Engineering Vehicle Safety Monitoring System

Engineering Vehicle Safety Monitoring

Project Background: Engineering vehicles are large cargo transportation machines. Due to their high mobility, significant hazards, small field of vision, large blind spots, and issues like unlicensed or non-standard driving, speeding, and overloading, safety accidents occur frequently.

Four major pain points: low management efficiency, high maintenance costs, unexpected downtime risks, and uneven vehicle utilization.

Solution: Develop an AI-based visual safety monitoring system for engineering vehicle cargo transportation scenarios. The system includes facial recognition for certified personnel, DMS driver behavior monitoring, BSD or AVM panoramic blind spot detection for pedestrians and obstacles, vehicle condition monitoring and forecasting, and integrated vehicle control for an active safety solution. This system helps drivers improve forklift transportation safety and efficiency in various complex working environments, ensuring the safety of people, vehicles, and goods.

AI Visual Safety Monitoring System

AI Visual Features:

Facial recognition, DMS driver monitoring, BSD pedestrian/vehicle blind spot detection, and 360° AVM panoramic view. The BSD early warning system uses pedestrian detection visual algorithms and provides active voice alerts, which can be continuously optimized and upgraded to meet different customer needs.

Vehicle Networking Features:

Supports vehicle CAN information collection and processing, GPS location, speed, load, working mileage, duration, and status information statistics.