Abstract: A method and apparatus for annotating a virtual lane at a crossing is provided, which relates to the field of intelligent transportation, and specifically includes: calculating a virtual connecting probability of various lanes that do not connected to each other at the crossing based on driving trajectory data of a vehicles that passing through the crossing within a detection time, and generating the virtual lane at the crossing for two disconnected lanes whose virtual connecting probability is greater than a threshold, and annotating the virtual lane on a map. In this process, the virtual lane at the crossing can be automatically generated according to the driving trajectory data of the vehicles passing through the crossing, and because the driving trajectory data of the vehicles passing through the crossing is real trajectory data, which is more in conformity with actual driving rules of the vehicles, and has a higher annotation accuracy.

Abstract: Technology described in this document can be embodied in a method that includes generating, using one or more processing devices of a vehicle operating in an environment, a discretized representation of the environment, including a plurality of cells. The cells are occupied by particles representing at least one of: an object, or a free space in the environment. Sensor data indicative of a state of at least one particle is received, and an update to a time-varying particle density function associated with a location of the particle in the dynamic occupancy grid is calculated from the sensor data and one or more models. The method further includes generating a prediction of occupancy of at least one cell in the discretized representation based on the updated particle density function, and operating the vehicle, using a controller circuit of the vehicle, based at least in part on the prediction.

Abstract: A decision method, device, equipment in a lane changing process and storage medium are provided. The method includes: acquiring a first planned track of a driverless vehicle for travelling to a first lane and a second planned track of the driverless vehicle for travelling to a second lane within a preset time period, in a lane changing process of the driverless vehicle; predicting a predicted track of at least one obstacle within the preset time period according to a travelling state of the obstacle, wherein the obstacle is in a preset range around the driverless vehicle; and determining a travelling motion of the driverless vehicle according to the first planned track, the second planned track and the predicted track of the obstacle.

Abstract: Systems, devices, products, apparatuses, and/or methods for generating a driving path for an autonomous vehicle on a roadway by determining one or more prior probability distributions of one or more motion paths for one or more objects that have previously moved in a geographic location and/or for controlling travel of an autonomous vehicle on a roadway by predicting movement of a detected object according to one or more prior probability distributions of one or more motion paths for one or more objects that have previously moved in a geographic location.

Abstract: Systems, methods, tangible non-transitory computer-readable media, and devices associated with trajectory prediction are provided. For example, trajectory data and goal path data can be accessed. The trajectory data can be associated with an object's predicted trajectory. The predicted trajectory can include waypoints associated with waypoint position uncertainty distributions that can be based on an expectation maximization technique. The goal path data can be associated with a goal path and include locations the object is predicted to travel. Solution waypoints for the object can be determined based on application of optimization techniques to the waypoints and waypoint position uncertainty distributions. The optimization techniques can include operations to maximize the probability of each of the solution waypoints. Stitched trajectory data can be generated based on the solution waypoints. The stitched trajectory data can be associated with portions of the solution waypoints and the goal path.

Abstract: A system may receive information indicating a service area of a shared mobility service, calculate information indicating one or more of risk or revenue associated with the provision of shared mobility services in the service area, determine an adjusted service area, wherein the adjusted service area is associated with one or more of a reduction in risk or an increase in revenue, and transmit information indicating the adjusted service area to a device associated with the shared mobility service.

Abstract: A system 100 for autonomous vehicle operation can include: a low-level safety platform 130; and can optionally include and/or interface with any or all of: an autonomous agent 102, a sensor system, a computing system 120, a vehicle communication network 140, a vehicle control system 150, and/or any suitable components. The system functions to facilitate fallback planning and/or execution at the autonomous agent. Additionally or alternatively, the system can function to transition the autonomous agent between a primary (autonomous) operation mode and a fallback operation mode.

Abstract: A vehicle control system includes: an inputter that accepts an occupant's operation of a host vehicle; an autonomous driving controller that executes an autonomous driving mode for controlling steering and acceleration/deceleration of the host vehicle in a case where a predetermined operation is accepted by the inputter; and a mode controller that prohibits the autonomous driving mode from being started by the autonomous driving controller in a case where the host vehicle has reached a predetermined section including at least one of a divergence point, a merging point, and a destination.

Abstract: A vehicle control device includes a recognizer configured to recognize at least a surroundings situation and an in-vehicle situation of a vehicle, and a driving controller configured to perform at least one of speed control and steering control of the vehicle on the basis of a recognition result of the recognizer, the recognizer is configured to recognize an object to be loaded including at least any one of an occupant candidate present outside the vehicle and scheduled to board the vehicle and a baggage candidate present outside the vehicle and scheduled to be loaded into the vehicle, and to recognize that the object to be loaded has been loaded into the vehicle, and the driving controller is configured to permit start of travel of the vehicle in a case where the recognizer has recognized that all of the objects to be loaded have been loaded into the vehicle.

Abstract: In one embodiment, a lateral drifting error is determined based on at least a current location of an ADV. The lateral drifting error is segmented into a first drifting error and a second drifting error using a predetermined segmentation algorithm. A planning module plans a path or trajectory for a current driving cycle (e.g., planning cycle) to drive the ADV from the current location for a predetermined period of time. The planning module performs a first drifting error correction on the trajectory by modifying at least a starting point of the trajectory based on the first drifting error to generate a modified trajectory. A control module controls the ADV to drive according to the modified trajectory, including performing a second drifting error correction based on the second drifting error.

