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Occupant Classification and Smart Restraint System Design

30 Chapters
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01
Occupant classification and smart restraint system design
course scope, safety regulations, and system architecture overview.
Occupantclassification
02
Occupant sensing technologies
pressure bladder, capacitive, strain gauge, and weight-based seat sensors for classification.
Occupanttechnologies
03
Vision-based occupant detection
camera placement, IR illumination, and image preprocessing for in-cabin monitoring.
Vision-basedoccupant
04
Time-of-flight and radar sensing for occupant presence, position, and vital sign detection.
Time-of-flight and radar sensing for occupant presence, position, and vital sign detection.
Time-of-flightoccupant
05
Sensor fusion architectures for robust occupant classification under varying environmental conditions.
Sensor fusion architectures for robust occupant classification under varying environmental conditions.
architecturesoccupant
06
Feature extraction from occupant sensor data
weight distribution, pressure maps, and occupancy patterns.
extractionoccupant
07
Machine learning for occupant classification
supervised, unsupervised, and deep learning approaches.
occupantclassification
08
Occupant classification categories
adult, child, rear-facing child seat, empty seat, and out-of-position detection.
Occupantclassification
09
Real-time embedded implementation of classification algorithms on automotive-grade ECUs.
Real-time embedded implementation of classification algorithms on automotive-grade ECUs.
Real-timeembedded
10
Smart restraint system actuators
adaptive airbags, seatbelt pretensioners, and load limiters.
restraintactuators
11
Airbag deployment logic and suppression strategies based on occupant classification.
Airbag deployment logic and suppression strategies based on occupant classification.
deploymentsuppression
12
Seatbelt reminder and adaptive restraint control for different occupant sizes and positions.
Seatbelt reminder and adaptive restraint control for different occupant sizes and positions.
Seatbeltreminder
13
Occupant position and posture estimation using pressure and vision data for out-of-position detection.
Occupant position and posture estimation using pressure and vision data for out-of-position detection.
Occupantposition
14
Child seat detection and classification
ISO-Fix, weight-based, and RFID/Bluetooth approaches.
detectionclassification
15
Integration of occupant classification with vehicle crash sensors and event data recorders.
Integration of occupant classification with vehicle crash sensors and event data recorders.
Integrationoccupant
16
Functional safety (ISO 26262) and SOTIF considerations for occupant classification systems.
Functional safety (ISO 26262) and SOTIF considerations for occupant classification systems.
FunctionalISO
17
Cybersecurity and privacy challenges in camera-based occupant monitoring systems.
Cybersecurity and privacy challenges in camera-based occupant monitoring systems.
Cybersecuritychallenges
18
Calibration and validation of occupant classification systems using crash test dummies and human volunteers.
Calibration and validation of occupant classification systems using crash test dummies and human volunteers.
Calibrationvalidation
19
Regulatory requirements
FMVSS 208, Euro NCAP, and other global occupant safety standards.
Regulatoryrequirements
20
Case study
design and implementation of a pressure-based occupant classification system for a passenger car.
implementationpressure-based
21
Case study
vision-based child presence detection and smart airbag suppression in a production vehicle.
vision-basedpresence
22
Case study
sensor fusion for robust occupant classification in autonomous shuttle vehicles.
occupantclassification
23
Testing and validation methodologies
Hardware-in-the-Loop (HIL), software-in-the-loop, and vehicle-level tests.
validationmethodologies
24
Data collection, annotation, and dataset management for training occupant classification models.
Data collection, annotation, and dataset management for training occupant classification models.
collectionannotation
25
Edge computing and optimization techniques for deploying deep learning models on embedded platforms.
Edge computing and optimization techniques for deploying deep learning models on embedded platforms.
computingoptimization
26
Human factors and ergonomics in occupant classification
seating positions, body sizes, and clothing effects.
ergonomicsoccupant
27
Integration with vehicle networks
CAN, LIN, Automotive Ethernet, and diagnostic protocols.
Integrationnetworks
28
Smart restraint system design for electric and autonomous vehicles
new challenges and opportunities.
restraintelectric
29
Future trends
AI-driven personalization, biometric monitoring, and adaptive restraint systems.
AI-drivenpersonalization
30
Capstone project
design, simulate, and validate a complete occupant classification and smart restraint system for a given vehicle platform.
Capstonesimulate