论文标题
自动驾驶汽车的运动预测：评论
Motion Prediction on Self-driving Cars: A Review
论文作者
论文摘要
审查了自动驾驶汽车运动预测文献。运动预测是自动驾驶汽车和自动驾驶汽车中最具挑战性的任务。这些挑战已被讨论。后来，根据最新文献进行了审查，并讨论了当前的挑战。最先进的是由经典和物理方法，深度学习网络和强化学习组成。这篇评论中提出的研究方法和差距的普通和缺点。最后，将介绍围绕物体跟踪和运动的文献。结果，深度加强学习是解决自动驾驶汽车的最佳候选人。
The autonomous vehicle motion prediction literature is reviewed. Motion prediction is the most challenging task in autonomous vehicles and self-drive cars. These challenges have been discussed. Later on, the state-of-theart has reviewed based on the most recent literature and the current challenges are discussed. The state-of-the-art consists of classical and physical methods, deep learning networks, and reinforcement learning. prons and cons of the methods and gap of the research presented in this review. Finally, the literature surrounding object tracking and motion will be presented. As a result, deep reinforcement learning is the best candidate to tackle self-driving cars.
