Abstract: A sensor detects an obstacle to a vehicle. An alert device provides alert information for inquiring a passenger to determine whether to continue automatic driving when a distance between the obstacle and an end of the lane is equal to or larger than a width necessary for travel based on a width of the vehicle. An input device receives a passenger's manipulation to continue automatic driving in response the inquiry provided from the alert device. A command output unit outputs a command to ease lane-based restriction on continuation of automatic driving to an automatic driving control device when the manipulation in response is received.

Abstract: Provided is a vehicle control system for avoiding a collection between a right-turning vehicle and a straight-through vehicle without decreasing comfort for an occupant. In the system, a first vehicle V1 and a second vehicle V2 retain high-precision map data for identifying a traveling lane. When the first vehicle V1 traveling in an autonomous driving mode intends to turn right from a right turn lane at an intersection ahead based on the high-precision map data, the first vehicle V1 transmits a right turn notification indicating the intention to turn right to nearby vehicles using vehicle-to-vehicle communications. When the second vehicle V2 traveling in an autonomous driving mode intends to travel straight at an intersection ahead, the second vehicle V2 recognizes a right-turning vehicle in an oncoming lane at the intersection upon receiving the right turn notification from the right-turning vehicle, and performs a predetermined control for collision avoidance.

Abstract: Method and systems for generating vehicle motion planning model simulation scenarios are disclosed. The system receives a base simulation scenario with features of a scene through which a vehicle may travel. The system then generates an augmentation element with a simulated behavior for an object in the scene by: (i) accessing a data store in which behavior probabilities are mapped to object types to retrieve a set of behavior probabilities for the object; and (ii) applying a randomization function to the behavior probabilities to select the simulated behavior for the object. The system will add the augmentation element to the base simulation scenario at the interaction zone to yield an augmented simulation scenario. The system will then apply the augmented simulation scenario to an autonomous vehicle motion planning model to train the motion planning model.

Abstract: Described herein are examples of a system that processes information describing movement of a vehicle at a time related to a potential collision to reliably determine whether a collision occurred and/or one or more characteristics of the collision. In response to obtaining information regarding a potential collision, data describing movement of the vehicle before and/or after a time associated with the potential collision is analyzed to determine whether the collision occurred and/or to determine one or more collision characteristic(s). The analysis may be carried out at least in part using a trained classifier that classifies the vehicle movement data into one or more classes, where at least some the classes are associated with whether a collision occurred and/or one or more characteristics of a collision. If a collision is determined to be likely, one or more actions may be triggered based on the characteristic(s) of the collision.

Abstract: A system for use with an autonomous vehicle includes one or more processors configured to receive one or more inputs from a driver and to control the autonomous vehicle based on the one or more inputs. Each input is indicative of an anticipated driver response to a driving incident.

Abstract: A sensor pod system includes one or more sensor pods with a plurality of sensors configured to collect data from an environment. A sensor pod may include a housing and extend from a portion of a body of a vehicle. The sensor pod housing may have energy absorbing structures configured to absorb and dissipate energy during an impact in order to protect a pedestrian.

Abstract: A method for controlling operation of a wood-handling device in a work machine. The wood-handling device is attached through a rotation device to an end of a set of booms of the work machine to create a desired orientation of the wood-handling device for the operation. The orientation of the wood-handling device using the rotation device is tied on the basis of the operation to be performed using the set of booms. The orientation of the wood-handling device is tied to the position of the end of the set of booms. The position of the end of the set of booms is defined while performing the operation, on the basis of the position of the end of the set of booms. The wood-handling device is oriented using the rotation device, according to the said tie.

Abstract: A control unit performs a lane departure suppression control when a host vehicle is about to depart from a traveling lane. The control unit determines whether a low impact collision has occurred. The control unit performs a secondary collision damage mitigation control when the low impact collision is determined to have occurred while the lane departure suppression control is being performed.

Abstract: A method is disclosed. A data set including: (a) identifiers of a set of incidents occurring within a defined geographic region to which at least one service provider responded during a first time period and (b) address data identifying a location within the geographic region of each said incident of the set is retrieved over a network. An instruction to generate a heat map of the incidents occurring within the geographic region during the first time period is received from a user via a user interface generated to a display device. In response to the instruction to generate the heat map, the address data is converted to GPS data. A heat map of an aerial view of the geographic region based on the GPS data is generated. The heat map is displayed to the display device in a user interface.

