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1 Functions 2 Reference Model 3 I/O Data
4 Functions of AI Modules 5 I/O Data of AI Module 6 AIW, AIMs, AIMs and JSON Metadata
7 Reference Software 8 Conformance Texting 9 Performance Assessment

1 Functions of Human-CAV Interaction Subsystem

The Human-CAV interaction (HCI) Subsystem has the function to recognise the human owner or renter, respond to humans’ commands and queries, converse with humans during the travel, exchange information with the Autonomous Motion Subsystem in response to humans’ requests, and communicate with HCIs on board other CAVs.

2 Reference Model of Human-CAV Interaction Subsystem

Figure 1 represents the Human-CAV Interaction (HCI) Reference Model.

Note that it is assumed that Natural Language Understanding produces a Refined Text that is either the refined Recognised Text or the Input Text, depending on which one is active. Meaning is always computed based on the available text – Refined or Input. Personal Status Extraction is unaware of the decisions made by Natural Language Understanding.

Figure 1 – Human-CAV Interaction Reference Model

A group of humans approaches the CAV outside the CAV or is sitting inside the CAV:

  1. Audio-Visual Scene Description produces:
    1. Speech Scene Descriptors in the form of Speech Objects corresponding to each speaking human in the Environment (outside or inside the CAV)..
    2. Visual Scene Descriptors in the form of Descriptors of Faces and Bodies.
    3. All non-Speech Objects are removed from or signalled in the Audio Scene.
  2. Automatic Speech Recognition recognises the speech of each human and produces Recognised Text supporting multiple Speech Objects as input properly identified by the Spatial Attitude.
  3. Visual Object Identification produces Instance IDs of Visual Objects indicated by humans.
  4. Natural Language Understanding produces Refined Text and extracts Meaning from the Recognised Text of each Input Speech using the spatial information of Visual Object Identifiers.
  5. Speaker Identity Recognition and Face Identity Recognition identifies the humans the HCI is interacting with. If the Face Identity Recognition AIM provides Face IDs corresponding to the Speaker IDs, the Entity Dialogue Processing AIM can correctly associate the Speaker IDs (and the corresponding Text) with the Face IDs.
  6. Personal Status Extraction extracts the Personal Status of the humans.
  7. Entity Dialogue Processing
    1. Communicates with the Autonomous Motion Subsystem of the Ego CAV to request to:
      1. Move the CAV to a destination.
      2. Views the Full Environment Descriptors for the passengers’ benefit.
      3. Be informed about CAV’s situation.
      4. Receive relevant information for passengers.
    2. Communicates with the Autonomous Motion Subsystems of Remote CAVs.
    3. Produces the Machine Text and Machine Personal Status.
  8. Personal Status Display produces the Machine Portable Avatar conveying Machine Speech, Machine Personal Status, and any other information that may be relevant for the the Audio-Visual Rendering AIM .
  9. Audio-Visual Scene Rendering renders Audio, and Visual information using Machine Portable Avatar or the Autonomous Motion Subsystem’s Full Environment Descriptors based on the Point of View provided by the human.

The HCI interacts with the humans in the cabin in several ways:

  1. By responding to commands/queries from one or more humans at the same time, e.g.:
    • Commands to go to a waypoint, park at a place, etc.
    • Commands with an effect in the cabin, e.g., turn off air conditioning, turn on the radio, call a person, open window or door, search for information etc.
  2. By conversing with and responding to questions from one or more humans at the same time about travel-related issues (in-depth domain-specific conversation), e.g.:
    • Humans request information, e.g., time to destination, route conditions, weather at destination, etc.
    • CAV offers alternatives to humans, e.g., long but safe way, short but likely to have interruptions.
    • Humans ask questions about objects in the cabin.
  3. By following the conversation on travel matters held by humans in the cabin if 1) the passengers allow the HCI to do so, and 2) the processing is carried out inside the CAV.

The HCI interacts with the Ego-AMS and with Remote HCIs exchanging context-based messages.

