Vehicle Occupant Intent Recognition for Route Command Priority
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Solution Overview
Problem
Existing autonomous vehicle systems struggle to differentiate and manage driving commands between a main occupant and non-main occupants, leading to potential misinterpretation of intentions due to multiple voices and facial expressions, which can result in unintended route changes or actions.
Innovation Solution
A vehicle control method and apparatus that determines a main occupant through voice and facial recognition, analyzes conversations and expressions to differentiate between occupants, and provides a determination result to either approve or reject non-main occupant commands, driving based on the main occupant's consent.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If voice recognition and face recognition systems process all occupant inputs equally, then user convenience is improved, but reliability deteriorates due to inability to differentiate main occupant intentions from non-main occupant expressions
Solution Approach 1:
The system segments occupant inputs by identifying and prioritizing the main occupant's voice and facial expressions separate from non-main occupants. The processor analyzes multiple input sources and assigns different weights or priorities based on occupant role, ensuring main occupant intentions are distinguished from passenger expressions while still allowing passenger inputs to be processed for convenience features.
Solution Approach 2:
The system introduces an intermediary layer (the processor) that mediates between raw voice/face recognition inputs and vehicle control actions. This intermediary analyzes the source and context of each input, determines occupant hierarchy, and filters or prioritizes commands accordingly before executing vehicle actions, thus resolving the conflict between accepting all inputs and following only authoritative commands.
2Ease of operation
If the system accepts voice commands from any occupant, then ease of operation is improved, but manufacturing precision deteriorates in terms of accurate route determination
Solution Approach 1:
The system segments command sources by identifying which occupant issued which command, then applies different processing rules based on occupant role. Main occupant commands related to route determination are given priority and final authority, while non-main occupant commands are either rejected for critical functions or require main occupant confirmation, ensuring route accuracy while maintaining accessibility.
Solution Approach 2:
The system applies different quality standards to different types of commands based on source occupant. For critical functions like route determination, only main occupant inputs are accepted without confirmation. For non-critical functions, the system may accept inputs from any occupant or require confirmation based on the specific context, thus maintaining high precision for critical decisions while preserving ease of operation for routine functions.
3Adaptability or versatility
If the system processes facial expressions and conversations of all occupants, then adaptability is improved, but device complexity increases due to multiple recognition systems
Solution Approach 1:
The system uses a single processor that performs multiple functions: voice recognition, face recognition, occupant identification, intent analysis, and command prioritization. Rather than having separate dedicated systems for each function, the processor handles all recognition and decision-making tasks, reducing overall system complexity while maintaining the ability to process multiple types of occupant inputs flexibly.
Solution Approach 2:
The system merges voice recognition and face recognition processing into a unified framework where the processor analyzes both modalities together to determine occupant identity and intent. By combining these functions and processing them through a single decision-making architecture with main occupant identification, the system achieves high adaptability without proportionally increasing complexity, as the same processor handles all recognition tasks.
Data Source
AI summary
In an embodiment a method for controlling a vehicle using a vehicle control apparatus is disclosed. The method includes determining whether one or more occupants are in the vehicle and determining whether to change a route based on a conversation between a main occupant and a non-main occupant, a voice of the main occupant, or a facial expression of the main occupant. The method further includes providing a determination result to the main occupant or the non-main occupant, wherein the determination result is displayed or announced by voice, and driving the vehicle based on the determination result.