Over the past decades Machine Learning and Deep Learning algorithm played a vital part in the development of Autonomous Vehicle. It is indeed for the perception system to examine the environment around the vehicle and identify the objects such as pedestrian, vehicle and traffic signals, etc. Using this information, control system module can take necessary action to control the vehicle in terms of braking, speed, lane change or steering, etc. This paper focuses on the survey of machine learning algorithms and techniques applied in the design of autonomous driving system over a decade. Performance of each algorithm was analyzed in terms of prediction time and accuracy have been documented and compared.

在过去的几十年中，机器学习和深度学习算法在自动驾驶汽车的发展中起着至关重要的作用。感知系统确实需要检查车辆周围的环境并识别诸如行人，车辆和交通信号等物体。使用此信息，控制系统模块可以采取必要的措施来控制车辆的制动，速度，换道或转向等。本文重点研究了十年来用于自动驾驶系统设计的机器学习算法和技术。根据预测时间分析了每种算法的性能，并记录和比较了准确性。