Abstract: A vehicle traveling control apparatus is configured to be applied to a vehicle and includes a traveling environment data acquiring unit and a traveling controller. The traveling controller includes a preceding vehicle recognizing unit, a curve curvature detecting unit, and a vehicle speed keep controlling unit. The preceding vehicle recognizing unit is configured to recognize a preceding vehicle, based on traveling environment data acquired by the traveling environment data acquiring unit. The curve curvature detecting unit is configured to detect a curvature of a curved road, based on the traveling environment data or road map data. The vehicle speed keep controlling unit is configured such that, in a case where the preceding vehicle recognizing unit loses track of the preceding vehicle, the vehicle speed keep controlling unit cause the vehicle to travel on the basis of a preceding-vehicle-following vehicle speed that is before losing track of the preceding vehicle.

Abstract: Techniques for determining a parking trajectory for a vehicle are discussed herein. A parking management component may determine or receive a three-dimensional grid (“grid”), discretized based at least in part on a heading offset, a lateral offset, and/or a longitudinal offset between a first and second pose. A cell of the grid may include a cost associated indicating a minimum difference to the second pose when moving from the first as may be limited based on kinematic and/or dynamic constraints. When driving, a vehicle may determine a relative state of the vehicle to a desired location, use the relative state to access a cost from the grid, and determine whether to follow a trajectory based on the cost.

Abstract: A sorting system includes a first number of entry points configured to provide sorting goods. The sorting system further has a second number of terminal points configured to receive the sorting goods, and a third number of driverless sorting vehicles configured to transport the sorting goods between the first number of insertion points and the second number of terminal points. Further, a control device is provided, configured to control the driverless sorting vehicles between the first number of insertion points and the second number of terminal points.

Abstract: Methods and systems are provided for determining and mitigating vehicle pull force from passing other vehicles. In an exemplary embodiment, methods and systems are provided that include: obtaining sensor data via one or more sensors of a vehicle, the sensor data including both perception sensor data and vehicle dynamics sensor data; identifying, via a processor, one or more additional vehicles to be passed by the vehicle, using the perception sensor data; and predicting and determining, via the processor, a pull force for the vehicle that is caused by the passing of the vehicle by the one or more additional vehicles along with an impact of the pull force on the vehicle, based on both the perception sensor data and the vehicle dynamics sensor data, and control the vehicle to proactively mitigate the pull force on the vehicle; and vehicle with trailer when towing.

Abstract: Systems and methods are provided for vehicle navigation. In one implementation, at least one processor may receive, from a camera, at least one captured image representative of features in an environment of the vehicle. The processor may identify an intersection and a pedestrian in a vicinity of the intersection represented in the image. The processor may determine a navigational action for the vehicle relative to the intersection based on routing information for the vehicle; and determine a predicted path for the vehicle relative to the intersection based on the determined navigational action and a predicted path for the pedestrian based on analysis of the image. The processor may further determine whether the vehicle is projected to collide with the pedestrian based on the projected paths; and, in response, cause a system associated with the vehicle to implement a collision mitigation action.

Abstract: A vehicle control apparatus includes a microprocessor configured to perform generating an action plan so that the subject vehicle moves from a first lane to a second lane when it is determined that the lane change is necessary. The target route includes a specific road in which the second lane is separated from the first lane up to a first point ahead of a current position, the second lane is adjacent to the first lane from the first point to a second point ahead of the first point, and the second lane is separated from the first lane again behind the second point. The action plan is generated so that the lane change is performed in a section from the first point to the second point, and the lane change is started from a position away from the first point by a predetermined distance ahead.

Abstract: Systems and techniques are provided for simulating movement of an articulated vehicle. An example method can include generating a simulation environment for an autonomous vehicle, wherein the simulation environment includes at least one articulated vehicle that includes a tractor coupled to a first trailer using a first pivot joint; determining a first velocity of the first pivot joint based on a first simulated movement associated with the tractor; and determining a second simulated movement associated with the first trailer based on the first velocity of the first pivot joint and a first distance between the first pivot joint and a first point on the first trailer.

Abstract: Described herein are systems and methods for training machine learning models to generate three-dimensional (3D) motions based on light detection and ranging (LiDAR) point clouds. In various embodiments, a computing system can encode a machine learning model representing an object in a scene. The computing system can train the machine learning model using a dataset comprising synchronous LiDAR point clouds captured by monocular LiDAR sensors and ground-truth three-dimensional motions obtained from IMU devices. The machine learning model can be configured to generate a three-dimensional motion of the object based on an input of a plurality of point cloud frames captured by a monocular LiDAR sensor.