Abstract: Systems and methods are provided for navigating a host vehicle. A processing device may be programmed to receive an image representative of an environment of the host vehicle; determine a planned navigational action for the host vehicle; analyze the image to identify a target vehicle travelling toward the host vehicle; determine a next-state distance between the host vehicle and the target vehicle that would result if the planned navigational action was taken; determine a stopping distance for the host vehicle based on a braking profile, a maximum acceleration capability, and a current speed of the host vehicle; determine a stopping distance for the target vehicle based on a braking profile and a current speed of the target vehicle; and implement the planned navigational action if the determined next-state distance is greater than a sum of the stopping distances for the host vehicle and the target vehicle.

Abstract: A system for illuminating an area around a motor vehicle, having: a pair of radar systems and a pair of lighting systems mounted on the motor vehicle; a vehicle level sensor configured to detect changes in pitch or roll of the motor vehicle; and an illumination control system in the motor vehicle. The illumination control system includes a key fob communication system and determines the location of a person by detecting the person approaching the motor vehicle by detecting the presence of the key fob while also detecting the location of the person with a radar system mounted on the vehicle. Next, when the location of the person has been determined, the illumination system lights up the area the person is standing while following movement of the person with the radar system to continuously re-directing the illumination towards the location on the ground where the person is standing as the person moves.

Abstract: A vehicle control device includes a communication unit configured to communicate with a plurality of autonomous driving vehicles configured to perform autonomous traveling, and a processor. The processor is configured to, when an abnormality occurs in or around at least one first autonomous driving vehicle among the plurality of autonomous driving vehicles, determine a travel instruction for controlling traveling of the first autonomous driving vehicle, transmit the travel instruction to the first autonomous driving vehicle via the communication unit, set a priority representing the degree of the priority in which an instruction operator is notified of the travel instruction in the order determined according to the content of the abnormality, notify any one of at least one instruction terminal of the determined travel instruction in the order of highest priority, and receive a result of checking the determined travel instruction from the instruction terminal.

Abstract: Disclosed is a driving assist apparatus which executes a process for collision avoidance when the possibility of collision of a vehicle with an obstacle is high. When the steering angle of the steering wheel is greater than an angle threshold, the apparatus determines that a driver is trying to avoid the collision by operating the steering wheel and does not execute the collision avoidance process. When the vehicle is making a right turn or a left turn, there arises a possibility that the collision avoidance process is not executed even in a situation where the collision avoidance process must be executed. In view of this, when the vehicle is making a right turn or a left turn, the angle threshold mentioned above is set to a larger value as compared with the case where the vehicle is not making a right turn or a left turn.

Abstract: An information processing apparatus according to an embodiment of the present technology includes a detection unit, an estimation unit, and a prediction unit. The detection unit detects a target object from an input image. The estimation unit estimates a posture of the detected target object. The prediction unit predicts an action of the target object on a basis of the estimated posture.

Abstract: A collision avoidance method for a water-based vessel, including: obtaining real time data relating to the path of two or more vessels; identifying a collision risk between the two or more vessels; determining if the collision risk is above a predetermined threshold, wherein when the collision risk is above the predetermined threshold, determining one or more collision avoidance manoeuvres on the basis of historical navigational data which corresponds to the real-time data; providing the one or more collision avoidance manoeuvres to an operator of the one or more vessels.

Abstract: A target detection method based on fusion of vision, lidar and millimeter wave radar comprises: obtaining original data detected by a camera, a millimeter wave radar, and a lidar, and synchronizing the millimeter wave radar, the lidar, and the camera in time and space; performing a calculation on the original data detected by the millimeter wave radar according to a radar protocol; generating a region of interest by using a position, a speed, and a radar reflection area obtained from the calculation; extracting feature maps of a point cloud bird's-eye view and the original data detected by the camera; projecting the region of interest onto the feature maps of the point cloud bird's-eye view and the original data detected by the camera; fusing the feature maps of the point cloud bird's-eye view and the original data detected by the camera, and processing a fused image through a fully connected layer.

Abstract: Techniques are described for enabling a drone device to use a dynamic multi-dimensional spatial representation of an indoor property environment to improve autonomous navigation. In some implementations, an instruction to perform an action at a particular location of a property is received by a drone device. A spatial representation of the property that identifies a dynamic object is obtained by the drone device. The status of the dynamic object impacts an ability of the drone device to navigate near the dynamic object. Sensor data collected by one or more sensors of a monitoring system of the property and that indicates a present status of the dynamic object is obtained by the drone device. A path to the particular location is determined by the drone device. The path to the particular location is finally navigated by the drone device.

