Abstract: In an aspect, a vehicle apparatus of a vehicle obtains, based on sensor data from one or more vehicle sensors communicatively coupled to the vehicle, intra-lane position data relative to the vehicle, the intra-lane position data indicating at least one lateral distance between at least one side of a primary vehicle and at least one lane reference point. The vehicle apparatus transmits the intra-lane position data to one or more neighboring entities. In another aspect, a vehicle management device obtains intra-lane position data that indicates at least one lateral distance between at least one side of at least one observed vehicle of a plurality of neighboring vehicles and at least one lane reference point, and instructs at least one vehicle of the plurality of neighboring vehicles to perform one or more actions based on the intra-lane position data.

Abstract: A system and method for an on-demand shuttle, bus, or taxi service able to operate on private and public roads provides situational awareness and confidence displays. The shuttle may include ISO 26262 Level 4 or Level 5 functionality and can vary the route dynamically on-demand, and/or follow a predefined route or virtual rail. The shuttle is able to stop at any predetermined station along the route. The system allows passengers to request rides and interact with the system via a variety of interfaces, including without limitation a mobile device, desktop computer, or kiosks. Each shuttle preferably includes an in-vehicle controller, which preferably is an AI Supercomputer designed and optimized for autonomous vehicle functionality, with computer vision, deep learning, and real time ray tracing accelerators. An AI Dispatcher performs AI simulations to optimize system performance according to operator-specified system parameters.

Abstract: An autonomous moving body includes: a determination unit configured to determine that the autonomous moving body has arrived at a waiting area on a current floor before the autonomous moving body gets on a car of the elevator; an orientation adjustment unit configured to adjust, when the determination unit determines that the autonomous moving body has arrived at the waiting area, an orientation of the autonomous moving body based on an exiting direction from the car on a destination floor; and a movement controller configured to cause, when the car arrives, the autonomous moving body to enter the car while maintaining the orientation adjusted by the orientation adjustment unit.

Abstract: An artificial intelligence system for an autonomous vehicle includes one or more processors and a memory in communication with the one or more processors and storing a target-orientated navigation system module. When executed, the target-orientated navigation system module causes the one or more processors to receive sensor data from one or more sensors of a vehicle, synchronize the sensor data, preprocess the synchronized sensor data by transforming the sensor data into a common data format, concatenate the transformed sensor data into a K-dimensional array, which acts as an input state array, apply a navigation policy to the input state array to estimate an action-value array, direct a vehicle control system to guide the vehicle to a location representative of a cell in the action-value array that has a highest reward value.

Abstract: In some exemplary processes for accessing a vehicle, a transit request is initiated. The transit request summons a vehicle towards a location of a user device. Information generated in response to the transit request is received. The information includes first authentication information and second authentication information. Third authentication information from the vehicle is received using a first wireless communication protocol. A determination is made as to whether the third authentication information corresponds to the first authentication information. In accordance with determining that the third authentication information corresponds to the first authentication information, a wireless communication connection is established with the vehicle using a second wireless communication protocol and the second authentication information. The second wireless communication protocol is different from the first wireless communication protocol.

Abstract: In an example, a method determines a reference vehicle traveling in a first lane. The first lane merges with a second lane at a merging point. The method can determine a merging plan for a merging vehicle traveling in the second lane to merge into the first lane based on a vehicle position, and in some cases the speed, of the reference vehicle in the first lane. The method can overlay, via a display device, a virtual target in a field of view of a roadway environment of a driver of the merging vehicle based on the merging plan of the merging vehicle.

Abstract: Techniques described herein are directed to classifying lanes in an environment of a vehicle for, for example, performing lane handling. In an example, system(s) of a vehicle can determine a signal indicative of a presence of the vehicle in a lane of a drivable surface in an environment within which the vehicle is located. The system(s) can determine, based at least in part on the signal, a classification of the lane as at least one of occupied (an object is at least partially in the lane), unoccupied (no object in the lane), and/or established (e.g., where an object has established a priority in the lane). The system(s) can control the vehicle based at least in part on the classification of the lane to improve safety in scenarios, for example, including merging.

