A method of operating an autonomous vehicle includes detecting, based on an input received from a sensor of an autonomous vehicle that is being navigated by an on-board computer system, an occurrence of a driving event, making a determination by the on-board computer system, upon the detecting the occurrence of the driving event, whether or how to alter the path planned by the on-board computer system according to a set of rules, and performing further navigation of the autonomous vehicle based on the determination until the driving event is resolved. The driving event may include a presence of an object in a shoulder area of the road. The driving event may include accumulation of more than a certain number of vehicles behind the autonomous vehicle. The driving event may include a slow vehicle ahead of the autonomous vehicle. The driving event may include a do-not-change-lane zone is within a threshold.

A control apparatus plans a movement route of a moving body based on a recognition result of an exterior environment of the moving body; corrects the planned movement route, based on a recognition result of an obstacle in the exterior environment of the moving body; and controls the movement of the moving body based on the corrected movement route. The recognition result of the obstacle includes a recognition result of a side portion of the obstacle with respect to the movement route of the moving body, and in the correction, the planned movement route is corrected based on a correction amount continuously obtained based on the recognition result of the obstacle associated with advancement of the moving body.

A vehicle is provided. The vehicle includes a plurality of sensors including a first sensor and a second sensor. The vehicle also includes a vehicle controller. The vehicle controller is programmed to (i) collect a first plurality of sensor information observed by the first sensor during operation of the vehicle; (ii) analyze the first plurality of sensor information to detect a gap along the vehicle's path of travel; (iii) compare one or more dimensions of the gap to one or more dimensions of the vehicle; (iv) receive a second plurality of sensor information from the second sensor; and (v) control the vehicle to travel through the gap based on the comparison of the one or more dimensions of the gap to the one or more dimensions of the vehicle and the second plurality of sensor information from the second sensor.

A safety system for a vehicle may include one or more processors configured to determine, based on a friction prediction model, one or more predictive friction coefficients between the ground and one or more tires of the ground vehicle using first ground condition data and second ground condition data. The first ground condition data represent conditions of the ground at or near the position of the ground vehicle, and the second ground condition data represent conditions of the ground in front of the ground vehicle with respect to a driving direction of the ground vehicle. The one or more processors are further configured to determine driving conditions of the ground vehicle using the determined one or more predictive friction coefficients.

Performance anomalies in autonomous vehicle can be difficult to identify, and the impact of such anomalies on systems within the autonomous vehicle may be difficult to understand. In examples, systems of the autonomous vehicle are modeled as nodes in a probabilistic graphical network. Probabilities of data generated at each of the nodes is determined. The probabilities are used to determine capabilities associated with higher level functions of the autonomous vehicle.

Among other things, we describe techniques for adjusting lateral clearance for a vehicle using a multi-dimensional envelope. A trajectory is generated for the vehicle. A multi-dimensional envelope is generated indicating a drivable region for the vehicle and containing the trajectory. One or more objects are identified located along or adjacent to the trajectory. At least one dimension of the generated multi-dimensional envelope is adjusted to adjust a lateral clearance between the vehicle and the identified one or more objects. A control module of the vehicle navigates the vehicle along the multi-dimensional envelope.

An autonomous vehicle (AV) includes features that allows the AV to comply with applicable regulations and statutes for performing safe driving operation. An example method includes detecting that a group of motorcycles is operating on a roadway on which the AV is located. The group of motorcycles are each located within a pre-determined distance away from one another. The method further includes determining an aggregate footprint area that surrounds respective locations of the group of motorcycles. The method further includes causing navigation of the autonomous vehicle that avoids penetration of the aggregate footprint area based on transmitting navigation instructions to one or more subsystems of the autonomous vehicle.

An example method includes detecting, via sensor data collected from sensors located on the AV, an upcoming object located on a roadway. The method further includes determining, from the sensor data, a relative distance and a relative direction of the upcoming object with respect to the autonomous vehicle. The method further includes mapping the upcoming object to an absolute location with respect to the roadway based on map data that describes upcoming topology of the roadway and a location of the autonomous vehicle. The method further includes associating the upcoming object with a lane of the roadway based on the absolute location mapped to the upcoming object and based on lane geometry data for the roadway. The method further includes operating the autonomous vehicle based on a relationship between the lane associated with the upcoming object and a current lane in which the autonomous vehicle is located.

An autonomous vehicle (AV) includes features that allows the AV to perform safe driving operations. An example method includes detecting that a motorcycle is operating on a roadway on which the autonomous vehicle is located. The method further includes classifying a behavior state of the motorcycle based on a location of the motorcycle relative to a split zone that extends between and into two adjacent lanes of the roadway. The behavior state indicates whether the motorcycle is lane splitting. The method further includes determining, based on the behavior state of the motorcycle, a lane permission parameter that controls whether a given trajectory for the autonomous vehicle can extend into one of the two adjacent lanes. The method further includes causing the autonomous vehicle to operate in accordance with a trajectory that satisfies the lane permission parameter based on transmitting instructions related to the trajectory to subsystems of the autonomous vehicle.

A computer includes a processor and a memory, the memory storing instructions executable by the processor to move a vehicle within a roadway lane according to an emergency lane procedure to form an emergency lane when a longitudinal speed of the vehicle is below a specified speed and end the emergency lane procedure when the longitudinal speed is at least a second specified speed.