A system and method are provided and include a light source projector with a positional actuator mounted on a subject vehicle that projects a laser line on a roadway upon which the subject vehicle is traveling. A controller is in communication with a platoon vehicle traveling in front of or behind the subject vehicle in a platoon and controls the positional actuator to project the laser line on the roadway between the subject vehicle and the at least one platoon vehicle.

In some embodiments, a powered trailer is provided having a chassis, a fixation structure for fixing the chassis to a target vehicle to be pushed, a drive mechanism for applying motive force to the chassis, an energy source for powering the drive mechanism, and a controller for controlling the application of motive force to the chassis by the drive mechanism. The drive mechanism is actuated by the controller to push the target vehicle.

Aspects of the disclosure provide for displaying notifications on a display of an autonomous vehicle 100. In one instance, a distance from the vehicle to a destination of the vehicle or a passenger may be determined. When the distance is between a first distance and a second distance, a first notification 650 may be displayed on the display. The second distance may be less than the first distance. When the distance is less than the second distance, a second notification 660 may be displayed on the display. The second notification provides additional information not provided by the first notification.

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 detachable vehicular lighting device for identifying vehicles traveling in a caravan includes a housing that has a bottom that is substantially rectangular. The bottom has an outer perimeter and an opening that defines an inner perimeter. An outer casing is coupled to the outer perimeter and extends transversely from the bottom. An inner casing, coupled to the inner perimeter, defines a cavity positioned in the bottom of the housing. The bottom, the outer casing and the inner casing define an internal space. A power module and a plurality of lights are coupled to the housing and positioned in the internal space. The lights are operationally coupled to the power module. A switch is operationally coupled to the power module and the plurality of lights. A fastener, coupled to the bottom, is configured to couple the housing to the exterior surface of a vehicle.

Systems and methods implemented in algorithms, software, firmware, logic, or circuitry may be configured to process data and sensory input to determine whether an object external to an autonomous vehicle (e.g., another vehicle, a pedestrian, road debris, a bicyclist, etc.) may be a potential collision threat to the autonomous vehicle. The autonomous vehicle may be configured to implement active safety measures to avoid the potential collision and/or mitigate the impact of an actual collision to passengers in the autonomous vehicle and/or to the autonomous vehicle itself. Interior safety systems, exterior safety systems, a drive system or some combination of those systems may be activated to implement active safety measures in the autonomous vehicle.

A system, an apparatus or a process may be configured to implement an application that applies artificial intelligence and/or machine-learning techniques to predict an optimal course of action (or a subset of courses of action) for an autonomous vehicle system (e.g., one or more of a planner of an autonomous vehicle, a simulator, or a teleoperator) to undertake based on suboptimal autonomous vehicle performance and/or changes in detected sensor data (e.g., new buildings, landmarks, potholes, etc.). The application may determine a subset of trajectories based on a number of decisions and interactions when resolving an anomaly due to an event or condition. The application may use aggregated sensor data from multiple autonomous vehicles to assist in identifying events or conditions that might affect travel (e.g., using semantic scene classification). An optimal subset of trajectories may be formed based on recommendations responsive to semantic changes (e.g., road construction).

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 an autonomous vehicle fleet as a service. In particular, a method may include receiving data associated with a sensor measurement of a perceived object, determining a label associated with the perceived object based on an initial calibration, retrieving log file data associated with the label, determining a calibration parameter associated with the sensor measurement based on the retrieved log file data, and storing the calibration parameter in association with a sensor associated with the sensor measurement. Sensors may be calibrated on the fly while the autonomous vehicle is in operation using one or more other sensors and/or fused data from multiple types of sensors.

A system, an apparatus or a process may be configured to implement an application that applies artificial intelligence and/or machine-learning techniques to predict an optimal course of action (or a subset of courses of action) for an autonomous vehicle system (e.g., one or more of a planner of an autonomous vehicle, a simulator, or a teleoperator) to undertake based on suboptimal autonomous vehicle performance and/or changes in detected sensor data (e.g., new buildings, landmarks, potholes, etc.). The application may determine a subset of trajectories based on a number of decisions and interactions when resolving an anomaly due to an event or condition. The application may use aggregated sensor data from multiple autonomous vehicles to assist in identifying events or conditions that might affect travel (e.g., using semantic scene classification). An optimal subset of trajectories may be formed based on recommendations responsive to semantic changes (e.g., road construction).

Systems and methods implemented in algorithms, software, firmware, logic, or circuitry may be configured to process data and sensory input to determine whether an object external to an autonomous vehicle (e.g., another vehicle, a pedestrian, road debris, a bicyclist, etc.) may be a potential collision threat to the autonomous vehicle. The autonomous vehicle may be configured to implement active safety measures to avoid the potential collision and/or mitigate the impact of an actual collision to passengers in the autonomous vehicle and/or to the autonomous vehicle itself. Interior safety systems, exterior safety systems, a drive system or some combination of those systems may be activated to implement active safety measures in the autonomous vehicle.