Abstract: This application provides an internet of things platoon communication method. In this method, an internet of things platform plans and manages a communication mode of a platoon in a traveling process. In the traveling process, the platoon selects, based on a communication mode plan sent by the platform, a communication mode corresponding to a current location of the platoon for communication. Because the platform can obtain global information such as a network and a map, the communication mode planned by the platform is more reasonable and accurate. This resolves a problem of one-by-one switchover and repeated switchover of fleet members at road sections such as a congested road section, a road section with poor network coverage, and a multi-block road section.

Abstract: A server includes a communicator and a server controller is configured to determine a personal mobility group for cluster driving based on position information and destination information of a plurality of personal mobilities received from the communicator. The server controller determines a master personal mobility among the personal mobility group, and operates the communicator to transmit a control command for determining at least one slave personal mobility following the first master personal mobility to the personal mobility group.

Abstract: A method and system for distributing cost of platooning trucks, are discussed. The method comprising a step of receiving (S301, S302) at least two platooning requests and determining (S303), based on said requests, a platooning plan comprising which is determined to reduce a combined estimated resource consumption. The method further comprises the step of determining (S304) at least one estimated resource gain distribution for the vehicles (501, 502, 601, 602) associated with the platooning plan and determining (5305) an estimated compensation associated with each vehicle (501, 502, 601, 602) based on the estimated resource gain distribution.

Abstract: A traveling vehicle system includes a plurality of traveling vehicles, a controller that controls the plurality of transport vehicles, a traveling region of the traveling vehicles having a plurality of blocking sections each of which undergoes exclusive control to prohibit another traveling vehicle from moving thereinto. The traveling vehicle makes a request to the controller for occupation permissions regarding a plurality of blocking sections to be occupied by the traveling vehicle and are designated by instructions. The controller then determines, among the plurality of blocking sections for which the traveling vehicle makes a request for occupation permissions, one or more of the blocking sections that are able to allow permissions consecutively from an end thereof to be primarily occupied, based on the traveling direction of the traveling vehicle, and the controller grants the traveling vehicle the permissions for one or more blocking sections determined to allow the permissions.

Abstract: Aspects of the disclosure relate to testing situational awareness of a test driver tasked with monitoring the driving of a vehicle operating in an autonomous driving mode. For instance, a signal that indicates that the test driver may be distracted may be identified. Based on the signal, that a question can be asked of the test driver may be determined. A plurality of factors relating to a driving context for the vehicle may be identified. Based on the determination, a question may be generated based on the plurality of factors. The question may be provided to the test driver. Input may be received from the test driver providing an answer to the question.

Abstract: A system comprises a computer having a processor and a memory, the memory storing instructions executable by the processor to access sensor data from one or more imaging radar sensors of a vehicle, identify, based on the sensor data, a radar point cloud corresponding to an occupant of the vehicle, analyze the radar point cloud to determine an occupant classification of the occupant, determine an operating parameter for a feature of the vehicle based on the occupant classification, and implement the operating parameter for the feature.

Abstract: An autonomous vehicle (AV) computing device including at least one processor may be provided. The at least processor may be programmed to (i) receive a proposed trip including a destination location and a departure time, (ii) determine environmental conditions data based on the destination location and the departure time, (iii) retrieve current software ecosystem data for the AV, (iv) retrieve aggregated data for a plurality of AVs, the aggregated data including a plurality of correlations, each correlation including a) an interaction between at least one software application and at least one environmental condition and b) an adverse performance outcome associated with the interaction, (v) compare the environmental conditions data for the proposed trip and the current software ecosystem data for the AV to the plurality of correlations to identify an adverse performance outcome, and (vi) execute a remedial action to avoid the adverse performance outcome.

Abstract: A method is provided for classifying a tracked object in an environment of a vehicle. The vehicle includes a plurality of radar sensors and a processing device configured to establish a neural network. According to the method, local radar detections are captured from an object in the environment of the vehicle via the radar sensors. Based on the local radar detections, point features and tracker features are determined. The point features are encoded via point encoding layers of the neural network, whereas the tracker features are encoded via track encoding layers of the neural network. A temporal fusion of the encoded point features and the encoded tracker features is performed via temporal fusion layers of the neural network. The tracked object is classified based on the fused encoded point and tracker features via classifying layers of the neural network.