Abstract: A method for passing a curve, the method may include sensing, by a vehicle sensor, environment information regarding an environment of the vehicle; sensing at least one current propagation parameter of the vehicle; detecting, based on at least the environment information, (a) that the vehicle is about to reach the curve, (b) one or more first road conditions of a first road segment that precedes the curve; determining one or more curve passing propagation parameters to be applied by the vehicle while passing the curve, wherein the determining is based, at least in part, on the one or more first road conditions and on the at least one current propagation parameter; and responding to the determining.

Abstract: Embodiments disclose a method and a system to operate an autonomous driving vehicle (ADV). In one embodiment, a system determines a source lane speed limit (SLSL) for a source lane (SL) and a target lane speed limit (TLSL) for a target lane (TL), where the SLSL is greater than the TLSL. The system generates a first trajectory for the ADV to follow the SL, the first trajectory having a modified speed corresponding to the TLSL instead of the SLSL. The system generates a second trajectory for the ADV to change from the SL to the TL, the second trajectory having a speed corresponding to the TLSL. The system determines a first cost and a second cost for the first trajectory and the second trajectory respectively based on a cost function for comparison. The system controls the ADV according to a trajectory with a lowest cost.

Abstract: Methods and systems for controlling navigation of a vehicle are disclosed. The system will first detect a URU within a threshold distance of a drivable area that a vehicle is traversing or will traverse. The system will then receive perception information relating to the URU, and use a plurality of features associated with each of a plurality of entry points on a drivable area boundary that the URU can use to enter the drivable area to determine a likelihood that the URU will enter the drivable area from that entry point. The system will then generate a trajectory of the URU using the plurality of entry points and the corresponding likelihoods, and control navigation of the vehicle while traversing the drivable area to avoid collision with the URU.

Abstract: A method includes receiving data relating to pedestrian activity at one or more locations outside of a crosswalk, analyzing the data, based on the data, identifying at least one location of the one or more locations as a constructive crosswalk, and controlling operation of an autonomous vehicle based on the at least one location of the constructive crosswalk.

Abstract: Navigation systems can identify objects in an environment and generate representations of those objects. A representation of an articulated vehicle can include two segments rotated relative to each other about a pivot, with a first segment corresponding to a first portion of the articulated vehicle and the second segment corresponding to a second portion of the articulated vehicle. The articulated object can be tracked in the environment by generating estimated updated states of the articulated agent based on previous states and/or measured states of the object using differing motion model updates for the differing portions. The estimated updated states may be determined using one or more filtering algorithms, which may be constrained using pseudo-observables.

Abstract: An autonomous vehicle is configured to estimate a change in direction of a vehicle that is on a roadway and is proximate to the autonomous vehicle. The autonomous vehicle has a mechanical system, one or more sensors that generate one or more sensor signals, and a computing system in communication with the mechanical system and the one or more sensors. The autonomous vehicle is configured to detect an imminent lane change by another vehicle based on at least one of a computed angle between a wheel of the other vehicle and a longitudinal direction of travel of the other vehicle, a degree of misalignment between the wheel of the other vehicle and a body of the other vehicle, and/or an eccentricity of the wheel of the other vehicle. The mechanical system of the autonomous vehicle is controlled by the computing system based upon the detected imminent lane change.

Abstract: Among other things, sensor signals are received using a vehicle comprising an autonomous driving capability. A risk associated with operating the vehicle is identified based on the sensor signals. The autonomous driving capability is modified in response to the risk. The operation of the vehicle is updated based on the modifying of the autonomous capability.

Abstract: A system, method and storage medium for providing an emergency vehicle alert includes first device transmitting EV data including a current location of an EV to a management server, management server receiving the EV data from the first device, the management server determining a geofence for the EV at least based on the received EV data and geographical map data near the EV, and management server transmitting the determined geofence to a second device. The first device is associated with the EV, and the second device is associated with the another vehicle.

Abstract: Systems and methods are provided for navigating a host vehicle. At least one processing device may be programmed to receive an image representative of an environment of the host vehicle; determine a planned navigational action for the host vehicle; analyze the image to identify a target vehicle in the environment of the host vehicle; determine a next-state lateral distance between the host vehicle and the target vehicle that would result if the planned navigational action was taken; determine a lateral braking distance for the host vehicle and the target vehicle based on a maximum yaw rate capability, a maximum change in turn radius capability, and a current lateral speed of the host vehicle and the target vehicle; and implement the planned navigational action if the determined next-state distance is greater than a sum of the lateral braking distances for the host vehicle and the target vehicle.

