Tag: Lateral

Price: $60.00
(as of Dec 17,2024 19:21:03 UTC – Details)

Publisher ‏ : ‎ LAP LAMBERT Academic Publishing (January 23, 2020)
Language ‏ : ‎ English
Paperback ‏ : ‎ 92 pages
ISBN-10 ‏ : ‎ 6200531536
ISBN-13 ‏ : ‎ 978-6200531537
Item Weight ‏ : ‎ 5.1 ounces
Dimensions ‏ : ‎ 5.91 x 0.21 x 8.66 inches

Lateral Control of Autonomous Vehicle Based on Reinforcement Learning: Design and Applications

In recent years, autonomous vehicles have gained significant attention due to their potential to revolutionize the transportation industry. One of the key challenges in developing autonomous vehicles is designing robust control algorithms that can safely navigate complex environments. In this post, we will discuss the use of reinforcement learning for lateral control of autonomous vehicles and its design and applications.

Reinforcement learning is a machine learning technique that enables an agent to learn optimal control policies by interacting with its environment. In the context of autonomous vehicles, reinforcement learning can be used to learn lateral control policies that enable the vehicle to maintain its desired trajectory while avoiding obstacles and other vehicles.

The design of a reinforcement learning-based lateral control system typically involves the following steps:

1. State Representation: The first step in designing a reinforcement learning-based lateral control system is to define the state space of the system. The state space typically includes information about the vehicle’s position, velocity, orientation, and other relevant variables.

2. Action Space: The next step is to define the action space, which consists of the possible control actions that the vehicle can take. In the case of lateral control, the action space typically consists of steering angles or lateral acceleration commands.

3. Reward Function: The reward function is used to provide feedback to the agent on its performance. The reward function typically penalizes deviations from the desired trajectory and rewards safe and efficient behavior.

4. Training: Once the state representation, action space, and reward function are defined, the agent can be trained using reinforcement learning algorithms such as deep Q-learning or policy gradients. During training, the agent interacts with a simulated environment and learns optimal control policies through trial and error.

The applications of reinforcement learning-based lateral control systems for autonomous vehicles are diverse and include lane keeping, lane changing, and obstacle avoidance. These systems can enable autonomous vehicles to navigate complex urban environments, highways, and intersections safely and efficiently.

In conclusion, the use of reinforcement learning for lateral control of autonomous vehicles shows great promise in enabling safe and efficient navigation in complex environments. By designing robust control algorithms and training them using reinforcement learning techniques, we can accelerate the development and deployment of autonomous vehicles and unlock their full potential in transforming the transportation industry.
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