Abstract: A management server executes a process including: a step of acquiring operation information when an execution condition is established; a step of setting a traveling route; a step of calculating an accumulated amount of load when there is a vehicle set on the forwarding route; a step of setting the details of canceling the travel restriction according to the accumulated amount of the load of the vehicle on the forwarding route when there is a vehicle in a high accumulated state; a step of setting cancellation of travel restrictions for vehicles on the forwarding route when there is no vehicle in a high accumulation state; and a step of transmitting the travel plan information.

Abstract: A method for protection of inflight aircraft during approaching-to-landing and takeoff/climbout phases of flight against handheld laser attacks includes two different drone types: a skeining drone and a swarming drone. One or more skeining drones are deployed close to the aircraft and/or one or more swarming drones are deployed further from the aircraft and closer to the beam source. Prior to the aircraft's traversal of a determinable approach point, a plurality of swarming drones are pre-deployed in loitering mode or else launched, and are subsequently directed toward the reckoned source of a trained beam while skeining drones are pre-deployed in a patrol mode or else launched, and fly closer to the aircraft. The skein classically shields the cockpit by flying a controlled interference pattern roughly parallel to the aircraft flightpath while the swarm saturates one or more determined and dynamically redetermined regions athwart the beam source location.

Abstract: Techniques are disclosed to increase the safety of vehicles travelling in a vehicle platoon. These techniques include the utilization of a comprehensive safety framework such as a safety driving model (SDM) for the platoon control systems. In contrast to the conventional approaches, the use of the SDM model allows for platoon vehicle control systems to consider the acceleration/deceleration capabilities of the vehicles to calculate minimum safe longitudinal distances between the platoon vehicles. The disclosed techniques may utilize the periodicity of platoon messages as well as other parameters to improve upon platoon vehicle control and safety.

Abstract: A method includes: capturing, via a navigational sensor of a mobile automation apparatus, three-dimensional point cloud data depicting a portion of an aisle containing a support structure, the support structure having a forward plane facing into the aisle; generating, from the point cloud data, a two-dimensional projection in a facility coordinate system; retrieving, from a stored map, an expected location of the forward plane of the support structure in the facility coordinate system; selecting, from the projection, a subset of regions satisfying a positional criterion relative to the location of the forward plane; determining, based on the selected subset of regions from the projection, an actual location of the forward plane of the support structure in the facility coordinate system; and providing the actual location of the forward plane to a navigational controller of the mobile automation apparatus.

Abstract: The driver assistance system comprises a detector, a navigator, a stimulator, and a voice interactor. The detector detects wakefulness reduction of a driver or absent-minded driving by the driver. The navigator navigates a vehicle based on map information. The stimulator continuously or intermittently applies stimulation to the driver. The voice interactor interacts with the driver by voice. The navigator searches for a resting facility on a route from the map information in response to a detection of the wakefulness reduction or the absent-minded driving. The voice interactor executes a first interaction of proposing navigation to the resting facility by the navigator in response to the detection of the wakefulness reduction or the absent-minded driving, and executes a second interaction of proposing application of stimulation by the stimulator after an execution of the first interaction and during a period until the vehicle arrives at the resting facility.

Abstract: An autonomous vehicle (AV) includes features that allows the AV to comply with applicable regulations and statutes for performing safe driving operation. An example method includes detecting that a group of motorcycles is operating on a roadway on which the AV is located. The group of motorcycles are each located within a pre-determined distance away from one another. The method further includes determining an aggregate footprint area that surrounds respective locations of the group of motorcycles. The method further includes causing navigation of the autonomous vehicle that avoids penetration of the aggregate footprint area based on transmitting navigation instructions to one or more subsystems of the autonomous vehicle.

Abstract: Aspects of the disclosure provide for arranging trips for autonomous vehicles. For instance, a request for a trip may be received by one or more processors of one or more server computing devices. The request may identify an initial location. A weather condition at the initial location may be identified. One or more internal vehicle state conditions and one or more priorities for pulling over may be determined based on the weather condition. A second location may be determined based on the one or more priorities and the initial location. Dispatch instructions may be provided to an autonomous vehicle, the dispatch instructions identifying the second location and the one or more internal vehicle state conditions in order to cause computing devices of the autonomous vehicle to control the autonomous vehicle to the second location and adjust internal vehicle state conditions based on the one or more internal vehicle state conditions.

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: Disclosed are a vehicular electronic device and an operation method thereof. The device includes a processor configured to acquire data of a situation of a vehicle, to determine whether the vehicle is a platooning vehicle based on the data, and to determine any one of vehicles in the group as a representative vehicle that transmits a basic safety message (BSM) as a representative of the group upon determining that the vehicle is the platooning vehicle. Data generated by the vehicular electronic device is transmitted to an external device using a 5G communication method. According to the present disclosure, an electronic device of an autonomous vehicle is associated or fuses with a device related to an artificial intelligence module, an unmanned aerial vehicle (UAV), a robot, an augmented reality (AR) device, a virtual reality (VR) device, a 5G service, or the like.