Abstract: A method and system for representing an environment of a first mobile platform. The method includes: capturing features of the environment by discrete time sequences of sensor-data from at least two sensors and respective time markers; determining distances of the first mobile platform to the features of the environment; estimating semantic information of the features of the environment; transforming the semantic information of the features of the environment into a moving spatial reference system, wherein a position of the first mobile platform is at a constant site, using the respective determined distances and respective time markers; creating an input tensor using sequences of the transformed semantic information of the features of the environment, corresponding to the sequences of the sensor data of the at least two sensors; generating an output tensor that represents the environment using a deep neural network at a requested point in time and the input tensor.

Abstract: The invention relates to a method for determining a model of the environment of a vehicle, in which method an initial position estimate for the vehicle is acquired and map data are acquired, wherein the map data comprise information about the spatial arrangement of geographical areas, and the geographical areas are assigned to different area categories. Environmental data within an acquisition space is acquired, and objects are detected using the environmental data, wherein an object position and an object category are assigned to each detected object. The environmental model is determined using the detected objects. Assignment rules are provided which define the assignment of the object categories to the area categories, wherein the objects are detected in accordance with the assignment rules.

Abstract: A control unit for a vehicle is designed to detect movement sensor data with regard to a movement of at least one component of the vehicle. The movement is or has been effected by a roadway driven upon by the vehicle. In addition, the control unit is designed to detect a lane boundary of the roadway on the basis of movement sensor data and, in reaction thereto, to cause a functional reaction of the vehicle.

Abstract: A set of sensor information may include first sensor information generated based on a first sensor of a first vehicle and second sensor information generated based on a second sensor of a second vehicle. Individual sensor information may characterize positions of objects in an environment of individual sensors. Relevant sensor information for a vehicle may be determined based on the set of sensor information and a position of the vehicle. The relevant sensor information may characterize positions of objects in a maneuver environment of the vehicle. A desired navigation of the vehicle in the maneuver environment of the vehicle may be determined based on the relevant sensor information. An instruction may be provided to the vehicle based on the desired navigation of the vehicle. The instruction may characterize one or more maneuvers to be performed by the vehicle to execute the desired navigation.

Abstract: Techniques are disclosed for creating and presenting a graphical user interface for display of autonomous vehicle behaviors. The techniques include determining a trajectory of a vehicle operating in a real-world environment. Sensors of the vehicle obtain sensor data representing an object in the real-world environment. A maneuver of the vehicle to avoid a collision with the object is predicted based on the sensor data and the trajectory of the vehicle. It is determined that a passenger comfort level of a passenger riding in the vehicle will decrease based on the maneuver of the vehicle. The passenger comfort level is measured by passenger sensors of the vehicle. A graphical user interface is generated including representations of the vehicle, the object, and a graphic, text, or a symbol alerting the passenger of the predicted maneuver. The graphical user interface is transmitted to a display device of the vehicle.

Abstract: A method of controlling autonomous driving of a vehicle includes: selecting a target object ahead of the vehicle based on driving information, generating a velocity profile for maintaining a desired distance to the target object, calculating a desired acceleration based on the velocity profile and a delay time of the vehicle, and controlling an actuator of the vehicle based on the desired acceleration.

Abstract: A computer includes a processor and a memory, the memory including instructions executable by the processor to predict a collision time between a corner point of a host vehicle and a target vehicle, predict a reach time for the corner point of the host vehicle to reach the target vehicle based on a predicted relative heading angle between the host vehicle and the target vehicle, and determine a threat number based on the lower of the collision time and the reach time.

Abstract: Collision alert systems and methods for micromobility vehicles includes a proximity sensor, a speed sensor, a warning device, and a controller having a bypass mode and a warning mode. The controller compares the speed of the micromobility vehicle with a predetermined speed threshold, enters the bypass mode when the speed of the micromobility vehicle is less than the predetermined speed threshold, and enters the warning mode when the speed of the micromobility vehicle is greater than the predetermined speed threshold. The controller does not activate the warning device in the bypass mode. In the warning mode, calculates an estimated time until a potential collision with the object, compares the estimated time object with a predetermined time threshold, and generates a collision warning by activating the warning device in response to the estimated time until collision being less than the predetermined time threshold.