Abstract: In one embodiment, a system uses an actor-critic reinforcement learning model to generate a trajectory for an autonomous driving vehicle (ADV) in an open space. The system perceives an environment surrounding an ADV. The system applies a RL algorithm to an initial state of a planning trajectory based on the perceived environment to determine a plurality of controls for the ADV to advance to a plurality of trajectory states based on map and vehicle control information for the ADV. The system determines a reward prediction by the RL algorithm for each of the plurality of controls in view of a target destination state. The system generates a first trajectory from the trajectory states by maximizing the reward predictions to control the ADV autonomously according to the first trajectory.

Abstract: A robot includes a body that is movable relative to a surface one or more measurement devices within the body to output information based on an orientation of the body at an initial location on the surface, and a controller within the body to determine an orientation of the body based on the information and to restrict movement of the body to an area by preventing movement of the body beyond a barrier that is based on the orientation of the body and the initial location.

Abstract: In one embodiment, a computing system of a vehicle may receive perception data associated with a scenario encountered by a vehicle while operating in an autonomous driving mode. The system may identify the scenario based at least on the perception data. The system may generate a performance metric associated with a vehicle navigation plan to navigate the vehicle in accordance with the identified scenario. In response to a determination that the performance metric associated with the vehicle navigation plan fails to satisfy one or more criteria for navigating the vehicle in accordance with the identified scenario, the system may trigger a disengagement operation related to disengaging the vehicle from the autonomous driving mode. The system may generate a disengagement record associated with the triggered disengagement operation. The disengagement record may include information associated with the identified scenario encountered by the vehicle related to the disengagement operation.

Abstract: An autonomous cleaning robot includes a controller configured to execute instructions to perform one or more operations. The one or more operations includes operating a drive system to move the cleaning robot in a forward drive direction along a first obstacle surface with a side surface of the cleaning robot facing the first obstacle surface, then operating the drive system to turn the cleaning robot such that the side surface of the cleaning robot faces a second obstacle surface, then operating the drive system to move the cleaning robot in a rearward drive direction along the second obstacle surface, and then operating the drive system to move the cleaning robot in the forward drive direction along the second obstacle surface.

Abstract: This vehicle control system comprises: a driving control section 1 that performs switching between autonomous driving and manual driving of a vehicle V; a seat 10 that can be changed to forms which are different during autonomous driving and during manual driving; and a seat control unit 2 that controls the operation of the seat 10 when changing the forms. If the seat 10 is in the form adopted during autonomous driving, the driving control unit 1 can perform control so that the switching from autonomous driving to manual driving of the vehicle V is not performed. Thus, it is possible to improve safety when switching from autonomous driving to manual driving.

Abstract: A drive mode switch control device acquires operation information. The drive mode switch control device switches a drive state among at least an autonomous drive state, a manual drive state, and a coordination drive state. The operation detection unit detects a first operation and a second operation based on the operation information when the drive state is not in the manual drive state. The second operation is the drive operation different from the first operation and input after the input of the first operation. The drive mode switch control device switches the drive state from the autonomous drive state to the coordination drive state based on a detection determination of the first operation. The drive mode switch control device switches the drive state from the coordination drive state to the manual drive state based on a detection determination of the first operation.

Abstract: Aspects of the disclosure provide for controlling an autonomous vehicle having an autonomous driving mode. For instance, sensor data generated by a perception system of a vehicle may be received. The sensor data identifying objects in an environment of the vehicle. A trajectory corresponding to a future path of the vehicle may be received. That the vehicle is approaching an unmarked crosswalk situation may be determined based on the trajectory and the sensor data. In response to the determination, an area for an unmarked crosswalk is identified. The vehicle may be controlled in the autonomous driving mode based on the determination and the area.

