Perception is a vital part of driving. Every year, the loss in visibility due to snow, fog, and rain causes serious accidents worldwide. Therefore, it is important to be aware of the impact of weather conditions on perception performance while driving on highways and urban traffic in all weather conditions. The goal of this paper is to provide a survey of sensing technologies used to detect the surrounding environment and obstacles during driving maneuvers in different weather conditions. Firstly, some important historical milestones are presented. Secondly, the state-of-the-art automated driving applications (adaptive cruise control, pedestrian collision avoidance, etc.) are introduced with a focus on all-weather activity. Thirdly, the most involved sensor technologies (radar, lidar, ultrasonic, camera, and far-infrared) employed by automated driving applications are studied. Furthermore, the difference between the current and expected states of performance is determined by the use of spider charts. As a result, a fusion perspective is proposed that can fill gaps and increase the robustness of the perception system.

https://www.mdpi.com/1424-8220/20/22/6532/pdf

The Perception System of Intelligent Ground Vehicles in All Weather Conditions: A Systematic Literature Review

The emerging of the fourth industrial revolution, also known as Industry 4.0 (I4.0), from the advancement in several technologies is viewed not only to promote economic growth, but also to enable a greener future. The 2030 Agenda of the United Nations for sustainable development sets out clear goals for the industry to foster the economy, while preserving social well-being and ecological validity. However, the influence of I4.0 technologies on the achievement of the Sustainable Development Goals (SDG) has not been conclusively or systematically investigated. By understanding the link between the I4.0 technologies and the SDGs, researchers can better support policymakers to consider the technological advancement in updating and harmonizing policies and strategies in different sectors (i.e., education, industry, and governmental) with the SDGs. To address this gap, academic experts in this paper have investigated the influence of I4.0 technologies on the sustainability targets identified by the UN. Key I4.0 element technologies have been classified to enable a quantitative mapping with the 17 SDGs. The results indicate that the majority of the I4.0 technologies can contribute positively to achieving the UN agenda. It was also found that the effects of the technologies on individual goals varies between direct and strong, and indirect and weak influences. The main insights and lessons learned from the mapping are provided to support future policy.

Mapping Industry 4.0 Enabling Technologies into United Nations Sustainability Development Goals

Proximity sensors are broadly used in mobile robots for obstacle detection. The traditional calibration process of this kind of sensor could be a time-consuming task because it is usually done by identification in a manual and repetitive way. The resulting obstacles detection models are usually nonlinear functions that can be different for each proximity sensor attached to the robot. In addition, the model is highly dependent on the type of sensor (e.g., ultrasonic or infrared), on changes in light intensity, and on the properties of the obstacle such as shape, colour, and surface texture, among others. That is why in some situations it could be useful to gather all the measurements provided by different kinds of sensor in order to build a unique model that estimates the distances to the obstacles around the robot. This paper presents a novel approach to get an obstacles detection model based on the fusion of sensors data and automatic calibration by using artificial neural networks.

A Neural Network Approach for Building An Obstacle Detection Model by Fusion of Proximity Sensors Data

Border surveillance is the most important task in the field of national defense and security. To maintain peace and to ensure safety of a country’s people, the borders need to be kept under 24/7 monitoring. Especially, under current circumstances, when activities like terrorist infiltrations and illegal movement of both living as well as non-living beings have become common, it becomes of utmost importance to strictly protect the border areas against such activities. To curb such happenings on the border areas, the least that can be done is to provide constant monitoring. In current scenario, this monitoring takes place manually by the border security forces which are responsible for continuously keeping an eye on the borders. It takes a lot of manpower and assets as the borders are stretched across hundreds of miles and have extreme terrain as well as climatic conditions. Hence, the need of the hour is to design an automated border surveillance system which can perform the surveillance task without requiring any human assistance. It can eliminate the need of deploying humans at hostile conditions at all the times. Moreover, in case if something suspicious is detected by the system, it must be able to take the necessary decisions and hence actions along with issuing alert messages for the human controllers. The central control rooms can be set up at a distance from the border area. Once the human controller is aware of the intrusion, it is upon him to decide the next course of action. Such systems if deployed successfully, can not only save resources but also can reduce the risk to human life significantly. However, complete automation of border surveillance is not yet attainable due to safety constraints but surely such systems can provide assistance and can work in collaboration with the defense forces and thus, making the country’s borders secure.

Smart Border Surveillance System using Wireless Sensor Network and Computer Vision

In this paper usage of a small, light, low-cost VL53LOX sensor for robot environment detection is proposed. Using the time-of-flight technology it provides reliable information about surrounding objects over a range of up to 2 meters. Precision of the sensor is tested using surfaces of different materials and colors. Also sensor operation under different lighting condition was tested. It is shown that this sensor provides a good and reliable low-cost alternative that has potential application in robotics for the purpose of proximity sensing and potential applications in object recognition and odometry.

Application of low-cost VL53L0X ToF sensor for robot environment detection

In robotics, Ultrasonic sensors and Infrared sensors are commonly used for distance measurement. These low-cost sensors fundamentally address majority of problems related to the obstacle detection and obstacle avoidance. In this paper, the performance comparison of ultrasonic and infrared measurement techniques across obstacles of different types of materials presented. The Vehicle model integrated with the sensors, moving with constant velocity towards different types of obstacles for capturing the distance parameter. Based on the data acquired from the sensors, correlation analysis of the measured distance with actual distance performed. This analysis will be very much useful, to select the right sensor - Ultrasonic sensor / Infrared sensor or a combination of both sensors, while developing the algorithm for addressing obstacle detection problems. The detection range and inherent properties of sensors (reflection/ absorption etc.) also were tested in this experiment.

https://iopscience.iop.org/article/10.1088/1757-899X/149/1/012141/pdf

S Mohamed Kaleemuddin

Papers

Performance comparison of Infrared and Ultrasonic sensors for obstacles of different materials in vehicle/ robot navigation applications