The problem Rescue Buddy solves

1. Preventing accidents: Drowsy driving is a leading cause of accidents on the road. By detecting drowsiness in drivers and alerting them to take a break or pull over, drowsy detection software can help prevent accidents and save lives.

2. Improving road safety: By alerting drivers to their level of fatigue and encouraging them to take a break, drowsy detection software can help improve overall road safety and reduce the risk of accidents caused by drowsy driving.

3. Enhancing driver performance: Drowsy detection software can help drivers maintain their focus and attention on the road by alerting them to their level of fatigue and encouraging them to take a break or adjust their driving behavior.

4. Reducing the cost of accidents: Accidents caused by drowsy driving can be expensive, both in terms of property damage and medical costs. By reducing the number of accidents caused by drowsy driving, drowsy detection software can help reduce the overall cost of accidents.

5. Increasing driver awareness: Drowsy detection software can help increase driver awareness of the dangers of drowsy driving and encourage drivers to take steps to prevent it, such as getting enough sleep before driving or taking breaks during long trips.

Challenges we ran into

1. Accuracy: The accuracy of drowsy detection software is crucial for its effectiveness. Machine learning algorithms may need to be trained on large amounts of data to achieve high accuracy, and it can be difficult to ensure that the algorithms are detecting drowsiness accurately in all situations.

2. Data Collection and Labeling: Drowsy detection software relies on large amounts of data to be trained effectively. Collecting and labeling this data can be a time-consuming and expensive process, as it requires large amounts of driver data in different driving situations and environments.

3. Model selection: Choosing the right machine learning model for drowsy detection was challenging, as there are many different models to choose from. Each model has its own strengths and weaknesses, and selecting the right model requires careful consideration of the specific requirements of the drowsy detection application. Especially in the implementation of Twilio into the model.

4. Training and testing: Training and testing machine learning algorithms were time-consuming and computationally expensive. It was important to ensure that the algorithms were trained on a representative sample of the data and that the testing process was rigorous enough to ensure that the algorithm was accurate and effective.