An autonomous vehicle (AV) includes features that allows the AV to comply with applicable regulations and statues for performing safe driving operation. An example method for operating the AV includes determining a trajectory related information of a vehicle operating on a roadway on which the AV is operating; receiving sensor data of a first area that includes the vehicle; determining an additional trajectory related information for the AV by comparing the trajectory related information of the vehicle to a current trajectory related information of the AV, wherein the additional trajectory related information is based on a category to which the vehicle belongs, and wherein the additional trajectory related information allows the AV to maintain at least a distance between the AV and the vehicle; and causing the AV to operate in accordance with the additional trajectory related information.

A vehicle-based safety intervention system receives sensor data collected or generated by an onboard computing system of a vehicle. The sensor data is divided into a plurality of blocks, each of the blocks having a duration. A driver behavioral model is applied to one or more of the plurality of blocks to generate one or more driver behavioral parameters. A trend of the one or more driver behavioral parameters is extracted from the plurality of blocks. Based on the extracted trend, it is determined that a driver's performance when operating the vehicle is unsatisfactory or will be unsatisfactory in the future. A vehicle-based intervention is generated based on the determination that the driver's performance is unsatisfactory or will be unsatisfactory in the future.

According to an embodiment, a mobile object control device includes a recognizer configured to recognize a surrounding situation of a mobile object on the basis of an output of an external sensor and a marking recognizer configured to recognize markings for dividing an area through which the mobile object passes on the basis of the surrounding situation recognized by the recognizer. The marking recognizer extracts a prescribed area from the surrounding situation when it is determined that marking recognition accuracy has been lowered, extracts an edge within the extracted prescribed area, and recognizes the markings on the basis of an extraction result.

Methods of determining relevance of objects that a vehicle detected are disclosed. A system will receive a data log of a run of the vehicle. The data log includes perception data captured by vehicle sensors during the run. The system will identify an interaction time, along with a look-ahead lane based on a lane in which the vehicle traveled during the run. The system will define a region of interest (ROI) that includes a lane segment within the look-ahead lane. The system will identify, from the perception data, objects that the vehicle detected within the ROI during the run. For each object, the system will determine a detectability value by measuring an amount of the object that the vehicle detected. The system will create a subset with only objects having at least a threshold detectability value, and it will classify any such object as a priority relevant object.

In various examples, lanes may be grouped and a sign may be assigned to a lane in a group, then propagated to another lane in the group to associate semantic meaning corresponding to the sign with the lanes. The sign may be assigned to the most similar lane as quantified by a matching score subject to the lane meeting any hard constraints. Propagation of an assignment of the sign to a different lane may be based on lane attributes and/or sign attributes. Lane attributes may be evaluated and assignments of signs may occur for a lane as a whole, and/or for particular segments of a lane (e.g., of multiple segments perceived by the system). A sign may be a compound sign that is identified as individual signs, which are associated with one another. Attributes of the compound sign may provide semantic meaning used to operate a machine.

A system for maneuvering a vehicle has a detection system, a prediction system, and a vehicle control system. The detection system is configured to detect a nearby vehicle adjacent to the vehicle. The prediction system is configured to calculate a predicted trajectory of the nearby vehicle upon receiving a detection result from the detection system. The vehicle control system is configured to maneuver the vehicle based on the predicted trajectory upon receiving a control signal from the prediction system. The vehicle control system maneuvers the vehicle to keep a specified distance away from the nearby vehicle. A method for maneuvering a vehicle includes detecting a nearby vehicle adjacent to the vehicle, calculating a predicted trajectory of the nearby vehicle, and maneuvering the vehicle based on the predicted trajectory to keep a specified distance away from the nearby vehicle.

Categorizing driving behaviors of other road users includes maintaining a first history of first lateral-offset values of a road user with respect to a center line of a lane of a road; determining a first pattern based on the first history of the first lateral-offset values; determining a driving behavior of the road user based on the first pattern; and autonomously performing, by a host vehicle, a driving maneuver based on the driving behavior of the road user. The first history can be maintained for a predetermined period of time. An apparatus includes a processor that is configured to track a trajectory history of a road user; determine, based on the trajectory history, a driving behavior of the road user; and transmit a notification of the driving behavior.

Methods of determining relevance of objects that a vehicle's perception system detects are disclosed. A system on or in communication with the vehicle will identify a time horizon, and a look-ahead lane based on a lane in which the vehicle is currently traveling. The system defines a region of interest (ROI) that includes one or more lane segments within the look-ahead lane. The system identifies a first subset that includes objects located within the ROI, but not objects not located within the ROI. The system identifies a second subset that includes objects located within the ROI that may interact with the vehicle during the time horizon, but not excludes actors that may not interact with the vehicle during the time horizon. The system classifies any object that is in the first subset, the second subset or both subsets as a priority relevant object.

An autonomous vehicle control system is provided. The control system may include a plurality of cameras to acquire a plurality of images of an area in a vicinity of a vehicle; and at least one processing device configured to: recognize a curve to be navigated based on map data and vehicle position information; determine an initial target velocity for the vehicle based on at least one characteristic of the curve as reflected in the map data; adjust a velocity of the vehicle to the initial target velocity; determine, based on the plurality of images, observed characteristics of the curve; determine an updated target velocity based on the observed characteristics of the curve; and adjust the velocity of the vehicle to the updated target velocity.

In a method for ending shoulder driving, it is detected by means of a detection unit of a motor vehicle that a wheel is located on a shoulder. By means of a sensor unit, a driver's reaction is recorded, and by means of a computing unit, the driver's reaction is assigned to one of at least two intensity classes. By means of a control unit, an intervention in controlling the vehicle is undertaken counteracting the driver's reaction when the driver's reaction has been assigned to a first intensity class, and an intervention supporting the driver's reaction is undertaken when the driver's reaction has been assigned to a second intensity class.