A system comprises an autonomous vehicle (AV) and a control device operably coupled with the AV. The control device detects a series of events within a threshold period of time, where a number of series of events in the series of events is above a threshold number. The series of events taken in the aggregate within the threshold period of time deviates from a normalcy mode. The normalcy mode comprises events that are expected to the encountered by the AV. The control device determines whether the series of events corresponds to a malicious event, where the malicious event indicates tampering with the AV. In response to determining that the series of events corresponds to the malicious event, the series of events are escalated to be addressed.

System, methods, and other embodiments described herein relate to addressing inconsistencies between a trajectory plan and a communication from an occupant of an autonomously operated vehicle. A method of resolving inconsistent communication includes obtaining a trajectory plan for the vehicle, detecting, using one or more internal sensors, body language of the occupant, analyzing sensor data from the one or more internal sensors to determine a verbal or non-verbal communication indication by the occupant, and detecting an inconsistency between the verbal or non-verbal communication indication and the trajectory plan and: 1) modifying the trajectory plan to form a modified trajectory plan aligned with the verbal or non-verbal communication indication, or 2) transmitting a notification to the occupant prompting the occupant to adjust the verbal or non-verbal communication indication.

A method for providing vehicle alerts includes receiving an ignition signal indicating a current status of an ignition of a vehicle and receiving a gear position signal indicating a current gear position of a transmission of the vehicle. The method also includes receiving a vehicle speed signal indicating a current vehicle speed of the vehicle and identifying a vehicle alert data file based on at least the ignition signal, the gear position signal, the vehicle speed signal. The method also includes retrieving the vehicle alert data file from a vehicle alert database and loading data associated with the vehicle alert data file into a buffer. The method also includes outputting contents of the buffer to at least one output device of the vehicle.

A vehicle includes an automated driving assistance system that controls maneuvering of the vehicle under certain conditions. When the vehicle comes to a stop, the driving assistance system dynamically selects a threshold stop time corresponding to a duration of time that the vehicle can remain stopped before the driving assistance system will either detect a physical action from the user to resume automated driving assistance or time out and cease the driving assistance.

A vehicle control apparatus selects, as a first group, objects in a predetermined area from objects included in object information, selects, as a second group, an upper limit number of the objects from the first group in descending order of a priority value when the number of the objects of the first group is greater than the upper limit number, and executes a collision avoidance control when an index value satisfies a predetermined condition. The priority value represents a probability that the own vehicle collides with the object. The apparatus reduces the priority value of a particular object of the first group when the moving speed of the own vehicle is equal to or lower than a moving speed threshold. The particular object is an object which is deemed to have a moving speed lower than a moving speed of a four-wheel vehicle.

Example embodiments described in this disclosure are generally directed to systems and methods for preventing vehicular mishaps. In an example method, an object detector mounted on a building or a roadside fixture, detects an object in a detection coverage area of the object detector. The object is undetectable by a collision avoidance system of a vehicle. The object detector conveys information about the object to a supervisory computer. The information can include, for example, a location and/or a direction of travel of the object (if the object is moving). The supervisory computer evaluates the information and transmits an alert to the collision avoidance system of the vehicle, so as to prevent a collision between the vehicle and the object. In an example situation, where the object is a pedestrian, the supervisory computer may also transmit a warning alert to a personal communication device of the pedestrian.

Methods and systems for controlling navigation of an autonomous vehicle for traversing a drivable area are disclosed. The methods include receiving information relating to a drivable area that includes a plurality of polygons, identifying a plurality of logical edges that form a boundary of the drivable area, sequentially and repeatedly analyzing concavities of each the plurality of logical edges until identification of a first logical edge that has a concavity greater than a threshold, creating a first logical segment of the boundary of the drivable area. This segmentation may be repeated until each of the plurality of logical edges has been classified. The method may include creating and adding (to a map) a data representation of the drivable area that comprises an indication of the plurality of logical segments, and adding the data representation to a road network map comprising the drivable area.

Methods and systems for assessing, detecting, and responding to malfunctions involving components of autonomous vehicle and/or smart homes are described herein. Autonomous operation features and related components can be assessed using direct or indirect data regarding operation. Such assessment may be performed to determine the condition of components for salvage following a collision or other loss-event. To this end, the information regarding a plurality of components may be received. A component of the plurality of components may be identified for assessment. Assessment may including causing test signals to be sent to the identified component. In response to the test signal, one or more responses may be received. The received response may be compared to an expected response to determine whether the identified component is salvageable.

Multi-object tracking in autonomous vehicles uses both camera data and LiDAR data for training, but not LiDAR data at query time. Thus, no LiDAR sensor is on a piloted autonomous vehicle. Example systems and methods rely on camera 2D object detections alone, rather than 3D annotations. Example systems/methods utilize a single network that is given a camera image as input and can learn both object detection and dense depth in a multimodal regression setting, where the ground truth LiDAR data is used only at training time to compute depth regression loss. The network uses the camera image alone as input at test time (i.e., when deployed for piloting an autonomous vehicle) and can predict both object detections and dense depth of the scene. LiDAR is only used for data acquisition and is not required for drawing 3D annotations or for piloting the vehicle.

An information processing device includes a control unit. The control unit is configured to execute acquiring information about temperature inside or outside a vehicle, and generating a command to open or close a window included in the vehicle based on the information about the temperature inside or outside the vehicle.