coordinate system at the current time; and determines, based on the inertial positioning information, the point cloud data and at least one local map built in a local coordinate system, a positioning result of the device in the local coordinate system at the current time. Techniques of the present disclosure can provide an effective and stable local positioning result.

Abstract: A method of providing an indication identifying a flight as including or not including a flight path deviation, the method includes receiving flight data associated with a flight, the flight data comprising a plurality of coordinates indicating a location of an aircraft at a plurality of times during the flight. The method also includes classifying a plurality of time intervals of the flight by applying a convolutional neural network algorithm to subsets of the plurality coordinates to determine that the time interval includes or does not include at least one flight path deviation. The method also includes identifying the flight as including at least one flight path deviation if at least one of the time intervals is classified as including the at least one flight path deviation and providing an indication identifying the flight as including at least one flight path deviation to a second computing system in response to identifying the flight as including at least one flight path deviation.

Abstract: In accordance with an aspect of a driver assistance system includes a radar sensor installed in a vehicle, having a side sensing field of view of the vehicle, and configured to acquire side sensing data; and a controller including a processor configured to process the side sensing data; and the controller may be configured to estimate motion state of a moving object at the side of the vehicle based on applying the side sensing data to at least one of a first estimation model and a second estimation model.

Abstract: A recording system for a vehicle according to an example of the present disclosure includes proximity sensors configured to obtain data indicative of a distance between a vehicle and external objects and at least one vehicle operation sensor configured to obtain data indicative of how a driver is operating the vehicle. A controller is configured to obtain input data for a current monitoring period that includes data from the plurality of proximity sensors and the at least one vehicle operation sensor; utilize a predictive engine to determine whether the input data fulfills a criteria set corresponding to a potential impending vehicle incident; based on the input data fulfilling the criteria set commence saving of the input data from the current monitoring period and subsequent monitoring periods in a data set; and based on the input data not fulfilling the criteria set delete the input data for the current monitoring period.

Abstract: A method and apparatus for determining driving scene, and a vehicle are provided. The method includes: acquiring current driving data collected by a preset collection component; comparing the current driving data with a preset driving data threshold; and determining a current driving scene according to a comparison result between the current driving data and the threshold.

Abstract: According to an aspect of an embodiment, operations may comprise capturing, at a vehicle as the vehicle travels, LIDAR scans and camera images. The operations may further comprise selecting, at the vehicle as the vehicle travels, a subset of the LIDAR scans and the camera images that are determined to be useful for calibration. The operations may further comprise computing, at the vehicle as the vehicle travels, LIDAR-to-camera transformations for the subset of the LIDAR scans and the camera images using an optimization algorithm. The operations may further comprise calibrating, at the vehicle as the vehicle travels, one or more sensors of the vehicle based on the LIDAR-to-camera transformations.

Abstract: A driver assistance device is configured to determine whether a type of a deceleration target is included in a category of a position-fixed object or a moving object, and determine whether the deceleration target is lost, and to continue the deceleration assistance on assumption that the lost deceleration target exists when the deceleration target is determined to be lost. The driver assistance device is configured to notify a driver of a host vehicle that the deceleration target is lost when the deceleration target is determined to be lost and the type of the deceleration target is included in the category of the moving object, and not to notify the driver that the deceleration target is lost when the deceleration target is determined to be lost and the type of the deceleration target is included in the category of the position-fixed object.