Functional module data flow and status in the system architecture diagram

• Ego-motion: describes the motion of the ego vehicle relative to the world coordinate system.

--Provide information reflecting the dynamic condition of the autonomous vehicle to determine maneuverability

--Provides information about the current motion of the autonomous vehicle

-- Considered together with the target trajectory, share the autonomous vehicle pose information to generate actuator requests

--Attitude over time, i.e. translational and rotational velocities and accelerations

• Actuator Requests: Send control inputs to the autonomous vehicle’s brake, steering, and acceleration actuators.

• Actuator Feedback: Provides feedback signals from vehicle actuators.

--Vehicle motion constraints should be exposed to the rest of the system as this feedback is necessary

--Can interface with various external execution modules, this feedback will be different in units, format, type, etc.

• Chassis sensor data: Vehicle internal sensors that provide motion-related information (e.g. wheel speed sensors, steering wheel angle, etc.).

• IMU data: Measured vehicle acceleration and rotation rate provided by the IMU.

• Compass data: True north direction provided by a magnetometer.

• GNSS data: Data from the Global Navigation Satellite System (GNSS). Includes georeferenced position estimates expressed as WGS84/GPS positions (uncertainty typically measured in metres).

--GNSS-based position information, possibly combined with information from correction services

-- Includes georeferenced position estimate. May already include ego motion estimate

• Environmental sensor data: Normative data from one or more environmental sensors.

-- May include camera data (visible light and/or infrared wavelengths), radar (time-of-flight measurement of gestures), sonar (audible and/or ultrasonic), or other sensing modalities

--Note: May include various combinations of sensor inputs

• Cabin sensor data: Data from one or more sensors within the cabin. A variety of sensing technologies can be used for passenger monitoring.

--Examples might include cameras (visible light and infrared wavelengths), radar (gesture/heartbeat detection), audio, and various other sensing mechanisms.

situation

• Mission Feedback: Feedback on the mission status, providing communication to the vehicle occupant/driver. An example is a system request for driver intervention. Feedback on the current mission is provided to the vehicle occupant via the HMI. This can include information such as progress towards route objectives, warning the driver to take over control, or similar information.

• Mission objective: This can reflect a complex mission statement where the goal is “reach the target destination” or, at lower automation levels, a simpler goal such as “stay centered in the current lane”. Mission objectives may change during the journey.

• Route Target: A description of the route the autonomous vehicle will take, including lanes if applicable, to achieve the mission goal. Describes the required lanes and turns at each intersection.

• Maneuvers: Provide a list of at least two actions, including a safe maneuver. Maneuvers are expressed as a new position on the map, and a target speed at that position, and describe high-level vehicle motion behavior (e.g., cruise, follow, change lanes, turn, or stop).

• Target trajectory: is the decomposition of the maneuver into a target trajectory (curved path) where changes in steering, braking, and acceleration are expressed along the trajectory path.

• Region of Interest: is a description of one or more regions in world space that perception should prioritize. This can describe regions of interest in world space where perception should provide superior performance (if possible). For example, this can allow configuring a sensor with non-uniform perception resolution so that specific roads/infrastructure of interest are "seen" at a higher resolution.

• Dynamic objects: Identify all moving or movable objects, such as vehicles, pedestrians or animals.

• Static Objects: Identify all non-movable objects or infrastructure. Static objects may still have changeable state associated with them. Examples include a road surface, curb, cone, road sign, traffic light, electronic sign. Or even an infrastructure element like a movable barrier (e.g. a toll booth).

• Dynamic Object Prediction: Predict the path of dynamic objects.

• Static Object Prediction: Predict changes in the state of static objects, such as the timing of traffic light changes.

• Scenario data: Provides a description of the current scenario that is used to determine if the automated driving system is operating within the operational design domain. An example is a note about a flooded road that the automated driving system has not been designed to handle.

• Pose: describes the current position and orientation of the autonomous vehicle relative to the map. Used with map information to enable or improve many behaviors of Advanced Driver Assistance Systems (ADAS).

• Perception capabilities: Give a description of the dynamic perception capabilities of the autonomous driving system (e.g., the perception range of a certain type of entity).

• System Integrity: Provides a report on the operating status of different components of the vehicle that are relevant to the safe operation of the automated driving system; derived from parts related to the hardware and software platforms.