Driverless cars appeared straight out of the future, but with the evolutions in autonomous vehicle technology, that future could be exact around the corner. Soon, our cars will be capable to drive us from destination to destination without any human involvement. Currently, an autonomous semi-truck finished a trip across the U.S. without any concern. The latest study has revealed that over 90 %of road accidents are generated due to human error.
Accidents have tends to a huge number of deaths that could have otherwise been saved via safer driving. This is what self-driving cars appear to be in the scenario. Tesla's autopilot system has by far been the feature of self-driving tech and it has been in the highlight since the start. In the last year, Tesla's autopilot system has registered near about 2 billion miles of use. That's a remarkable amount of miles, with few accidents as compared to human drivers.
With the technology still improving, perhaps still in its beginnings, what is self-driving technology, and how do cars work?
What are self-driving cars?
Both terms self-driving and autonomous are utilized fairly equivalent and they both are essential. Autonomous is more extensive; however, self-driving correlates to the vehicles. Self-driving cars depend on hardware and software to operate down the road without user input. The hardware accumulates the data; the software assembles this data and compiles it. On the software side, the input data will generally be processed via machine learning algorithms or composite lines of code that have been instructed in real-world scenarios.
A self-driving car is able to sense its environment and traversing without human input. To achieve this capability, each vehicle is generally equipped with a GPS unit, an inertial navigation system and a range of sensors containing laser range finders, radar as well as video. The vehicle utilizes positional information and data from the GPS and inertial navigation system to restrict itself and sensor data to improve its estimated position as well as to construct a three-dimensional image of its environment.
Data/Information from each sensor is filtered to withdraw noise and frequently fused with other data sources to increase the original image. How the vehicle eventually utilizes this data to make navigation decisions is discovered by its control system.
The most of the self-driving vehicle control systems execute a planned architecture, meaning that they are able to make intelligent decisions by 1) keeping an internal map of their world and 2) utilizing that map to search for an optimal way to their destination that avoids hurdles (such as a road architecture, pedestrians and other vehicles) from a set of possible ways. Once the vehicle discovers the best suitable path to take, the decision is analyzed into commands, which are fed to the vehicle’s actuators. These actuators are used to handle the vehicle’s steering, braking and throttle.
This entire process of localization, mapping, hurdle avoidance and path planning is repeated many times after every second on strong on-board processors until the vehicle arrives at its destination.
How Do Self-Driving Cars Work?
Self-driving cars contain a remarkable amount of technology in them. The hardware inside these cars has remained fairly constant, but the software behind the cars is continuously changing and being upgraded. The technological jumps and bounds we have made in a few years have finally led up in making self-driving cars a reality. Self-driving cars are possible because of the existence of the following technologies:
1: IoT Sensors
There are various types of sensors accessible today that make autonomous cars a reality. Sensors for forwarding collision indication, camera, radar, LIDAR and ultrasonic all operate together to make navigation of a self-driving car possible. Radar has the lowest resolution but it can see through bad weather conditions. LIDAR sensors are situated on top of self-driving cars spinning around. These sensors flash out light and utilize the feedback to create a highly-detailed 3D map of its nearby area. Self-driving cars will also use GPS tracking, along with ultrasonic sensors and inertial sensors to acquire a full picture of what the car is exactly doing and what’s occurring surrounding it.
2: IoT Connectivity
Self-driving cars utilize cloud computing to react upon traffic data, maps, weather, adjacent cars and surface situations among others. This supports them to analyze their surroundings better and create informed decisions. Self-driving cars need to be connected to the internet even if edge computing hardware can resolve small computing tasks locally.
3: Software Algorithms
All the data the car assembles requires to be monitored to discover the best course of action. This is the prime function of the control algorithms and software. This is one of the complex parts of the self-driving car since it has to create decisions perfectly.
There are some apparent advantages to self-driving cars, likewise a reduction in traffic victims, traffic collisions and traffic congestion; whereas, there are some other advantages that come with automation, some of which are scaling down in Infrastructure Spending, Energy consumption and Increase in Productivity.
Car manufacturers have made notable advances in the past few years towards making self-driving cars a truth. Self-driving vehicles have also yet to reveal the same ability as human drivers in interpreting and traversing unstructured environments likewise construction zones and accident areas.
These hurdles though are not invincible. The total amount of road and traffic data accessible to these vehicles is improving, newer range sensors are seizing more data and the algorithms for understanding road scenes are evolving.
The transformation from human-operated vehicles to completely self-driving cars will be moderate, with vehicles at first executing only a subset of driving tasks likewise parking and driving in stop-and-go traffic independently. As technology develops, more driving tasks can be truly outsourced to the vehicle.
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In most cases construction is considered as a key service and work has to continue in these tough times in spite of the fact that with severe safety requirements in place.Remote meetings, virtual inspections and a combination of social distancing safety devices are few of the technology-based solutions being acquired enabling work to move safely.
Virtual reality technology can be utilized to take virtual run-through so stakeholders can communicate with the job site workers and get an up-to-date vision of work in progress.IoT devices are utilized for real-time tracking, analyzing and measurement on the job site thus opening huge possibilities for executing AI and improving better outcomes for contractors.
Below are some ways that COVID19 is speeding up IoT adoption in the construction industry.Project Analyzing and InvestigationTo maintain social distancing, technicians and engineers should avoid visiting factories, remote areas, or any other crowded places.
Hence with the reduction in job site visits by the investigators will helps to maintain the safety of employees which improves workplace efficiency as well.Remote WorkThe coronavirus has proposed vital changes to the construction’s back offices as well.
Many work employees had to work from home depending on technology such as video conferencing, emailing and texting to stay in contact.Many organizations are now realizing that the utilization of smartphones for photos and videos to make a document of the job site will improve communication as well as reducing waste.
In this way, project success status information is shared more precisely, effectively and efficiently than when project managers are allocated to do it physically.Supply Chain ManagementWorker’s safety is a prime concern in any organization which will help to speed up the industry’s development to offsite construction methods.
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Nowadays, a majority of cold storage facilities are relying on IoT solutions to streamline the logistics process and overcome monitoring problems.
From continuous monitoring of temperature levels in the facility to efficient management of the perishable items, there are many ways in which this tech is helping out.
Continue reading to know how the implementation of IoT solutions in cold storage warehouses.Common monitoring challenges for cold storage warehouses:Product management – Managing products in a cold storage facility can be particularly challenging.
The temperature requirements for these commodities tend to vary.
Maintaining the right temperature for all the stored items is not humanly possible.
Keeping up with the industry standards – There are stringent regulations in place that cold storage units and warehouses must adhere to.
Industrial IoT solutions have been changing the way the industry works for the last few years.
We can now have real-time monitoring devices at a low cost that sends data to an algorithm on a continuous basis.
With the added power of Machine Learning, it can detect whether there is something going wrong with a machine… make a prediction.
Hakuna Matata Solutions have been providing Industrial IoT solutions for more than 15 years helping enterprises increase ROI Alastair Orchard of Siemens notes that “IoT allows us to sprinkle devices around the factory quickly and cost-effectively to find out what’s going on without going through the multiyear process of reorganizing the shop floor.”