Abstract: The present disclosure involves systems and methods for detecting road blockages and generating alternative routes. In some cases, a system detects, based at least in part on sensor data associated with an autonomous vehicle, a portion of an environment that impedes a planned path of the autonomous vehicle. The system determines a semantic classification associated with the portion of the environment and transmits a re-routing request comprising the semantic classification to one or more remote computing devices. The system receives an instruction associated with navigating the autonomous vehicle around the portion of the environment from the remote computing devices, where the instruction includes an alternative route determined from a plurality of alternative routes. The system further controls the autonomous vehicle to navigate around the portion of the environment based at least in part on the instruction.

Abstract: A control device for controlling a host vehicle including: a processing circuitry configured to, when another vehicle traveling in a opposite direction to the host vehicle and having a possibility to collide with the host vehicle is detected in front of the host vehicle, execute an out-of-vehicle notification using a headlight based on a distance between the host vehicle and the another vehicle. When the distance is equal to or smaller than a first threshold and equal to or larger than a second threshold that is smaller than the first threshold, the processing circuitry executes the out-of-vehicle notification, and when the distance is smaller than the second threshold, the processing circuitry does not execute the out-of-vehicle notification or executes the out-of-vehicle notification with a notification intensity lower than that when the distance is equal to or smaller than the first threshold and equal to or larger than the second threshold.

Abstract: The invention relates to a method for carrying out a driving maneuver for creating a gap in the traffic flow, which is multiple lanes wide, involving a first vehicle and a plurality of second vehicles. The first vehicle, which requests the gap in the traffic flow periodically sends corresponding request signals at least to a plurality of second vehicles. The second vehicles that intend to comply with the request send out corresponding signaling signals. As soon as a sufficient minimum number of second vehicles that intend to comply with the request can be determined, the second vehicles participating in the driving maneuver for creating the gap in the traffic flow are informed. The second vehicles participating in the driving maneuver agree on the beginning of the driving maneuver and mutually coordinate their trajectories. One of the second vehicles is designated as the maneuver leader and transmits at least one characteristic of the gap in the traffic flow to the first vehicle.

Abstract: A vehicle includes one or more sensors configured to obtain raw data related to a scene, one or more processors, and machine readable instructions stored in one or more memory modules. The one machine readable instructions, when executed by the one or more processors, cause the vehicle to: process the raw data with a first neural network stored in the one or more memory modules to obtain a first prediction about the scene, transmit the raw data to a computing device external to the vehicle, receive a second prediction about the scene from the computing device in response to transmitting the raw data to the computing device, and determine an updated prediction about the scene based on a combination of the first prediction and the second prediction.

Abstract: A method for performing at least one perception task associated with autonomous vehicle control includes receiving a first dataset and identifying a first object category of objects associated with the plurality of images, the first object category including a plurality of object types. The method also includes identifying a current statistical distribution of a first object type of the plurality of object types and determining a first distribution difference between the current statistical distribution of the first object type and a standard statistical distribution associated with the first object category. The method also includes, in response to a determination that the first distribution difference is greater than a threshold, generating first object type data corresponding to the first object type, configuring at least one attribute of the first object type data, and generating a second dataset by augmenting the first dataset using the first object type data.

Abstract: A navigational system for a host vehicle may comprise at least one processor. The processor may be programmed to receive an image representative of an environment of the host vehicle; analyze the image to identify a navigational state associated with the host vehicle; and determine, based on the navigational state, a navigational action for the host vehicle based on a policy that maps possible navigational actions to sensed states. The navigational action may be based on a safety constraint applicable to the navigational state, the safety constraint including a safety distance constraint associated with the host vehicle, wherein the safety distance constraint is based on a determined speed of the host vehicle and a determined speed of a detected target object. The processor may cause an adjustment of a navigational actuator of the host vehicle to implement the determined navigational action.

Abstract: Various aspects related to a platooning group in a V2V and/or V2X network are described. In an aspect of the disclosure, a method, a computer-readable medium, and an apparatus are provided. The apparatus maybe a first UE configured to transmit a first message to announce an existence of the platooning group. In one configuration, the first UE may receive, in response to the first message, a second message indicating a request to join the platooning group from a second UE. The first UE may allow or deny the second UE to join the platooning group based on the second message. In another aspect, the apparatus maybe a first UE configured to receive a first message indicating an existence of a platooning group. The first UE may be configured to decode a portion of the first message, and determine whether the platooning group is a private group based on the decoding.