Abstract: The subject disclosure relates to techniques for improving performance of a machine learning algorithm that at least receives data descriptive of a 3-D object and provides an output, where the 3-D object occurs infrequently in a training dataset. A process of the disclosed technology can include determining that the machine learning algorithm performed below a threshold performance score when receiving data descriptive of the 3-D object in a real-world scene, wherein the 3-D object is classified as a first type of object, creating at least one 3-D representation of the first type of object for use in a simulation, modifying a plurality of simulated scenes to include the at least one 3-D representation of the first type of object, and training the machine learning algorithm with the modified simulated scenes, whereby the machine learning algorithm has greater exposure to the first type of object.

Abstract: Systems and method are provided for controlling a vehicle. In one embodiment, a method includes: receiving, by the controller, first return data detected by a first detector of a lidar device as a result of a first laser pulse or chirp; receiving, by the controller, second return data detected by a second detector of the lidar device as a result of a second laser pulse or chirp; combining, by the controller, the first return data and the second return data to form a point cloud; and controlling, by the controller, the vehicle based on the point cloud.

Abstract: Autonomous mobile robots (AMRs) having adaptive safety systems may operate individually or as part of a convoy. During individual operations, an AMR may determine a safety zone and stop responsive to detecting an object within the safety zone. During convoy operations, an AMR may selectively mute a portion of the safety zone and allow a forward AMR of a convoy within the portion of the safety zone. In this manner, the adaptive safety systems may enable convoy operations of multiple AMRs with relatively greater density and greater speed, thereby improving speed and efficiency of AMR operations without negatively impacting safety.

Abstract: Upon detecting a mobile object approaching a target region, a target speed profile for a vehicle, a position and a speed for the vehicle, and a position and a speed for the mobile object are input to a first neural network that outputs an entry probability distribution of predicted entry times at which the mobile object will enter the target region. The target speed profile, the position and the speed for the vehicle, the position and the speed for the mobile object, and a predicted entry time at which the mobile object entered the target region are input to a second neural network that outputs an exit probability distribution of predicted exit times at which the mobile object will exit the target region. An optimized speed profile is determined based on the entry and exit probability distributions by executing a model predictive control algorithm in a rolling horizon. A vehicle is operated based on the optimized speed profile.

Abstract: A platooning control system is based on active collision avoidance control. The system includes a first platooning control apparatus located in a foremost leading vehicle in a platoon for determining whether it is possible for the leading vehicle to collide when the leading vehicle is fully braked and whether it is possible to avoid collision when it is determined that collision will occur when the leading vehicle is fully braked, calculating a longitudinal deceleration profile and a transverse path of the leading vehicle, and collision with following vehicles that follow the leading vehicle, and transmitting the calculation result to the following vehicles.

Abstract: The technology relates to assisting large self-driving vehicles, such as cargo vehicles, as they maneuver towards and/or park at a destination facility. This may include a given vehicle transitioning between different autonomous driving modes. Such a vehicles may be permitted to drive in a fully autonomous mode on certain roadways for the majority of a trip, but may need to change to a partially autonomous mode on other roadways or when entering or leaving a destination facility such as a warehouse, depot or service center. Large vehicles such as cargo truck may have limited room to maneuver in and park at the destination, which may also prevent operation in a fully autonomous mode. Here, information from the destination facility and/or a remote assistance service can be employed to aid in real-time semi-autonomous maneuvering.

Abstract: An apparatus for an autonomous vehicle may include a plurality of sensors and processing circuitry. The processing circuitry may be configured to receive a trip plan via including a pick-up location, a drop-off location, an identity of the passenger, and control parameters, execute the trip plan according to the pick-up location, the drop-off location, the identity of the passenger, and the control parameters, receive sensor data from the plurality of sensors, and analyze the sensor data in relation to a criteria profile to determine whether compliance boundaries of the criteria profile have been violated. The criteria profile may be generated based on the trip plan and may indicate compliance boundaries for the trip plan. The processing circuitry may be further configured to send an alert in response to determining that the sensor data indicates that a violation of a compliance boundary of the criteria profile has occurred.

