Driver Assistance Systems with Driver Drowsiness Detection Using Haar-Cascade Algorithm

The proposed work is developed to avoid accidents caused due to different parameters and aims to build an effective system. Design modifications are not required in vehicle, this work can be externally placed in vehicle so all old vehicles can be part of this system. Emphasizing the aspects that make commuting a laborious job and providing appropriate solutions to make the journey safe and reliable is crucial in improving the overall commuting experience. The proposed system covers two aspect - First aspect determine driver's drowsiness which will determine the sleeping and yawning state of the driver. The system automatically detects driver fatigue based on visual information and artificial intelligence. Identifying, tracking and analyzing both the driver's face and eyes and using softmax of neuron transfer functions measures PERCLOS (percentage of eyes closed).Second aspect includes adaptive cruise system which includes sensors. The sensor used in this system is not reliant on human visual perception, distance judgment, or directional awareness. Instead, it monitors the driving environment and identifies potential dangerous situations. It handles various tasks such as preventing collisions while reversing, maintaining a safe distance from the vehicle in front, avoiding collisions at intersections, avoiding obstacles on the road, and keeping the vehicle within its lane. To achieve real-time monitoring, the system utilizes the Raspberry Pi Camera. The methods proposed in this work, such as feature extraction and neural networks, help to overcome the drawbacks of the current approaches, increase the effectiveness of traffic sign detection, and decrease the number of accidents on the road.