Abstract: In an apparatus for controlling a vehicle equipped with an autonomous sensor and a communication unit, an object detector is configured to determine whether a predefined mobile-object condition is met, where the predefined mobile-object condition indicates that a mobile object detected from detection information received from an external device via the communication unit and a mobile object detected from detection information acquired from the autonomous sensor are the same object. An occupant assister is configured to perform occupant assistance for assisting an occupant of the vehicle, and configured to, in response to a detection accuracy condition being met, determine a mode of occupant assistance as a first mode, and in response to neither the detection accuracy condition nor the mobile-object condition being met, determine the mode of occupant assistance as a second mode is different from the first mode.

Abstract: A vehicle collision prediction apparatus, installable to a host vehicle that is equipped with a peripheral sensor for detecting a preceding vehicle, includes a collision prediction region setting section that sets a collision prediction region in front of the host vehicle and a collision prediction section which predicts that there is a probability of a collision between the host vehicle and the preceding vehicle if the preceding vehicle is within the collision prediction region; wherein in response to the preceding vehicle satisfying predetermined U-turn conditions for estimating that the preceding vehicle is executing a U-turn, the collision prediction region setting section expands the collision prediction region, making the collision prediction region larger than in response to the preceding vehicle not satisfying the U-turn conditions.

Abstract: A method for adjusting the path of a satellite to limit a risk of collision with items of debris each having a date of closest pass with the satellite is disclosed including: propagating at least one orbit from the reference path of the satellite according to at least one manoeuvre to the farthest date of closest pass; determining a probability of collision for each item of debris according to the at least one orbit; determining at least one overall probability according to the set of probabilities determined; selecting the lowest overall probability from among the at least one overall probability obtained; determining a command for the satellite including the manoeuvre associated with the lowest overall probability.

Abstract: The invention relates to a driver assistance system for a motor vehicle, the motor vehicle and a method for operating same. In some embodiments, an obstacle is detected based on environmental data and a risk of collision is determined in consideration of driving state data. Further, an evasion trajectory for preventing a collision of the motor vehicle with the obstacle is determined. The driver assistance system is configured to detect another oncoming vehicle for which the risk of collision is greater than a predefined first threshold value as the obstacle. Further, the driver assistance system is configured to check whether a control action of a driver of the motor vehicle is guiding same along the determined evasion trajectory and, if this is not the case, to modify the control action of the driver automatically such that the motor vehicle is guided along the determined evasion trajectory by the modified control action.

Abstract: The present invention regards a watercraft vehicle (1) having a propeller shaft (9) coupled to a motor (3) and a propeller (7) forming a propeller disc (11) having a hub (17). A first blade (8) of the propeller (7) is hingedly coupled to a first oblique lag-pitch hinge (22?) of the hub (17) and a second blade (10) of the propeller (7) is hingedly coupled to a second oblique lag-pitch hinge (22?) of the hub (17). The first oblique lag-pitch hinge (22?) being oriented in a direction oblique to the axis of rotation (RX) and parallel with the second oblique lag-pitch hinge (22?).

Abstract: An object detection device includes: a transmission and reception unit configured to transmit a transmission wave including an ultrasonic wave having directivity in a direction parallel or substantially parallel to a traveling direction of a movable body, and receive a reflected wave from an object; a determination unit configured to determine presence or absence of an abnormality based on a predetermined reference distance and a downward distance between the transmission and reception unit and an object present below the transmission and reception unit in a vertical direction, the downward distance being calculated based on a reflected wave of an ultrasonic wave of the transmission wave traveling downward in the vertical direction from the transmission and reception unit; and an output unit configured to output information regarding the abnormality.

Abstract: A method of tracking at least one emergency service provider is disclosed. An electronic history is compiled that includes at least one identifier of a service provider, at least one identifier of an event to which the service provider responded, and GPS data identifying the geographic location of the service provider at each time interval within the duration of the event. A user interface within which is displayed a first identifier of a first event is generated to a display device. A selection of the event identifier is received from a user. In response to the selection of the identifier, an aerial view of a geographic region within which the first event took place is generated. At least one icon is displayed in the aerial view representing the service provider at the geographic location corresponding to at least one time interval during the event.