Abstract: An autonomous driving system for an autonomous vehicle includes a plurality of on-board autonomous sensors that sense data related to operation of the autonomous vehicle and a surrounding environment and an automated driving controller in electronic communication with the plurality of on-board autonomous sensors. The automated driving controller is instructed to receive an indication one or more of the plurality of on-board autonomous sensors are non-functional and a secondary autonomous sensor system including one or more replacement sensors are installed. The automated driving controller is instructed to verify the secondary autonomous sensor system based on a security check and perform a redundancy check between the one or more replacement sensors and the plurality of on-board autonomous sensors. In response to determining the one or more replacement sensors are valid based on the redundancy check, the automated driving controller operates the autonomous vehicle in a limp home mode.

Abstract: Methods, systems and apparatus, including computer programs encoded on computer storage media for unmanned aerial vehicle beyond visual line of sight (BVLOS) flight operations. In an embodiment, a flight planning system of an unmanned aerial vehicle (UAV) can identify handoff zones along a UAV flight corridor for transferring control of the UAV between ground control stations. The start of the handoff zones can be determined prior to a flight or while the UAV is in flight. For handoff zones determined prior to flight, the flight planning system can identify suitable locations to place a ground control station (GCS). The handoff zone can be based on a threshold visual line of sight range between a controlling GCS and the UAV. For determining handoff zones while in flight, the UAV can monitor RF signals from each GCS participating in the handoff to determine the start of a handoff period.

Abstract: Systems and methods for increasing the efficiency of vehicle platooning systems are described. In one aspect, drivers are more likely to enjoy a system if it begins platooning as desired and does not accidently end platoons. When a certain amount of data packets sent between vehicles are dropped, systems typically will either not engage in a platoon or end a current platoon. When a platoon has a very small gap between vehicles, the platoon should end—or not start, when a certain amount of packets are dropped. However, if a gap is large enough to provide a driver with more time to react, a system may accept a greater amount of dropped packets before it refuses to start a platoon or causes the end of a platoon.

Abstract: The apparatus for controlling group driving according to as aspect may include an inter-vehicle communication unit for communicating with a leader vehicle to receive the driving state and traveling track of the leader vehicle, a leader vehicle learning unit for learning a driving pattern of the leader vehicle based on the driving state of the leader vehicle received through the inter-vehicle communication unit, an autonomous drive unit for autonomously driving the follower vehicle in accordance with the traveling track of the leader vehicle, and a follow-up control unit for receiving the driving state of the leader vehicle to learn the driving pattern of the leader vehicle, controlling the autonomous drive unit to follow the traveling track of the leader vehicle, and performing the autonomous driving by applying the driving pattern of the leader vehicle.

Abstract: A management system of a work site in which an unmanned vehicle and a manned vehicle operate in a mixed manner includes: a determination unit that determines whether or not the manned vehicle exists in a predetermined area of the work site; and a command unit that outputs a work command to cause the unmanned vehicle or the manned vehicle to travel to a work point set in a work place based on the determination result.

Abstract: Techniques for determining a degraded state associated with a sensor are discussed herein. For example, a sensor associated with vehicle may captured data of an environment. A portion of the data may represent a portion of the vehicle. Data associated with a region of interest can be determined based on a calibration associated with the sensor. For example, in the context of image data, image coordinates may be used to determine a region of interest, while in the context of lidar data, a beam and/or azimuth can be used to determine a region of interest. A data metric can be determined for data in the region of interest, and an action can be determined based on the data metric. For example, the action can include cleaning a sensor, scheduling maintenance, reducing a confidence associated with the data, or slowing or stopping the vehicle.

Abstract: A method, apparatus, and system for filtering a point cloud generated by a LIDAR device in an autonomous vehicle is disclosed. A point cloud comprising a plurality of points is generated based on outputs of the LIDAR device. The point cloud is filtered to remove a first set of points in the plurality of points that correspond to noise based on one or more of: point intensity measurements, distances between points, or a combination thereof. Perception data is generated based on the filtered point cloud. Operations of the autonomous vehicle are controlled based on the perception data.

