This project implements an Automatic Emergency Braking (AEB) System, a safety feature designed to prevent or mitigate collisions in vehicles. The system utilizes sensor data and advanced algorithms to detect potential collisions and automatically apply the brakes if necessary. It aims to enhance vehicle safety by providing an additional layer of protection for drivers and passengers.
Advantages:
1.Collision Prevention:
The AEB system helps prevent collisions by detecting obstacles or vehicles in the vehicle's path and applying the brakes automatically.
2.Enhanced Safety:
By reducing the likelihood of collisions, the system enhances overall safety for both occupants of the vehicle and other road users.
3.Quick Response:
Utilizing advanced sensor technologies and algorithms, the system can react swiftly to potential collision scenarios, minimizing the risk of accidents.
4.Adaptive Functionality:
The system can adapt to various driving conditions and environments, providing reliable performance across different scenarios.
5.Reduced Impact:
In cases where collisions cannot be entirely avoided, the AEB system can still reduce the severity of impacts, potentially saving lives and minimizing property damage.
Technology Used:
1.Machine Learning:
Advanced machine learning algorithms are employed to analyze sensor data and accurately identify potential collision risks.
2.Embedded Systems:
The AEB system is designed to be implemented within the vehicle's onboard computer system, ensuring seamless integration and optimal performance.
3.Safety Standards:
The project adheres to established safety standards and guidelines to ensure the reliability and effectiveness of the AEB system.
Contribution:
Contributions to this project are welcome! Whether you're interested in improving algorithm efficiency, adding support for additional sensors, or enhancing system reliability, your contributions can help advance vehicle safety technology.
Note
To improves performance we have to use 9 epoch and 500 step_epoch it will approximatly give less than 100 loss and more than 97% accuracy
By using this firstly the model have 79000 loss and we reduces it to the 2700