Abstract: Systems and methods are provided for detecting objects of interest. A computing system can input sensor data to one or more first machine-learned models associated with detecting objects external to an autonomous vehicle. The computing system can obtain as an output of the first machine-learned models, data indicative of one or more detected objects. The computing system can determine data indicative of at least one uncertainty associated with the one or more detected objects and input the data indicative of the one or more detected objects and the data indicative of the at least one uncertainty to one or more second machine-learned models. The computing system can obtain as an output of the second machine-learned models, data indicative of at least one prediction associated with the one or more detected objects. The at least one prediction can be based at least in part on the detected objects and the uncertainty.

Abstract: Among other things, techniques are described for predicting how an agent (e.g., a vehicle, bicycle, pedestrian, etc.) will move in an environment based on prior movement, the road network, the surrounding objects and/or other relevant environmental factors. One trajectory prediction technique involves generating a probability map for an agent's movement. Another trajectory prediction technique involves generating a trajectory lattice, for an agent's movement. In addition, a different trajectory prediction technique involves multi-modal regression where a classifier (e.g., a neural network) is trained to classify the probability of a number of (learned) modes such that each model produces a trajectory based on the current input.

Abstract: A method of controlling the operation of a foldable accelerator pedal device in manual driving mode of an autonomous driving vehicle includes: when an autonomous driving vehicle provided with a foldable accelerator pedal device is driven in manual driving mode and the speed of the autonomous driving vehicle exceeds the driving speed limit of a road, a safety mode, which includes a haptic mode and a pedal pad protrusion reducing mode, and a pedal pad hiding mode, is activated using an actuator provided for a folding function of a pedal pad.

Abstract: A method for calculating a dangerous spot and time for a computer to execute a process includes, detecting, by at least two mobile bodies, an avoidance action, which is an action that indicates a possibility that each mobile body has avoided a collision with another mobile body, based on locus data of a plurality of mobile bodies that belongs to a predetermined area; calculating an evaluation value that indicates a possibility that an avoidance action by one of the two mobile bodies has occurred under an influence of an avoidance action by another one; and calculating, based on the evaluation value, a collision risk in an area where a plurality of mobile bodies is concentrated.

Abstract: In various examples, a sequential deep neural network (DNN) may be trained using ground truth data generated by correlating (e.g., by cross-sensor fusion) sensor data with image data representative of a sequences of images. In deployment, the sequential DNN may leverage the sensor correlation to compute various predictions using image data alone. The predictions may include velocities, in world space, of objects in fields of view of an ego-vehicle, current and future locations of the objects in image space, and/or a time-to-collision (TTC) between the objects and the ego-vehicle. These predictions may be used as part of a perception system for understanding and reacting to a current physical environment of the ego-vehicle.

Abstract: A lift device includes a chassis, tractive elements, a platform, a lift assembly, a first set of proximity sensors, and a controller. The tractive elements are rotatably coupled to the chassis and support the chassis. The platform is disposed above the chassis and includes a deck for supporting an operator. The lift assembly couples the platform to the chassis and can selectably move the platform between a lowered position and a raised position. The first set of proximity sensors are coupled to the platform and detect a distance of an obstacle relative to the platform. The controller is operably coupled with an alert system and receives obstacle detection data from the first set of proximity sensors. The controller selects a subset of the first set of proximity sensors to analyze based on an active function of the lift device and determines whether the obstacle is within the minimum allowable distance.

Abstract: The present disclosure provides a driving assistance method and system for a mobile vehicle to avoid hazards in its environment. This system can detect hazards in a vehicle's surroundings and unobtrusively adjust its driving module's driving commands when necessary to avoid hazards. The system is configured to allow it to provide interference-free assistance to the operator of a human-operated vehicle or the motion controller of an autonomous vehicle to avoid hazards.

Abstract: Methods and systems are disclosed including a mobile device configured for initiating a communication session with a transceiver interface of a node that comprises a network interface. The node may be queried via the communication session for a status report associated with an error, and an instruction may be communicated to the node via the communication session instructing the node to perform an operation. Communication may be established with the wireless access point via the wireless network after communication of the instruction. The transceiver interface may be configured to communicate via a second network to address issues in the wide area network.

Abstract: Among other things, we describe techniques for adjusting lateral clearance for a vehicle using a multi-dimensional envelope. A trajectory is generated for the vehicle. A multi-dimensional envelope is generated indicating a drivable region for the vehicle and containing the trajectory. One or more objects are identified located along or adjacent to the trajectory. At least one dimension of the generated multi-dimensional envelope is adjusted to adjust a lateral clearance between the vehicle and the identified one or more objects. A control module of the vehicle navigates the vehicle along the multi-dimensional envelope.