Abstract: Systems and methods for situational modification of autonomous vehicle operation are disclosed. According to aspects, a computing device may detect the occurrence of an emergency event and may determine a current operation of an autonomous vehicle that may be associated with the emergency event. The computing device may determine a modification to operation of the autonomous vehicle, where the modification may represent a violation of a roadway regulation that may enable effective handling of the emergency event. The computing device may generate a set of instructions for the autonomous vehicle to execute to cause the autonomous vehicle to undertake the operation modification.

Abstract: A system for a host vehicle includes a processor programmed to receive, from an image capture device, an image representative of an environment of the host vehicle, detect at least one obstacle in the environment of the host vehicle based on an analysis of the at least one image, determine a velocity of the host vehicle and a predicted path for the host vehicle, monitor a driver input to at least one of a throttle control, a brake control, or a steering control associated with the host vehicle, and determine whether the driver input would result in the host vehicle navigating within a proximity buffer relative to the at least one obstacle, wherein the proximity buffer is determined based on the determined velocity, a maximum acceleration capacity of the host vehicle, and a maximum braking capacity of the host vehicle, and a reaction time associated with the host vehicle.

Abstract: A navigation system and a route search method thereof are provided. The navigation system includes a vehicle controller that performs platoon driving and a service server. The service server includes a communicator that communicates with the vehicle and receives traffic information, a database that stores platoon driving information received from the communicator, and a processor that generates a driving route based on the traffic information and the platoon driving information according to a route search request from the vehicle.

Abstract: The technology involves operation of a self-driving truck or other cargo vehicle when it is being inspected at a weigh station. This may include determining whether a weigh station is open for inspection. Once at the weigh station, the vehicle may follow instructions of an inspection officer or autonomous inspection system. The vehicle may perform predefined actions or operations so that various vehicle systems and safety issues can be evaluated, such as the brakes, lights, tires, connections between the tractor and trailer, exposed fuel tanks, leaks, etc. A visual inspection may be performed to ensure the load is secured, vehicle and cargo documents meet certain criteria, and the carrier's safety record meets any requirements. In addition, the weigh station itself may be operated in a partly or fully autonomous mode when dealing with autonomous and manually driven vehicles.

Abstract: A system comprises a lead autonomous vehicle (AV), a control device associated with the lead AV, and a following AV. The control device receives a command to navigate the lead AV to avoid an unexpected road condition. The control device receives sensor data from a sensor of the lead AV, comprising location coordinates of objects ahead of the lead AV. The control device accesses environmental data associated with a portion of a road between the lead AV and following AV. The environmental data comprises location coordinates of objects between the lead AV and following AV. The control device determines whether an object in the sensor data or environmental data impedes performing the command by the following AV. The control device updates the command, if the control device determines that an object impedes performing the command by the following AV, and communicates the updated command to the following AV.

Abstract: A method for controlling a parking of a plurality of autonomous vehicles parking in a row is provided. The method includes determining a minimum row length of the row needed to park the plurality of vehicles in the row taking into account a minimum physical space including an individual vehicle length, determining at least one additional space requirement of at least one of the plurality of vehicles which is temporarily needed by said at least one vehicle for a predefined event, determining a total row length of the row, determining a free space in the row, distributing the free space to the row, determining parking information for each of the plurality of vehicles based on the distributed free space, and distributing the parking information to at least some of the plurality of vehicles.

Abstract: A method and system of determining whether a stationary vehicle is a blocking vehicle to improve control of an autonomous vehicle. A perception engine may detect a stationary vehicle in an environment of the autonomous vehicle from sensor data received by the autonomous vehicle. Responsive to this detection, the perception engine may determine feature values of the environment of the vehicle from sensor data (e.g., features of the stationary vehicle, other object(s), the environment itself). The autonomous vehicle may input these feature values into a machine-learning model to determine a probability that the stationary vehicle is a blocking vehicle and use the probability to generate a trajectory to control motion of the autonomous vehicle.

