Deploying Sensorless V2V Communication for Enhanced Driver Awareness: A C-V2X and GNSS-based System Utilizing OBD Ports for Broad Vehicle Integration
Session Number
ENGN 07
Advisor(s)
Matthew Walter, Toyota Technological Institute at Chicago
Discipline
Engineering
Start Date
17-4-2024 11:05 AM
End Date
17-4-2024 11:20 AM
Abstract
This paper presents a Vehicle-to-Vehicle (V2V) communication system designed to enhance road safety by leveraging Cellular Vehicle-to-Everything technology and the Global Navigation Satellite System. Unlike Advanced Driver Assistance Systems (ADAS), which rely on external sensors and direct vehicle control for collision avoidance, this module focuses on boosting driver awareness through real-time auditory alerts. Utilizing the vehicle's onboard diagnostic port for data access, the system employs machine learning algorithms to analyze vehicular communication data. It identifies potential hazards based on historical collision data and movements preceding collisions, resulting in auditory warnings conveyed directly through the car’s sound system. Although ADAS may achieve a higher reduction in collision rates by intervening in vehicle control, the proposed V2V module aims to significantly reduce accidents by alerting drivers to imminent dangers, thereby enhancing safety with broad accessibility and ease of retrofitting into existing vehicles. The system's design emphasizes ease of integration and scalability, supported by a detailed theoretical framework, positioning it as a promising advancement in vehicular safety technology.
Deploying Sensorless V2V Communication for Enhanced Driver Awareness: A C-V2X and GNSS-based System Utilizing OBD Ports for Broad Vehicle Integration
This paper presents a Vehicle-to-Vehicle (V2V) communication system designed to enhance road safety by leveraging Cellular Vehicle-to-Everything technology and the Global Navigation Satellite System. Unlike Advanced Driver Assistance Systems (ADAS), which rely on external sensors and direct vehicle control for collision avoidance, this module focuses on boosting driver awareness through real-time auditory alerts. Utilizing the vehicle's onboard diagnostic port for data access, the system employs machine learning algorithms to analyze vehicular communication data. It identifies potential hazards based on historical collision data and movements preceding collisions, resulting in auditory warnings conveyed directly through the car’s sound system. Although ADAS may achieve a higher reduction in collision rates by intervening in vehicle control, the proposed V2V module aims to significantly reduce accidents by alerting drivers to imminent dangers, thereby enhancing safety with broad accessibility and ease of retrofitting into existing vehicles. The system's design emphasizes ease of integration and scalability, supported by a detailed theoretical framework, positioning it as a promising advancement in vehicular safety technology.