Abstract: In one embodiment, a system for removing LiDAR points is provided. An image is received from a camera and a plurality of points is received from a LiDAR sensor. The points are placed on the image based on coordinates associated with each point. The image is divided into a plurality of cells by placing a grid over the image. For each cell, a threshold is calculated based on the minimum distance between the points in the cell and the camera. The threshold may control how many points are allowed to remain in each cell. After calculating the thresholds, points are removed from each cell until the number of points in each cell do not exceed the threshold for the cell. The image and/or the reduced plurality of points are then used to provide one or more vehicle functions.

Abstract: A road surface detection device, which detects a road surface of a road on which a host vehicle runs, includes: a sensor configured to detect three-dimensional positional information at individual detection points by scanning a layer that includes the road surface; and an external environment recognition unit configured to calculate two-dimensional positional information in which positional information in a height direction has been removed from the positional information at the individual detection points included in the layer, to calculate, based on the two-dimensional positional information, a point sequence of the individual detection points in a two-dimensional coordinate plane, and to detect a trench existing in the road surface in accordance with a state of change of the point sequence.

Abstract: Systems and methods for controlling an autonomous vehicle to reduce idle data usage and vehicle downtime are provided. In one example embodiment, a computing system can obtain data associated with autonomous vehicle(s) that are online with a service entity. The computing system can obtain data indicative of the geographic area with an imbalance in a number of vehicles associated with the geographic area. The computing system can determine a first autonomous vehicle for re-positioning with respect to the geographic area based at least in part on the data associated with the one or more autonomous vehicles and the data indicative of the geographic. The computing system can communicating data indicative of a first re-positioning assignment associated with the first autonomous vehicle. In some implementations, the computing system can generate vehicle service incentive to entice a vehicle provider to re-position its autonomous vehicles with respect to the geographic area.

Abstract: The present disclosure relates to a cleaning robot and a method of controlling the same, and more particularly, to a cleaning robot that changes a moving path by detecting an obstacle through a change in permittivity detected while driving about a cleaning space, and a method of controlling the same. The cleaning robot comprises a main body; a driver configured to move the main body; an obstacle detector including an electrode plate provided on the bottom of the main body and a touch IC configured to detect a change in capacitance detected by the electrode plate and; and a controller configured to determine the obstacle based on a signal transmitted by the obstacle detector, and to control the driver.

Abstract: The present invention is provided with: a sensing unit which is provided in a vehicle and measures the three-dimensional shape of an object; a matching unit for performing matching between multiple sets of structure information, in which are associated a data group indicating the three-dimensional shapes of structures provided beforehand near a path the vehicle will travel along and positional information indicating the posit ions of the structures, and a data group indicating the three-dimensional shape of the object measured by the sensing unit, and for specifying the position of the object; and an estimated position determination unit for determining, with the position specified by the matching unit as a reference position, the estimated position of the vehicle on the basis of the reference position.

Abstract: According to one embodiment, a moving body operation support system includes an acquirer, a microphone, a transceiver, and a processor. The acquirer acquires moving body information relating to a state of a moving body. The microphone is provided in the moving body. The transceiver performs transmitting to and receiving from an operator communication device. The processor implements one of a first operation or a second operation based on the moving body information and instruction information. The instruction information relates to an instruction based on a sound acquired by the microphone. The instruction is of a user riding in the moving body. In the first operation, the processor causes the moving body to perform an operation corresponding to the instruction information. In the second operation, the processor enables communication between the user and the operator by the transmitting and receiving between the transceiver and the operator communication device.

Abstract: Techniques are discussed for determining a data level for portions of data for processing. In some cases, a data level can correspond to a resolution level, a compression level, a bit rate, and the like. In the context of image data, the techniques can determine a region of first image data to be processed a high resolution and a region of second image data to be processed at a low resolution. The regions can be determined by a machine learned algorithm that is trained to output identifications of such regions. Training data may be determined by identifying differences in outputs based on the first and second image data. The image data associated with the determined regions and the determined resolutions can be processed to perform object detection, classification, segmentation, bounding box generation, and the like, thereby conserving processing, bandwidth, and/or memory resources in real time systems.