Abstract: A system includes a computer including a processor and a memory storing instructions executable by the processor to identify a location and an orientation of a vehicle on a map. The instructions include instructions to determine a location of an infrastructure sensor on the map based on the location and the orientation of the vehicle, data from a vehicle sensor, and data from the infrastructure sensor.

Abstract: A device is provided to extract scan lines data from images of a route, said scan line data comprising only a portion of the an image that extends along a scan line, and to store this data in memory together with respective positional data pertaining to the position of the device traveling along the route in a navigable network. This scan line and positional data can be obtained from a plurality of devices in a network and transmitted to computing apparatus, for example, for use in a production of a realistic view of a digital map.

Abstract: Systems and methods are provided for vehicle navigation. In one implementation, a system may comprise an interface to obtain sensing data of an environment of the host vehicle. A processing device may be configured to determine a planned navigational action for the host vehicle; identify a target vehicle in the environment of the host vehicle; predict a following distance between the host vehicle and the target vehicle that would result if the planned navigational action was taken; determine a host vehicle braking distance based on a braking capability, acceleration capability, and speed of the host vehicle; determine a target vehicle braking distance, based on a speed and maximum braking capability of the target vehicle; and implement the planned navigational action when the predicted following distance is greater than a minimum safe longitudinal distance based on the determined host vehicle braking distance and the determined target vehicle braking distance.

Abstract: An unmanned following vehicle includes a controller that controls the unmanned following vehicle to move in parallel with a moving object by following the moving object on a left side or a right side of the moving object. For example, the controller adjusts a moving speed of the unmanned following vehicle according to a Y-axis coordinate difference value, which is a difference value between a position of the moving object and a position of the unmanned following vehicle in a forward direction. The controller also adjusts a steering direction and a steering angle of the unmanned following vehicle according to an X-axis coordinate difference value, which is a difference value between the position of the moving object and the position of the unmanned following vehicle in a lateral direction.

Abstract: Systems and methods are provided for determining a street and segment corresponding to the geographic coordinates for a location and determining a heading and a side of the street for the location. The system and methods further provide for generating a list of places within a predetermined distance from the location, determining a first subset of places of the list of places that are located on the same street as the street corresponding to the geographic coordinates for the location, and generating a second subset of places from the first subset of places, each place of the second subset of places having a same heading and side of the street as the heading and side of the street for the location. The systems and methods further provide for selecting a place of the second subset of places and generating a semantic label for the selected place.

Abstract: In a video image recording device, when a scene outside of a vehicle is recorded through a wind shield, a scene on the inside of a vehicle room is reflected by the wind shield and reflection glare is produced. In order to reduce the influence of this reflection glare, the video image recording device is provided with a video image capture device which obtains a first data on a general video image and a second data on a predetermined part in a vehicle room, wherein the general video image is with a reflection video image, of the predetermined part and also with a transmission video image, a graphical image conversion device which converts the second data into a third data, the third data corresponding to a reflection video image by the wind shield, and a graphical image correction device which corrects the first data based on the third data.

Abstract: A positioned location adjustment method and apparatus. The method includes: a first vehicle sends a request message to a plurality of reference vehicles, where the request message includes current location information of the first vehicle; the first vehicle receives a response message from the reference vehicle, where the response message includes positioned location information of the reference vehicle, a positioning error value of the reference vehicle, and vehicle identifier information of the reference vehicle; the first vehicle determines a second vehicle from the plurality of reference vehicles based on the positioning error value of the reference vehicle; and the first vehicle adjusts the first positioned location information based on positioned location information of the second vehicle and vehicle identifier information of the second vehicle, to obtain second positioned location information. According to the embodiments, positioning precision and accuracy can be improved.

Abstract: The present invention provides an autonomous traveling work machine that can accurately receive positioning signals from navigation satellites and autonomously travel without deviating from a traveling path, even in the case of an inclined slope. The autonomous traveling work machine includes a traveling machine body, a positioning receiver that receives positioning signals from navigation satellites, an autonomous traveling control device that performs control for autonomous traveling along traveling paths based on the positioning signals, an inclination detection unit that detects the inclination of the traveling machine body and outputs inclination angle information, an inclination angle determination unit that determines an inclination angle based on the inclination angle information, and a rotation control mechanism that rotates the positioning receiver with one or more degrees of freedom. The rotation control mechanism keeps the positioning receiver horizontal based on the inclination angle.