3 I/O Data of Human-CAV Interaction

Table 1 gives the input/output data of Human-CAV Interaction. I/O Data to/from Remote HCI and Ego AMS are not part of this Technical Specification.

Table 1 – I/O data of Human-CAV Interaction

Input data From Comment
Input Audio Environment, Passenger Cabin User authentication, command/interaction with HCI, etc.
Input Text User Text complementing/replacing User input
Input Visual Environment, Passenger Cabin Environment perception, User authentication, command/interaction with HCI, etc.
AMS-HCI Message AMS Subsystem AMS response to HCI request.
Ego-Remote HCI Message Remote HCI Remote HCI to Ego HCI.
Output data To Comment
Output Text Cabin Passengers HCI’s avatar Text.
Output Speech Cabin Passengers HCT’s avatar Speech.
Output Audio Cabin Passengers HCI’s avatar or FED Audio.
Output Visual Cabin Passengers HCI’s avatar or FED Visual.
AMS-HCI Message AMS Subsystem HCI request to AMS, e.g., Route or Point of View.
Ego-Remote HCI Message Remote HCI Ego HCI to Remote HCI.

4 Functions of AI Modules of Human-CAV Interaction

Table 2 gives the functions of all Human-CAV Interaction AIMs.

Table 2 – Functions of Human-CAV Interaction’s AI Modules

AIM Function
Audio-Visual Scene Description 1. Receives Audio and Visual Objects from the appropriate Devices.
2. Produces Audio-Visual Scene Descriptors.
Automatic Speech Recognition 1. Receives Speech Objects.
2. Produces Recognised Text.
Visual Object Identification 1. Receives Visual Scenes Descriptors.
2. Provides Instance ID of indicated Visual Object.
Natural Language Understanding 1. Receives Recognised Text.
2. Uses context information (e.g., Instance ID of object).
3. Produces Natural Language Understanding Text (using Refined or Input) and Meaning.
Speaker Identity Recognition 1. Receives Speech Object of a human and Speech Scene Geometry.
2. Produces Speaker ID.
Personal Status Extraction 1. Receives Speech Object, Meaning, Face Descriptors and Body Descriptors of a human with a Participant ID.
2. Produces the human’s Personal Status.
Face Identity Recognition 1. Receives Face Object of a human and Visual Scene Geometry.
2. Produces Face ID.
Entity Dialogue Processing 1. Receives Speaker ID, Face ID, AV Scene Descriptors, Meaning, Natural Language Understanding Text , Visual Object ID, and Personal Status. Moreover it receives AMS-HCI Messages and Ego-Remote HCI Messages.
2. Produces Machine (HCI) Text Object and Personal Status. Moreover it produces AMS-HCI Messages and Ego-Remote HCI Messages.
Personal Status Display 1. Receives Machine Text Object and Machine Personal Status.
2. Produces Machine’s Portable Avatar.
Audio-Visual Scene Rendering 1. Receives AV Scene Descriptors, Portable Avatar, and View Selector.
2. Produces Output Speech, Output Audio, and Output Visual.

5 I/O Data of AI Modules of Human-CAV Interaction

Table 3 gives the AI Modules of the Human-CAV Interaction depicted in Figure 3.

Table 3 – AI Modules of Human-CAV Interaction AIW

AIM Input Output
Audio-Visual Scene Description – Input Audio
– Input Visual		 – AV Scene Descriptors
Automatic Speech Recognition – Speech Object – Recognised Text
Visual Object Identification – AV Scene Descriptors
– Visual Objects		 – Visual Object Instance ID
Natural Language Understanding – Recognised Text
– AV Scene Descriptors
– Visual Object Instance ID
– Input Text		 – Natural Language Understanding Text
– Meaning
Speaker Identity Recognition – Speech Object
– Speech Scene Geometry		 – Speaker ID
Personal Status Extraction – Meaning
– Input Speech
– Face Descriptors
– Body Descriptors		 – Personal Status
Face Identity Recognition – Face Object
– Visual Scene Geometry		 – Face ID
Entity Dialogue Processing – Ego-Remote HCI Message
– AMS-HCI Message
– Speaker ID
– Meaning
– Natural Language Understanding Text
– Visual Object Instance ID
– Personal Status
– Face ID		 – Ego-Remote HCI Message
– AMS-HCI Message
– Machine Text
– Machine Personal Status
Personal Status Display – Machine Personal Status
– Machine Text		 – Machine Portable Avatar
Audio-Visual Scene Rendering – AV Scene Descriptors
– Machine Portable Avatar
– Point of View
– View Selector		 – Output Text
– Output Speech
– Output Audio
– Output Visual