Abstract: This application relates to the field of self-driving car technologies. In a vehicle control method, first control behavior information is obtained, by processing circuitry of a vehicle, via a vehicle-mounted sensor system in a case that the vehicle is in an autonomous control mode. The first control behavior information is generated from a first user action performed on the vehicle. Whether the first control behavior information corresponds to a predetermined type of control behavior information is determined by the processing circuitry. The predetermined type of control behavior information corresponds to a user action type that is triggered by a reflex of a user. A switch, by the processing circuitry, is performed from the autonomous control mode to a manual control mode in a case that the first control behavior information is not determined as the predetermined type of control behavior information.

Abstract: A method for assisting entry or exit of a motor vehicle into and from a parking space. The motor vehicle equipped with a steering wheel and at least one steering servomotor capable of applying a steering angle steered wheels of the motor vehicle. A control unit actuates the steering servomotor for carrying out an automatic parking process, in which case, during the parking process there is no mechanical connection between the steering wheel and the steered wheels and the steering wheel does not move.

Abstract: The present disclosure relates to a controlling system console and a controlling system. In the console, a central control component is connected to an electric hoist control device via network, enhancing transmission speed and stability of signals. The console can send a combination action control signal through a combination action control component, such that the central control component creates a combination action control instruction and sends the combination action control instruction to the electric hoist control cabinet to make the electric hoist perform combination actions. The console can further send a collaboration action control signal through a collaboration action control component, such that the central control component creates a collaboration action control instruction and sends the collaboration action control instruction to the electric hoist control cabinet to make the electric hoist to act in collaboration with other arena devices.

Abstract: Methods and systems for autonomous and semi-autonomous vehicle routing are disclosed. Roadway suitability for autonomous operation is scored to facilitate use in route determination. Maps of roadways suitable for various levels of autonomous operation may be generated. Such map data may be used by autonomous vehicles or other computer devices in determining routes based upon criteria for vehicle trips. Such routes may be automatically updated based upon changes in road conditions, vehicle conditions, operator conditions, or environmental conditions. Emergency routing using such map data is described, such as automatic routing and travel when a passenger is experiencing a medical emergency.

Abstract: Various examples are directed to systems and methods for controlling an autonomous vehicle. For example, a navigator system at an autonomous vehicle may generate a plurality of local routes beginning at a vehicle location and extending to a plurality of local route end points. The navigator system may access general route cost data, the general route cost data describing general route costs from the plurality of local route end points to a trip end point. The navigator system may select the first local route of the plurality of routes based at least in part on the general route cost data. A vehicle autonomy system at the autonomous vehicle may begin to control the autonomous vehicle along the first local route.

Abstract: A method performs an at least partially automated maneuver. When an increased probability of collision of the vehicle with a vehicle in front is recognized, at least the decision to divert and/or the decision to return, i.e. the decision to return to the lane in which the vehicle was travelling before the diversion, is controlled as a function of a parameter of the traffic situation in front of the vehicle in front.

Abstract: A method for assisting the driver of a vehicle that comprises an analysis means which analyzes the environment in an area located in front of the vehicle in order to detect boundaries and traffic signs and to provide environmental data representative of said boundaries and traffic signs, and a control means which controls the automated driving of said vehicle. Said method comprises a step in which the environmental data are analyzed to determine a variation in speed limit correlated temporally with a variation in the number of traffic lanes, and when such a determination is made, it is considered that the vehicle is located close to a restricted area and an alert is generated requesting that the driver take back control of the driving while crossing said restricted area or that a specific automated driving strategy be implemented by the control means for said crossing.

