Autonomous Vehicle Direction Control Using Driver Intent Detection

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Solution Overview

Problem

Existing autonomous driving systems require manual driver intervention to select vehicle operating modes, which can lead to human error and potential collisions due to inaccurate mode selection.

Innovation Solution

An automated vehicle operating mode selection system that uses machine learning and sensor data to anticipate the driver's intention and select the appropriate driving direction, considering vehicle type, surroundings, and confidence levels.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual driver intervention is used to select vehicle operating modes, then the system is easier to operate and understand, but human error occurs leading to inaccurate mode selection and potential collisions

Engineering Contradiction:

Improvemode selection accuracyVSAvoidmanual intervention requirement

Core Design Contradiction:

ReliabilityVSEase of operation

Solution Approach 1:

The system performs self-service by automatically selecting vehicle operating modes without requiring manual driver intervention. The automated mode selection system analyzes sensor data and environmental factors to determine appropriate modes, eliminating human error while maintaining operational safety through confidence level thresholds that ensure reliable automated decisions

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the mechanical system of manual driver input with an automated electronic system that processes sensor data and environmental information. This substitution uses machine learning models and sensor fusion to determine operating modes, replacing human judgment with algorithmic decision-making that improves accuracy while reducing reliance on manual operation

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If automated mode selection is implemented, then collision risk is reduced through accurate mode selection, but the device complexity increases due to machine learning models and sensor integration

Engineering Contradiction:

Improvecollision risk reductionVSAvoidsystem architecture

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The automated mode selection system is segmented into distinct functional modules: sensor data acquisition, environmental factor analysis, machine learning model processing, confidence level calculation, and mode selection execution. This segmentation manages complexity by organizing the system into manageable, independent components that can be developed and validated separately while working together to reduce collision risk

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces intermediary elements including confidence level thresholds and safety validation layers that mediate between the complex machine learning models and the final mode selection. These intermediaries simplify the decision-making process by filtering outputs from complex models and ensuring only reliable predictions are acted upon, thereby managing system complexity while maintaining high reliability

Inventive Principle:

Principle #24Intermediary (Mediator)

3Productivity

If the system waits for driver input to select operating modes, then the system operates conservatively with lower automation level, but this increases loss of time and reduces productivity

Engineering Contradiction:

Improvevehicle operation efficiencyVSAvoidmode selection delay

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system performs preliminary action by continuously analyzing sensor data and pre-calculating appropriate operating modes before driver input is required. The automated system maintains readiness by continuously processing environmental information and having mode selections prepared in advance, eliminating delays while maintaining conservative operation through confidence level verification

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent implements feedback mechanisms where the system continuously monitors sensor data, confidence levels, and operational outcomes to refine mode selections in real-time. This feedback loop enables the system to learn from past decisions and improve future mode selections, increasing productivity while maintaining safety through continuous validation and adjustment based on actual performance

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS12534092B2Automated adjustment of vehicle direction based on environment analysis

Publication Date: 2026.01.27 TESLA INC

AI summary

The present disclosure relates to systems and methods for managing vehicle operations utilized for autonomous driving. An example method includes obtaining a set of inputs corresponding to a set of operational information associated with a vehicle; responsive to determined trigger, processing the set of inputs to determine whether the vehicle is available for travel; processing the set of inputs to determine whether at least one occupant has indicated an intent for the vehicle to initiate travel; responsive to determined vehicle availability for travel and determined intent for the vehicle to initiate travel, identifying a set of vehicle operational parameters corresponding to a determined path of travel; and causing the initiation of the identified set of vehicle operational parameters.