6 AIW, AIMs and JSON Metadata

Table 4 provides the links to the AIW and AIM specifications and to the JSON syntaxes. AIMs/1 indicates that the column contains Composite AIMs and AIMs/2 indicates that the column contains Basic and Composite AIMs. AIMs/3 indicates the the column only contains Basic AIMs.

Table 4 – AIMs and JSON Metadata

AIW AIMs/1 AIMs/2 AIMs/3 Name JSON
MMC-HCI Human-CAV Interaction X
OSD-AVS Audio-Visual Scene Description X
CAE-ASD Audio Scene Description X
CAE-AAT Audio Analysis Transform X
CAE-ASL Audio Source Localisation X
CAE-ASE Audio Separation and Enhancement X
CAE-AST Audio Synthesis Transform X
CAE-ADM Audio Descriptors Multiplexing X
OSD-VSD Visual Scene Description X
MMC-ASR Automatic Speech Recognition X
OSD-AVA Audio-Visual Alignment X
OSD-VOI Visual Object Identification X
OSD-VDI Visual Direction Identification X
OSD-VOE Visual Object Extraction X
OSD-VII Visual Instance Identification X
MMC-NLU Natural Language Understanding X
MMC-SIR Speaker Identity Recognition X
MMC-PSE Personal Status Extraction X
MMC-ETD Entity Text Description X
MMC-ESD Entity Speech Description X
PAF-EFD Entity Face Description X
PAF-EBD Entity Body Description X
MMC-PTI PS-Text Interpretation X
MMC-PSI PS-Speech Interpretation X
PAF-PFI PS-Face Interpretation X
PAF-PGI PS-Gesture Interpretation X
MMC-PMX Personal Status Multiplexing X
MMC-EDP Entity Dialogue Processing X
PAF-FIR Face Identity Recognition X
PAF-PSD Personal Status Display X
MMC-TTS Text-to-Speech X
PAF-IFD Entity Face Description X
PAF-IBD Entity Body Description X
PAF-PMX Portable Avatar Multiplexing X
PAF-AVR Audio-Visual Scene Rendering X

7 Reference Software

8 Conformance Testing

Table 5 provides the Conformance Testing Method for MMC-HCI AIM.

If a schema contains references to other schemas, conformance of data for the primary schema implies that any data referencing a secondary schema shall also validate against the relevant schema, if present and conform with the Qualifier, if present.

Table 5 – Conformance Testing Method for MMC-HCI AIM

Receives Input Audio Shall validate against Audio Object Schema.
Audio Data shall conform with Audio Qualifier.
Input Text Shall validate against Text Object Schema.
Speech Data shall conform with Text Qualifier.
Input Visual Shall validate against Visual Object Schema.
Speech Data shall conform with Visual Qualifier.
AMS-HCI Message Shall validate against AMS-HCI Message Schema.
Ego-Remote HCI Message Shall validate against Ego-Remote HCI Message Schema.
Produces Output Text Shall validate against Text Object Schema.
Text Data shall conform with Text Qualifier.
Output Speech Shall validate against Speech Object Schema.
Speech Data shall conform with Speech Qualifier.
Output Audio Shall validate against Audio Object Schema.
Audio Data shall conform with Audio Qualifier.
Output Visual Shall validate against Visual Object Schema.
Visual Data shall conform with Visual Qualifier.
AMS-HCI Message Shall validate against AMS-HCI Message Schema.
Ego-Remote HCI Message Shall validate against Ego-Remote HCI Message Schema.

9 Performance Assessment

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