Abstract: A driving assistance systems, methods, and programs are capable of communicating with a route guidance system that searches for a planned travel route of a vehicle and provides guidance on the planned travel route. The systems, methods, and programs accept, as input, the planned travel route from the route guidance system as a route for driving assistance and provide driving assistance of the vehicle on the route for driving assistance. The systems, methods, and programs accept, as input, when the planned travel route whose guidance is provided by the route guidance system no longer matches the route for driving assistance, the planned travel route from the route guidance system.

Abstract: A vehicle can include a base assembly, a rack assembly, and a processing system. The base assembly can include a platform, a plurality of wheels and a motor for driving at least one of the plurality of wheels. The rack assembly can include a storage shelf connected to the base assembly through a coupling, which includes a bearing assembly enabling the storage shelf to rotate with respect to the platform. The processing system can be configured to communicatively couple with the rack assembly and include one or more processors configured to: determine a location of a target good; cause the storage shelf to move based on the determined location.

Abstract: An automated vehicle assistance system is provided for supervised or unsupervised vehicle movement. The system includes a control system and a first sensor system. The first sensor system may receive first image data of a scene and may output a first disparity map and a first confidence map based on the first image data. The control system may output a video stream based on the first disparity map and the first confidence map. The vehicle assistance system also may include a second sensor system that receives second image data of at least a portion of the scene that outputs a second confidence map based on second image data. The video stream may include super-frames, with each super-frame including a 2D image of the scene, a depth map corresponding to the 2D image, and a certainty map corresponding to the depth map.

Abstract: A vehicle control system includes a steering input member rotatably supported by a vehicle body, a first capacitive sensor provided on a first surface portion of the steering input member facing in one direction along a rotational center line of the steering input member, a second capacitive sensor provided on a second surface portion of the steering input member opposite to the first surface portion, and a control unit configured to control a drive unit and/or a brake unit of the vehicle according to signals from the first and second capacitive sensors. The control unit executes an acceleration control to control the drive unit to accelerate the vehicle when a prescribed signal is received from the first capacitive sensor, and to execute a deceleration control to control the drive unit and/or the brake unit to decelerate the vehicle when a prescribed signal is received from the second capacitive sensor.

Abstract: Provided herein is a system and method for determining whether a sensor is calibrated and error handling of an uncalibrated sensor. The system comprises a sensor system comprising a sensor and an analysis engine configured to determine whether the sensor is uncalibrated. The system further comprises an error handling system configured to perform an error handling in response to the sensor system determining that the sensor is uncalibrated. The method comprises determining, by a sensor system, whether the sensor is uncalibrated, and performing, by an error handling system, an error handling in response to the sensor system determining that the sensor is uncalibrated.

Abstract: Systems and methods for implementing one or more autonomous features for autonomous and semi-autonomous control of one or more vehicles are provided. More specifically, image data may be obtained from an image acquisition device and processed utilizing one or more machine learning models to identify, track, and extract one or more features of the image utilized in decision making processes for providing steering angle and/or acceleration/deceleration input to one or more vehicle controllers. In some instances, techniques may be employed such that the autonomous and semi-autonomous control of a vehicle may change between vehicle follow and lane follow modes. In some instances, at least a portion of the machine learning model may be updated based on one or more conditions.

Abstract: Some embodiments provide an autonomous navigation system which autonomously navigates a vehicle through an environment based on predicted trajectories of one or more separate dynamic elements through the environment. The system identifies contextual cues associated with a monitored dynamic element, based on features of the dynamic element and actions of the dynamic element relative to various elements of the environment, including motions relative to other dynamic elements. A monitored dynamic element can be associated with a particular intention, which specifies a prediction of dynamic element movement through the environment, based on a correlation between identified contextual cues associated with the monitored dynamic element and a set of contextual cues which are associated with the particular intention. A predicted trajectory of the dynamic element is generated based on an associated intention.