Abstract: A method of controlling a primary vehicle (18) comprising an automated driving system (20) for driving the primary vehicle autonomously when the primary vehicle is in an autonomous mode, the primary vehicle also being operable manually by a driver when in a manual mode, the method comprising: determining failure of the driver to accept a request to switch the primary vehicle to the manual mode when the vehicle is in the autonomous mode; determining a primary vehicle driving state; acquiring vehicle data for one or more surrounding secondary vehicles (22); determining a contingency action to take with the primary vehicle based on the primary vehicle driving state and the vehicle data for the or each secondary vehicle; and outputting the contingency action to at least one system of the primary vehicle to drive the primary vehicle autonomously in accordance with the determined contingency action.

Abstract: Logic may implement protocols and procedures for vehicle-to-vehicle communications for platooning. Logic may implement a communications topology to distinguish time-critical communications from non-time-critical communications. Logic may sign time-critical communications with a message authentication code (MAC) algorithm with a hash function such as Keccak MAC or a Cipher-based MAC. Logic may generate a MAC based on pairwise, symmetric keys to sign the time-critical communications. Logic may sign non-time-critical communications with a digital signature. Logic may encrypt non-time-critical communications. Logic may append a certificate to non-time-critical communications. Logic may append a header to messages to create data packets and may include a packet type to identify time-critical communications. Logic may decode and verify the time-critical messages with a pairwise symmetric key. And logic may prioritize time-critical communications to meet a specified latency.

Abstract: Systems and apparatuses include one or more processing circuits comprising one or more memory devices coupled to one or more processors, the one or more memory devices configured to store instructions thereon that, when executed by the one or more processors, cause the one or more processors to: determine at least one reason for a deserter to exit a first platoon; determine a deserter position within the first platoon; communicate the deserter exit to the first platoon after determination of the at least one reason for the deserter to exit the first platoon based at least in part on the deserter position; adapt a behavior of the first platoon to allow the deserter to exit the first platoon; and reconfigure the remaining vehicles from the first platoon into a second platoon.

Abstract: An alert control apparatus that notifies a driver in advance of a transfer of control relating to a driving operation from an automatic driving function to the driver by controlling an alert device mounted on a vehicle, which is equipped with the automatic driving function, includes: an estimator that estimates an occurrence of a change execution situation that requires a lane change under a condition in which the driving operation of the vehicle is controlled by the automatic driving function; a determiner that determines a level of difficulty of lane change control based on a plurality of travel environment factors in the change execution situation; and a notification device that notifies the driver of a possibility of the transfer of the control together with a reason of the transfer of the control with a notification mode corresponding to the level using the alert device.

Abstract: Obstruction detection and management systems and methods include, in an Air Traffic Control (ATC) system including one or more servers communicatively coupled to a plurality of passenger drones via one or more wireless networks, receiving passenger drone data from a plurality of passenger drones, wherein the passenger drone data comprises operational data for the plurality of passenger drones and obstruction data from one or more passenger drones; updating an obstruction database based on the obstruction data, wherein the obstruction database comprises entries of obstructions with their height, size, location, and a permanency flag comprising either a temporary obstruction or a permanent obstruction; monitoring a flight plan for the plurality of passenger drones based on the operational data; and transmitting obstruction instructions to the plurality of passenger drones based on analyzing the obstruction database with their flight plan.

Abstract: An autonomous vehicle (AV) computing device including at least one processor may be provided. The at least processor may be programmed to (i) receive a proposed trip including a destination location and a departure time, (ii) determine environmental conditions data based on the destination location and the departure time, (iii) retrieve current software ecosystem data for the AV, (iv) retrieve aggregated data for a plurality of AVs, the aggregated data including a plurality of correlations, each correlation including a) an interaction between at least one software application and at least one environmental condition and b) an adverse performance outcome associated with the interaction, (v) compare the environmental conditions data for the proposed trip and the current software ecosystem data for the AV to the plurality of correlations to identify an adverse performance outcome, and (vi) execute a remedial action to avoid the adverse performance outcome.

