A vehicle control device includes a communication unit configured to receive location information of a vehicle, a sensing unit, a display unit, and at least one processor that is configured to determine, based on the location information received by the communication unit, that the vehicle has entered an area within a predetermined distance from an intersection at which the vehicle changes a travel direction according to a preset route information. The at least one processor is further configured to detect an object located around the intersection through the sensing unit, and to control the display unit to output a route to an entrance of the intersection based on information related to the object located around the intersection.

A system for controlling a movement of a vehicle from an initial state of the vehicle and a target state of the vehicle constructs a graph having multiple nodes defining states of the vehicle and including an initial node defining the initial state of the vehicle and a target node defining the target state of the vehicle and determines a path through the graph connecting the initial node with the target node. The system determines the graph using doubletree construction with an initial tree of nodes originating at the initial node and a target tree of nodes originating at the target node. The doubletree construction is configured to select an expandable node in the initial tree or the target tree based on a cost of the expandable node, and expand the graph by adding a child node connected to the expandable node with an edge defined by a collision free primitive motion, such that a cost of the child node is less than the cost of the expandable node.

A vehicle control device includes: an automatic driving control unit performing automatic driving control of automatically controlling one or both of acceleration/deceleration and steering of a subject vehicle such that the subject vehicle runs along a route to a destination; an avoidance target object detecting unit detecting an avoidance target object that is a target object to be avoided in the vicinity of the subject vehicle; and an avoidance control unit performing an avoidance operation by executing avoidance control of automatically controlling one or both of the acceleration/deceleration and the steering of the subject vehicle with a priority with respect to the automatic driving control in a case in which approach of the subject vehicle to the detected avoidance target object within a predetermined range is detected, and the automatic driving control unit ends the automatic driving control in a case in which the avoidance operation is executed.

A set of driving scenarios are determined for different types of vehicles. Each driving scenario corresponds to a specific movement of a particular type of autonomous vehicles. For each of the driving scenarios of each type of autonomous vehicles, a set of driving statistics is obtained, including driving parameters used to control and drive the vehicle, a driving condition at the point in time, and a sideslip caused by the driving parameters and the driving condition under the driving scenario. A driving scenario/sideslip mapping table or database is constructed. The scenario/sideslip mapping table includes a number of mapping entries. Each mapping entry maps a particular driving scenario to a sideslip that is calculated based on the driving statistics. The scenario/sideslip mapping table is utilized subsequently to predict the sideslip under the similar driving environment, such that the driving planning and control can be compensated.

A vehicle bears labels describing handling characteristics of the vehicle for the benefit of autonomous vehicles in proximity to the vehicle. The labels may be nonvisible, such as through use of UV or IR inks. The labels may be present such that they are visible regardless of view direction and may be affixed using a vehicle wrap applied to panels of the vehicle. Autonomous vehicles detect the labels and retrieve handling characteristics from a local database or a remote server. The autonomous vehicles are therefore relieved from the processing required to predict or infer the handling characteristics of the vehicle.

Systems and methods are provided for constructing, using, and updating the sparse map for autonomous vehicle navigation. In one implementation, a non-transitory computer-readable medium includes a sparse map for autonomous vehicle navigation along a road segment. The sparse map includes a polynomial representation of a target trajectory for the autonomous vehicle along the road segment and a plurality of predetermined landmarks associated with the road segment, wherein the plurality of predetermined landmarks are spaced apart by at least 50 meters. The sparse map has a data density of no more than 1 megabyte per kilometer.

A collision avoidance control method for a vehicle, through which a collision avoidance control apparatus controls a vehicle to avoid a collision. The collision avoidance control method includes: calculating a TTC (Time To Collision) between the vehicle and a rearward vehicle, when the rearward vehicle approaching the rear of the vehicle is sensed; determining whether the vehicle and the rearward vehicle are likely to collide with each other, by comparing the TTC to a preset reference TTC; and performing a collision avoidance function when it is determined that the vehicle and the rearward vehicle are likely to collide with each other, the collision avoidance function including one or more of a collision risk warning signal output function, a forward acceleration control function and a lane change control function.

Aspects of the disclosure relate to controlling a vehicle using a simplified model of an object. In one example, sensor data including a plurality of data points corresponding to surfaces of the object in the vehicle's environment may be received from one or more sensors of the vehicle. A first model may be determined using a subset of the plurality of data points. A set of secondary data points may be identified from the plurality of data points using a point on the vehicle. The set of secondary data points may be filtered from the subset of the plurality data points to determine a second model, wherein the second model is a simplified version of the first model. The vehicle may be controlled in an autonomous driving mode based on the second model.

In various examples, training sensor data generated by one or more sensors of autonomous machines may be localized to high definition (HD) map data to augment and/or generate ground truth datae.g., automatically, in embodiments. The ground truth data may be associated with the training sensor data for training one or more deep neural networks (DNNs) to compute outputs corresponding to autonomous machine operationssuch as object or feature detection, road feature detection and classification, wait condition identification and classification, etc. As a result, the HD map data may be leveraged during training such that the DNNsin deploymentmay aid autonomous machines in navigating environments safely without relying on HD map data to do so.

This application discloses a computing system to implement sensor event detection and fusion system in an assisted or automated driving system of a vehicle. The computing system can monitor an environmental model to identify spatial locations in the environmental model populated with temporally-aligned measurement data. The computing system can analyze, on a per-sensor basis, the temporally-aligned measurement data at the spatial locations in the environmental model to detect one or more sensor measurement events. The computing system can utilize the sensor measurement events to identify at least one detection event indicative of an object proximate to the vehicle. The computing system can combine the detection event with at least one of another detection event, a sensor measurement event, or other measurement data to generate a fused detection event. A control system for the vehicle can control operation of the vehicle based, at least in part, on the detection event.