Abstract: An alertness level that is the consciousness level of the driver of a vehicle is determined, and the timing of a manual driving return request notification is controlled in accordance with the alertness level. A data processing unit determines the alertness level of the driver of the vehicle, and records evaluation data. The data processing unit acquires observation information about the driver, determines the alertness level of the driver, and evaluates and records the return rate at each time. The vehicle is a vehicle capable of switching between automatic driving and manual driving, and the data processing unit controls the timing of a manual driving return request notification for the driver's return from automatic driving to manual driving, in accordance with the alertness level of the driver. The observation information to be acquired by the data processing unit includes observation information about the driver both before and after the driver gets in the vehicle.

Abstract: An aerial system includes a body, a lift mechanism coupled to the body, a processing system, and at least one camera. The aerial system also includes a first motor configured to rotate the at least one camera about a first axis and a second motor configured to rotate the at least one camera about a second axis. The processing system is configured to determine a direction of travel of the aerial system and to cause the first motor and the second motor to automatically orient the at least one camera about the first axis and the second axis such that the at least one camera automatically faces the direction of travel of the aerial system.

Abstract: The techniques of this disclosure relate to a driver-monitoring system. The system includes a controller circuit that receives monitoring data from a driver-monitor sensor that is configured to monitor a driver of a vehicle while the vehicle operates in an autonomous-driving mode. The controller circuit determines a score of one or more driver-supervisory metrics based on the monitoring data, each of the driver-supervisory metrics being indicative of whether the driver is supervising the operation of the vehicle. The controller circuit determines a supervision score based on the score of the driver-supervisory metrics. The supervision score is indicative of whether the driver is ready to resume control of the vehicle. The controller circuit indicates a driver-awareness status on a display in a field of view of the driver based on the supervision score. The system can improve vehicle safety by alerting the driver when the driver exhibits reduced driver awareness behavior.

Abstract: An autonomous driving control unit 18 of an onboard device 1 acquires the positional accuracies in the travelling direction and the lateral direction of the vehicle, respectively. With reference to map DB 10, the autonomous driving control unit 18 acquires normal line information indicative of the orientation of each feature. The autonomous driving control unit 18 outputs, to an electronic control device of the vehicle, control information for controlling the vehicle to increase the detection accuracy of a target feature which faces a direction, either the travelling direction or the lateral direction of the vehicle, whichever positional accuracy is lower.

Abstract: A system including a boundary wire and a charging station loop. The boundary wire makes a loop in the charging station that is narrower than and crosses the charging station loop. A return signal is received from a control unit commanding a robotic mower to return to the charging station. The robotic mower is controlled to follow the boundary wire until the charging station loop is detected. The robotic mower then follows the charging station loop until detection of a crossing between the charging station loop and the boundary wire loop. The robotic mower follows the charging station loop a first distance, and then moves in a direction straight forward for a second distance. When the robotic mower has moved the second distance it is turned a predefined angle towards the charging station and follows the boundary wire loop until reaching a charging position.

Abstract: A control device includes an electronic control unit executes steering control for avoiding a collision with an object that has a possibility of colliding with a host vehicle. The electronic control unit sets a timing when the host vehicle is predicted to pass the object as an end timing of the steering control when the steering control is performed. The electronic control unit determines whether or not a deviation possibility is present when the electronic control unit assumes that the steering control ends at the end timing. The deviation possibility is a possibility of the host vehicle deviating from a current traveling lane accompanying an end of the steering control. The electronic control unit performs reduction processing that reduces the deviation possibility when the electronic control unit determines that the deviation possibility is present.

Abstract: An activation system includes: management apparatus configured to manage activation information which is information associated with a vehicle and is information for activation of a vehicle control system, the management apparatus being provided outside the vehicle; and an activation device configured to activate the vehicle control system, the activation device being provided in the vehicle. The management apparatus transmits the activation information to the activation device when the management apparatus receives an activation request signal. The activation device activates the vehicle control system when the activation device receives the activation information from the management apparatus.

Abstract: A volume of contents in a container of a work vehicle can be estimated in various examples. One example involves a system with a 3D sensor on a work vehicle, where the sensor captures images of a material in a container of the work vehicle. A processor device that is in communication with the 3D sensor determines a volume of the material in the container using the images.

