Collision Avoidance/Mitigation by Machine Learning and Automatic Intervention

Smart overview of the Invention

A vehicle equipped with advanced machine learning and artificial intelligence can detect potential collisions with other vehicles or objects. By analyzing real-time data, the system can determine the best course of action—such as braking, swerving, or accelerating—to either avoid a collision or mitigate its impact if avoidance is not possible. The AI model is designed to learn from past incidents and driving behaviors, allowing it to predict the actions of surrounding vehicles and drivers.

Implementation and Communication

The AI algorithm operates on a processor within the vehicle, which may communicate with external systems like local access points or central supercomputers. This connectivity enhances the decision-making process by providing additional data and computational power. The goal is to implement rapid responses that can significantly reduce the likelihood of accidents and save lives.

Addressing Human Error in Driving

Traffic accidents are predominantly caused by human error, leading to millions of fatalities annually. Common scenarios include sudden traffic slowdowns where drivers may not react quickly enough, or side-encroachment incidents during lane changes. These situations highlight the need for automated systems that can respond faster than human reflexes, helping to prevent collisions or lessen their severity.

Limitations of Existing Systems

Current collision-avoidance systems often rely on simple distance sensors and may not adequately account for complex driving scenarios or individual driver capabilities. Such systems can inadvertently create dangerous situations by applying brakes too aggressively or taking control away from skilled drivers who could potentially avoid a collision themselves. There is a clear demand for more sophisticated solutions that adapt to various driving skills and conditions.

Advanced Collision Mitigation Strategies

The proposed technology includes methods for assessing imminent collisions through sensor data, calculating potential avoidance actions, and implementing those actions based on AI-driven algorithms. In cases where a collision cannot be avoided, the system will determine how to minimize damage effectively. This approach not only focuses on immediate collision avoidance but also addresses post-collision hazards, aiming to enhance overall road safety.