Abstract: The invention being presented here automates the functions of the driver of the garbage truck. It involves a waste collection truck designed to follow routes to collect waste using a human to pick up the bins with refuse or other material comprising a truck that includes a drive-by-wire kit, a database storing the collection routes, a mechanism for detecting the position of the human which collects the waste bins and empties them on the back of the truck and a control mechanism that follows the assigned route and speeds or slows down the truck as to minimize the distance that the human will need to walk to empty the trash in the back of the waste collection truck.

Abstract: Systems and methods for capturing fluid change history in a vehicle are provided. A method includes: detecting, by a computer device, a fill cap is removed from an inlet port of a fluid reservoir or a drain plug is removed from an outlet port of the fluid reservoir; activating, by the computer device and based on the detecting, one of a fill sensor and a drain sensor; obtaining, by the computer device, fluid characteristics data from one of the fill sensor and the drain sensor; determining, by the computer device, at least one fluid parameter based on the fluid characteristics data; and transmitting, by the computer device, the determined at least one fluid parameter to a remote system via a communication network that is external to the vehicle.

Abstract: A steering system for an agricultural vehicle that includes a steering wheel, a steering wheel sensor operably connected to the steering wheel, a drive selector having a forward position, a neutral position, and a reverse position, an actuating device configured for being operably connected to the steerable wheels of the agricultural vehicle, and an electronic control unit operably connected to the steering wheel sensor, the drive selector, and the actuating device. The electronic control unit is configured for selectively preventing a rotation of the steering wheel from steering the steerable wheels in the neutral position of the drive selector.

Abstract: A system includes a processor configured to display a traveled-portion of a route, responsive to a request, the traveled-portion representing an entire route traveled since the request was received, including scaling the display so that an entire route is included in a window of fixed size, regardless of how large a distance the displayed traveled-portion represents.

Abstract: Techniques and methods for determining regions. For instance, a vehicle may determine a trajectory of the vehicle and a trajectory of an agent, such as a pedestrian. The vehicle may then determine one or more contextual factors. In some examples, the one or more contextual factors are associated with a location of the agent with respect to a crosswalk, a location of the vehicle with respect to the crosswalk, a state of the crosswalk, and/or the like. The vehicle may then determine the region using the trajectory of the vehicle, the trajectory of the agent, and the one or more contextual factors. Additionally, using a time buffer value and a distance buffer value associated with the region, the vehicle may determine whether to yield to the agent within the region.

Abstract: A system for dynamic policy curation includes a computing system and interfaces with an autonomous agent. A method for dynamic policy curation includes collecting a set of inputs; processing the set of inputs; and determining a set of available policies based on processing the set of inputs. Additionally or alternatively, the method can include any or all of: selecting a policy; implementing a policy; and/or any other suitable processes.

Abstract: A vehicle control device that controls a vehicle configured to transport a passenger to a destination by autonomous traveling, the vehicle control device includes a vehicle controller configured to control operation of the vehicle, an image display configured to display an image of a periphery of the destination to the passenger via an output device, and a getting-off position detector configured to detect a desired getting-off position designated by the passenger via an input device. The image shows a region where stopping is prohibited, and the vehicle controller is configured to cause the vehicle to stop at the desired getting-off position.

Abstract: Techniques for operation of a vehicle using machine learning with motion planning include storing, using one or more processors of a vehicle located within an environment, a plurality of constraints for operating the vehicle within the environment. One or more sensors of the vehicle receive sensor data describing the environment. The one or more processors extract a feature vector from the stored plurality of constraints and the received sensor data. The feature vector includes a first feature describing an object located within the environment. A machine learning circuit of the vehicle is used to generate a first motion segment based on the feature vector. A number of violations of the stored plurality of constraints is below a threshold. The one or more processors operate the vehicle in accordance with the generated first motion segment.

Abstract: A computing system that analyzes the network effects of avoidance areas on autonomous vehicle routing is described herein. The computing system includes a data store that comprises a set of avoidance areas through which the autonomous vehicle is prohibited from being routed. A grouping system identifies groups of avoidance areas. A graph construction system constructs a graph representation of the avoidance area groups. A ranking algorithm is evaluated over the graph representation to generate a ranking of the avoidance area groups by relative impact on routing metrics for routes through an operational area of the autonomous vehicle. A mapping vehicle can be dispatched to resolve avoidance areas in avoidance area groups indicating in the ranking as having a greater impact on routing metrics than other avoidance area groups.

Abstract: A vehicle control system includes: an information acquiring unit that acquires an index value relating to easiness of merging which is evaluated on the basis of at least one of vehicle information relating to an operation of another vehicle that has advanced from a merging lane to a main lane before a subject vehicle and external system information acquired near a merging point using the other vehicle in a case in which the subject vehicle is running in the merging lane merging into the main lane; and an automated driving control unit that executes automated driving of the subject vehicle on the basis of the index value acquired by the information acquiring unit.