Autonomous vehicles include driverless, self-driving and robotic cars, and other platforms capable of sensing and interacting with its environment and navigating without human help. On the other hand, semiautonomous vehicles achieve partial realization of autonomy with human intervention, for example, in driver-assisted vehicles. Autonomous vehicles first interact with their surrounding using mounted sensors. Typically, visual sensors are used to acquire images, and computer vision techniques, signal processing, machine learning, and other techniques are applied to acquire, process, and extract information. The control subsystem interprets sensory information to identify appropriate navigation path to its destination and action plan to carry out tasks. Feedbacks are also elicited from the environment to improve upon its behavior. To increase sensing accuracy, autonomous vehicles are equipped with many sensors [light detection and ranging (LiDARs), infrared, sonar, inertial measurement units, etc.], as well as communication subsystem. Autonomous vehicles face several challenges such as unknown environments, blind spots (unseen views), non-line-of-sight scenarios, poor performance of sensors due to weather conditions, sensor errors, false alarms, limited energy, limited computational resources, algorithmic complexity, human–machine communications, size, and weight constraints. To tackle these problems, several algorithmic approaches have been implemented covering design of sensors, processing, control, and navigation. The review seeks to provide up-to-date information on the requirements, algorithms, and main challenges in the use of machine vision–based techniques for navigation and control in autonomous vehicles. An application using land-based vehicle as an Internet of Thing-enabled platform for pedestrian detection and tracking is also presented.

中文翻译：

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移动机器人系统中基于图像的识别和推理的算法和技术综述

自动驾驶汽车包括无人驾驶汽车、自动驾驶汽车和机器人汽车，以及其他能够感知环境并与环境交互并在没有人类帮助的情况下导航的平台。另一方面，半自动驾驶汽车通过人工干预实现了部分自动驾驶，例如在驾驶员辅助车辆中。自动驾驶汽车首先使用安装的传感器与周围环境进行交互。通常，视觉传感器用于获取图像，计算机视觉技术、信号处理、机器学习和其他技术用于获取、处理和提取信息。控制子系统解释感官信息以识别到达目的地的适当导航路径和执行任务的行动计划。还从环境中获得反馈以改进其行为。为了提高传感精度，自动驾驶汽车配备了许多传感器[光探测和测距（LiDAR）、红外、声纳、惯性测量单元等]，以及通信子系统。自动驾驶汽车面临着诸多挑战，例如未知环境、盲点（看不见的视野）、非视线场景、由于天气条件导致的传感器性能不佳、传感器错误、误报、能量有限、计算资源有限、算法复杂性、人机通信、尺寸和重量限制。为了解决这些问题，已经实施了几种算法方法，涵盖传感器、处理、控制和导航的设计。审查旨在提供有关要求、算法、以及在自动驾驶汽车中使用基于机器视觉的技术进行导航和控制的主要挑战。还介绍了使用陆基车辆作为物联网平台进行行人检测和跟踪的应用程序。