Abstract: An object is to provide a light-irradiation technique that are capable of making the driver of a vehicle around a vehicle platoon aware of changes in the vehicle configuration of the vehicle platoon. During no changes in the vehicle configuration of a vehicle platoon, a controller performs control in such a manner that an irradiation device of the vehicle casts light in a steady-state mode common in the vehicle platoon. In a process where the vehicle merges with another of the vehicle alone or another of the vehicle platoon, the controller performs control in such a manner that the irradiation device of the merging vehicle casts light in a transition mode. In a process where the vehicle leaves the vehicle platoon, the controller performs control in such a manner that the irradiation device of the leaving vehicle casts light in the transition mode.

Abstract: A method for generating a trajectory for a vehicle based on an abstract space of control parameters associated with the vehicle may include applying a machine learning model to determine an abstract space representation of the trajectory, which includes a sequence of control parameters associated with the vehicle. For instance, application of the machine learning model may include performing a search of the abstract space parameterized by control parameters that include derivatives of the position of the vehicle. Examples of control parameters include velocity, acceleration, jerk, and snap. A physical space representation of the trajectory may be determined by at least mapping the sequence of control parameters to a sequence of positions for the vehicle. A motion of the vehicle may be controlled based at least on the physical space representation of the trajectory. Related systems and computer program products are also provided.

Abstract: The present disclosure discloses an engineering machinery equipment, and a method, system, and storage medium for operation trajectory planning thereof, and relates to the field of artificial intelligence, automatic control, and engineering machinery technologies.

Abstract: Included are: a timing determining unit for determining timing for preparing for transfer of driving authority to a driver on the basis of current position information of the vehicle, route information of the vehicle, and vehicle information of the vehicle; an awakening level calculating unit for calculating an awakening level of an occupant on the basis of occupant information; an authority transfer determining unit for determining whether the transfer of the driving authority is allowed on the basis of the calculated awakening level; and a vehicle state controlling unit for performing control to change a state of the vehicle before switching to the manual driving is announced to the occupant of the vehicle when it is determined that the transfer of the driving authority is not allowed.

Abstract: A computer is programmed to determine a localization of a first vehicle, including location coordinates and an orientation of the first vehicle, based on first vehicle sensor data, and to wirelessly receive localizations of respective second vehicles, wherein a first vehicle field of view at least partially overlaps respective fields of view of each of the second vehicles. The computer is programmed to determine pair-wise localizations for respective pairs of the first vehicle and one of the second vehicles, wherein each of the pair-wise localizations defines a localization of the first vehicle relative to a global coordinate system based on a (a) relative localization of the first vehicle with reference to the respective second vehicle and (b) a second vehicle localization relative to the global coordinate system, and to determine an adjusted localization for the first vehicle that has a minimized sum of distances to the pair-wise localizations.

Abstract: In an apparatus for determining a traveling position of an own vehicle that is an autonomous driving vehicle equipped with the apparatus, a judgment section is configured to judge presence or absence of at least one of a travel history of other vehicles regarding the lane in which the own vehicle is traveling and an object that is located in the vicinity of the own vehicle within the lane in which the own vehicle is traveling and should be avoided coming into contact with. The traveling position is a widthwise position of the own vehicle within a lane in which the own vehicle is traveling. A determination section is configured to determine the traveling position using the travel history in response to the judgment section judging that the travel history exists and using position information of the object in response to the judgment section judging that the object exists.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for predicting how likely it is that a target agent in an environment will yield to another agent when the pair of agents are predicted to have overlapping future paths. In one aspect, a method comprises obtaining a first trajectory prediction specifying a predicted future path for a target agent in an environment; obtaining a second trajectory prediction specifying a predicted future path for another agent in the environment; determining that, at an overlapping region, the predicted future path for the target agent overlaps with the predicted future path for the other agent; and in response: providing as input to a machine learning model respective features for the target agent and the other agent; and obtaining the likelihood score as output from the machine learning model.