Autonomous Vehicle Route Planning Using Reinforcement Learning

Abstract

The invention presents a method and system for autonomous vehicle route planning using reinforcement learning (RL). It leverages real-time data, such as traffic conditions, road hazards, and vehicle performance metrics, to dynamically optimize navigation routes. By employing advanced RL algorithms like Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO), the system continuously learns and adapts to changing environments, enhancing route efficiency, safety, and fuel economy. Additionally, a multi-agent framework allows for data sharing between vehicles, improving predictive accuracy and decision-making. This scalable solution integrates seamlessly with existing autonomous vehicle systems, offering a future-proof approach to intelligent navigation.

Specification

Description:The present invention relates to the field of autonomous vehicle navigation systems, specifically to a method and system for route planning using reinforcement learning techniques. The invention aims to enhance the efficiency, safety, and adaptability of autonomous vehicles by dynamically optimizing travel routes in real-time, taking into account various environmental factors such as traffic conditions, road hazards, and vehicle performance metrics.
BACKGROUND OF THE INVENTION
The following description of related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of prior art.

Autonomous vehicles have revolutionized transportation, providing a promising future for self-driving cars. , Claims:1. A method for autonomous vehicle route planning using reinforcement learning, comprising:
• Receiving real-time environmental data including traffic density, road conditions, and vehicle performance metrics;
• Utilizing a reinforcement learning agent to determine optimal routes based on the received data;
• Defining a reward function to evaluate the efficiency and safety of the selected route;
• Continuously updating the planned route in response to changes in the environment;
• Implementing the updated route plan through a vehicle control module.

2. The method of claim 1, wherein the reinforcement learning agent uses a Deep Q-Network (DQN) algorithm to evaluate potential routes and optimize the reward function.
3. The method of claim 1, wherein the reinforcement learning agent uses a Proximal Policy Optimization (PPO) algorithm to optimize route selection in real-time.
4. The method of claim 1, further comprising a multi-agent framework where multiple autonomous vehicles share road condition data to enhan

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