Methods and systems for communicating between autonomous vehicles are described herein. Such communication may be performed for signaling, collision avoidance, path coordination, and/or autonomous control. Several communications from autonomous vehicles may be received at a computing device, where the autonomous vehicles are travelling within a threshold distance of each other. Each communication may include an indication of the next waypoint on a route for the respective vehicle. The computing device may analyze the communications to determine maneuvers for the autonomous vehicles so that each autonomous vehicle may navigate to the corresponding waypoint in the least amount of time or distance. The computing device also may cause each of the autonomous vehicles to move in accordance with the maneuvers for the respective vehicle.

Methods and systems for assessing, detecting, and responding to malfunctions involving components of autonomous vehicles and/or smart homes are described herein. Autonomous operation features and related components can be assessed using direct or indirect data regarding operation to determine the robustness of autonomous systems, including the use of virtual assessment of software components within a simulated environment. A server may retrieve one or more routines associated with autonomous operation. The server may also generate a set of test data associated with test conditions. The server may also execute an emulator that virtually simulates autonomous environment. The test data may be presented to the routines executing in the emulator to generate output data. The server may then analyze the output data to determine a quality metric.

Methods and systems for assessing, detecting, and responding to malfunctions involving components of autonomous vehicles and/or smart homes are described herein. A risk of malfunction and/or cyber-attack may be determined by collecting operating data from a plurality of autonomous vehicles and/or smart homes. The operating data may be analyzed to identify occurrences of a component malfunctioning. For each component, a risk associated with malfunctioning and/or cyber-attack may be determined based upon the identified occurrences. Based on the risks, at least one result associated with the malfunction and/or cyber-attack may be determined. A component profile may be generated based upon the determined risk and/or the impact of the determined results.

Various embodiments relate generally to autonomous vehicles and associated mechanical, electrical and electronic hardware, computer software and systems, and wired and wireless network communications to provide map data for autonomous vehicles. In particular, a method may include accessing subsets of multiple types of sensor data, aligning subsets of sensor data relative to a global coordinate system based on the multiple types of sensor data to form aligned sensor data, and generating datasets of three-dimensional map data. The method further includes detecting a change in data relative to at least two datasets of the three-dimensional map data and applying the change in data to form updated three-dimensional map data. The change in data may be representative of a state change of an environment at which the sensor data is sensed. The state change of the environment may be related to the presence or absences of an object located therein.

A pedestrian crosswalk through which a subject vehicle is expected to pass is specified as a target pedestrian crosswalk. Road configurations close to the target pedestrian crosswalk are detected. Traffic lines of moving objects crossing the target pedestrian crosswalk are estimated on the basis of the road configurations. An area including the estimated traffic lines is set as a detection area of a detector detecting objects around the subject vehicle. The moving objects are detected by the detector in the detection area. Travel of the subject vehicle is controlled on the basis of a detection result of the detector.

This application provides a collision detection method and related apparatus. An image taken by a photographing unit may be used to predict whether a collision with a to-be-detected target will occur. In a current collision prediction method, a type of the to-be-detected target needs to be determined first based on the image taken by the photographing unit, which requires consuming of a large amount of computing power. In the collision prediction method provided in this application, a change trend of a distance between the to-be-detected target and a vehicle in which the apparatus is located may be determined based on the distances between the to-be-detected target and the vehicle at different moments, to predict a collision between the to-be-detected target and the vehicle. This method can improve efficiency in collision prediction and reduce energy consumption in predicting collision.

Autonomous vehicle operation with explicit occlusion reasoning may include traversing, by a vehicle, a vehicle trans-network. Traversing the vehicle transportation network can include receiving, from a sensor of the vehicle, sensor data for a portion of a vehicle operational environment, determining, using the sensor data, a visibility grid comprising coordinates forming an unobserved region within a defined distance from the vehicle, computing a probability of a presence of an external object within the unobserved region by comparing the visibility grid to a map (e.g., a high-definition map), and traversing a portion of the vehicle transportation network using the probability. An apparatus and a vehicle are also described.

A method for warning a driver of a vehicle (1), in particular a truck, in turn maneuvers includes the following steps: generating (S1) an adaptive monitoring area (2) for the vehicle (1) based on at least a maximum lateral acceleration (4) of the vehicle (1) at a current longitudinal velocity (6) of the vehicle (1); identifying (S2) a vulnerable road user (VRU) (8) within the adaptive monitoring area (2); determining (S3, S4) a driver's intention to turn (40) the vehicle (1); determining S5) whether there is a collision risk between the vehicle (1) and the VRU (8); and outputting a warning signal (SW) based on the determined collision risk.

A driving assistance system for a vehicle includes a control operable to control the vehicle in an autonomous or semi-autonomous mode. When operating in the autonomous mode, driving of the vehicle is controlled by the control without human intervention and, when operating in the semi-autonomous mode, an occupant of the vehicle at least partially drives the vehicle. The control learns a driving style of a particular occupant when the vehicle is being driven by that particular occupant and when the control is operating in the semi-autonomous mode. The control, when operating in the autonomous mode, controls the vehicle in accordance with the learned driving style of the particular occupant of the vehicle. Responsive to a determination of a characteristic of an occupant in the vehicle, the control, when operating in the autonomous, may adjust control of the vehicle irrespective of the learned driving style of the particular occupant.

A vehicle safety support apparatus includes: a driver monitoring sensor configured to monitor a driver; an external environment monitoring sensor configured to monitor an external environment of a vehicle; and at least one processor configured to: determine whether the vehicle is in an immediate hazard situation based on data acquired from the driver monitoring sensor and the external environment monitoring sensor; determine, in response to determining that the vehicle is in the immediate hazard situation, whether to perform a recovery maneuver or a rescue maneuver based on the data acquired from the driver monitoring sensor and the external environment monitoring sensor to get out of the immediate hazard situation; and perform, in response to determining to perform the rescue maneuver, autonomous driving to move the vehicle to a safe area by taking over a driving control from the driver.