FREE PROJECTS(driving decision strategy based on ML)

Driving decision strategy based on ML :

By taking into account both internal and external vehicle components—such as consumable conditions and RPM levels—the DDS is intended to maximize an autonomous vehicle's driving strategy. 

This is how it operates:

Data collection: A self-driving car produces vast volumes of driving-related data, which is cloud-stored. The performance of the car, sensor readings, and past driving habits are all included in this data.
Machine Learning Approach: To analyze this cloud-based data, the DDS makes use of machine learning techniques. It determines the optimal driving style for the driverless car using a genetic algorithm.

Internal and exterior elements: The DDS considers the internal status of the vehicle in addition to exterior elements, in contrast to traditional techniques that only consider external aspects. For instance, it takes into account RPM levels and consumable circumstances (such as gasoline levels).

Validation: The DDS is tested against different machine learning models, like RF (Random Forest) and MLP (Multi-Layer Perceptron), in order to determine how effective it is. In tests, the DDS performed 40% faster than the MLP in identifying changes in RPM, speed, control points, and course.