Abstract: Systems and methods are disclosed for managing task assignments for a plurality of work machines at a site. An assignment engine may: receive first state data for a work machine including historical data, operating condition, and location data, and second state data for the site including characteristic data for materials and a plurality of available tasks; predict performance data and energy consumption data of the work machine for a task; select a task for the work machine by inputting first state data and second state data into a trained reinforcement-learning model, wherein: the model has been trained to learn an assignment policy that optimizes a reward function such that the learned policy selects a task for at least one work machine from the plurality of tasks available at the site; and cause the at least one work machine to be operated according to the at least one task assignment.

Abstract: A server, a personal mobility communicating with the server and a vehicle are provided. The server includes a transceiver that communicates with vehicles and a plurality of personal mobility. A controller sets a geo-fence area based on a size information of the vehicles and a size information of the plurality of personal mobility during cluster driving. The controller determines driving positions of the vehicles as primary in the set geo-fence area based on the size information of the vehicles, determines driving positions of the plurality of personal mobility as secondary in the set geo-fence area based on the size information of the plurality of personal mobility and adjusts layout information for the driving positions determined as the primary and the secondary to be transmitted to the vehicles and the plurality of personal mobility.

Abstract: Disclosed is a method for predicting a trajectory of a traffic participant in a complex heterogeneous environment, including the following steps: obtaining traffic participant information in a complex heterogeneous environment; arranging and numbering traffic participant classes based on the class information, to obtain serial numbers of the traffic participant classes; establishing a position graph, a velocity graph, an acceleration graph, and a class graph, into each of which expert experience is introduced; and capturing topological structure relationships and time dependence relationships to obtain a position hidden state, a velocity hidden state, an acceleration hidden state, and a class hidden state; classifying the position hidden state, the velocity hidden state, the acceleration hidden state, and the class hidden state to obtain a hidden state set of traffic participants; and decoding hidden states of the traffic participants separately using a corresponding decoder to obtain future trajectory predic

Abstract: System, methods, and computer-readable media for localizing an autonomous vehicle to a location in a stored LIDAR map relative to lane markings labeled in the stored LIDAR map. The labeled lane marking may be associated with a dashed line, a solid line, a double line, or an unmarked line. The autonomous vehicle may fit the set of lane marking points with the labeled lane marking via a point-to-line solver that locates the set of lane marking points to the line, excluding a longitudinal variable. The lane matching localizing system utilities a point-to-line iterative closet point, wherein the matched point pairs are calculated by perpendicular point of line from the query point instead of finding the closest sampled point.

Abstract: A platooning control system includes a platoon controller that generates environmental maps used for platooning of vehicles, and a travel controller that controls travel of a following vehicle following a leading vehicle. The platoon controller generates the environmental maps each representing the position of an object around the vehicles, and delivers the maps to the following vehicle. The travel controller transmits object information indicating the position of an object detected from environmental data outputted by an environmental sensor mounted on the following vehicle to the platoon controller, executes update so that the position of the object represented in the latest environmental map is changed to the position of the object at the time when the following vehicle reaches the position of the leading vehicle represented in the latest environmental map, and controls travel of the following.

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 is 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 are employed such that the autonomous and semi-autonomous control of a vehicle changes between lane follow and vehicle follow modes. Further, a vehicle may determine whether to continue to follow a vehicle after the followed vehicle moves in relation to the following vehicle.

Abstract: A framework for safely operating autonomous machinery, such as vehicles and other heavy equipment, in an in-field or off-road environment, includes detecting, identifying, and tracking objects from on-board sensors configured with the autonomous machinery as it performs activities in either an agricultural setting or a transportation environment. The framework generates commands for navigational control of autonomously-operated vehicles in response to detected objects and predicted tracks thereof for safe operation in the performance of those activities. The framework processes image data and range data in multiple fields of view around the autonomously-operated to discern and track objects in a deep learning to accurately interpret this data for determining and effecting such navigational control.

Abstract: A system included and a computer-implemented method performed in an autonomous-driving vehicle are described. The system performs: receive a request to meet a person at a location; drive the vehicle to the location; identify the person at the location; and providing an instruction for the person to interact with the vehicle.

Abstract: An autonomous vehicle may include an autonomous driving control apparatus having a processor that transmits vehicle data and a vehicle path for remote control of the autonomous vehicle to a control system when the remote control of the autonomous vehicle is required, and performs following and control based on adjusted path information when receiving the adjusted path information from the control system.

Abstract: A control unit that controls a reaction force actuator that applies steering guide torque to a steering wheel: calculates, based on a curvature of a curve of a travel road in front of a vehicle detected by a camera sensor, a target steering angle for causing the vehicle to travel along the curve; calculates a target steering guide torque, based on a deviation between the target steering angle that was calculated a prediction time period earlier and an actual steering angle; adjusts a target steering guide torque such that the target steering guide torque becomes smaller as a probability that a driver performs steering operation to deviate from a lane becomes higher; and controls the reaction force actuator such that the steering guide torque becomes the target steering guide torque.

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 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 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 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: 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: 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: 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.