Multi-Modal Attention Perception for Intelligent Vehicle Navigation Using Deep Reinforcement Learning

In this paper, we propose a new framework for collision-free intelligent vehicle navigation, aiming to successfully avoid obstacles using deep reinforcement learning. The navigation system separates perception and control and utilizes multimodal perception to achieve reliable online interaction with the surroundings. This allows for direct policy learning to generate flexible actions and avoid collisions. Our navigation system establishes a connection between the virtual environment and the real world, allowing learning policies in the virtual environment to be implemented in real-world environments through transfer learning. Our approach aims to integrate camera, Lidar, and Inertial Measurement Unit (IMU) data to construct a multimodal perception-based environment model, which is a state input for reinforcement learning. In this process, we utilize a series of cross-domain self-attention layers to enhance visual and Lidar perception, promoting significant improvements in perception. We also introduce the recurrent deduction to facilitate global decision-making based on local perception. Additionally, we introduce the Self-Assessment Gradient model into the reinforcement learning process to further optimize the learning policy. The experimental results demonstrate the proposed approach reduces the disparity between the virtual environment and the real world, highlighting its superiority over other state-of-the-art methods. Our project page is publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/CV4RA/MMAP-DRL-Nav</uri>.