Abstract: The invention relates to a positioning system for a mobile unit with a reference localization unit, through which a first reference position can be captured at a first point in time and a second reference position of the mobile unit can be captured at a later second point in time; a capturing unit, through with movement data of the mobile unit can be captured; and a computing unit, through which a calculated second position of the mobile unit can be determined at the second point in time by means of a neural network based on the first reference position and the captured movement data. At least one parameter of the neural network can thereby be adjusted based on a comparison of the second reference position with the calculated second position. The invention further relates to a method for operating a positioning system for a mobile unit, in which a first reference position of the mobile unit is captured at a first point in time.

Abstract: Embodiments herein relate to a method performed by an autonomous vehicle for activating an autonomous braking maneuver of the autonomous vehicle having an autonomous drive system. The autonomous vehicle detects at least one user initiated request for an autonomous braking maneuver of the vehicle when the vehicle is in a first autonomous drive mode at a speed. The autonomous braking maneuver is at least one of: speed reduction or stop. When the request has been detected, the autonomous vehicle activates the autonomous braking maneuver of the vehicle which reduces the speed and/or brakes the vehicle to a stop.

Abstract: There is provided a method to stop or slow down a vehicle in an emergency, said vehicle comprising an emergency stop system able to cause the vehicle to stop or slow down, the emergency stop system being able to receive stop signals from users that are not driving the vehicle, the method comprising the steps of a) the emergency stop system receiving a first signal to stop the vehicle, b) the emergency stop system immediately starting a timing procedure, c) the emergency stop system, within a predetermined time window, receiving a predetermined number of further signals to stop the vehicle, and d) the emergency stop system, if the required number of signals has been received, immediately causing the vehicle to stop or slow down.

Abstract: A sensor control system for a vehicle includes one or more processors and a memory communicably coupled to the one or more processors. The memory stores a zone of interest determination module including computer-readable instructions that when executed by the one or more processors cause the one or more processors to determine if at least one localized zone of interest resides within a preliminary zone of interest of the vehicle. The memory also stores a sensor control module including computer-readable instructions that when executed by the one or more processors cause the one or more processors to control one or more operational parameters of at least one sensor so as to, if at least one localized zone of interest resides within the preliminary zone of interest, bring at least a portion of the at least one localized zone of interest into a of field view of the at least one sensor.

Abstract: The present disclosure relates to techniques in the context of vehicles, and in particular to methods for identifying parking areas for vehicles. According to one aspect, the disclosure relates to a method for identifying parking areas. The method comprises determining stops of a plurality of vehicles, wherein one stop is a geographical position where one vehicle of the plurality of vehicles has stayed longer than a pre-determined time period. The method further comprises clustering the determined stops into clusters based on spatial closeness of the geographical positions of the determined stops and determining parking areas by identifying one or more of the clusters that fulfil one or more pre-determined criteria. The disclosure is also related to a control arrangement and to a computer program for performing the proposed method.

Abstract: An information processing method includes: acquiring, from one or more first vehicles, manual driving information for each of the first vehicles; acquiring, from one or more second vehicles, automatic driving information for each of the second vehicles; calculating, for each area, a first value of a driving parameter correlated with a degree of difficulty in driving according to the manual driving information; calculating, for the each area, a second value of the driving parameter according to the automatic driving information; comparing the first value with the second value for the each area to determine at least one difficult area in which travel of a vehicle by automatic driving is difficult; and creating difficult area information.

Abstract: In one embodiment, a method for generating a reference line for operating an autonomous driving vehicle includes determining a first ending reference point having a smallest curvature among a plurality of points within a first defined distance along a path, generating a first reference line based on a first initial reference point and the first ending reference point, determining a second ending reference point having a smallest curvature among a plurality of points within a second defined distance along the path, generating a second reference line based on the first and second ending reference points and an end section of the first reference line, connecting the first and second reference lines, and controlling the autonomous driving vehicle along the connected first reference line and the second reference line.