Introduction
With the advent of autonomous vehicles, there is an increasing need for more data-driven insights that can be used to improve the driving experience and make roads safer. However, Edge Computing, Latency Reduction gathering data in real time from sensors on vehicles is a challenge that many developers have yet to overcome. This is because when latency occurs in edge computing networks—the time it takes for data processing and transmission between various components within a system—it can negatively impact driver experiences like responsiveness and safety.
What is Edge Computing?
Edge computing is a new approach to computing that treats the edge of the network as a primary location for data processing and storage. It’s not just about reducing latency in the data processing chain, but also about taking advantage of cheaper hardware by moving it closer to where you need it.
Edge computing is a form of distributed computing, meaning that tasks are split up among multiple devices instead of being handled by one central unit (like your laptop). This can help reduce costs by eliminating some traditional infrastructure components like servers and storage systems at remote locations outside your organization’s physical boundaries.[1]
In addition to improving efficiency and lowering costs, edge computing offers other benefits:
Why does latency matter?
Latency is the time it takes for a message to travel from one point to another. Latency is critical for autonomous vehicles, because it determines how quickly the car can react to its environment. For example, if you’re driving down a highway at 65 mph and suddenly come up on an obstacle in your lane (perhaps another car or pedestrian), your reaction time will be measured in milliseconds–and that includes both processing what’s happening ahead of you and then telling your vehicle how best to avoid it.
The difference between safe operation and unsafe operation could be as little as 100 milliseconds: if there’s any delay between identifying danger and taking action against it, then lives could be lost unnecessarily as people make bad decisions based on outdated information about their surroundings
How does Edge Computing solve for latency?
How does Edge Computing solve for latency?
In order to understand how Edge Computing solves for latency, it’s important to understand what exactly it is and how it works.
Latency is the time it takes a signal or data packet to travel between two points over a network, such as a WiFi connection or cellular network. In today’s world where everything happens at lightning speed, this can be an issue if you want your content delivered instantly–and with little-to-no lag time between pressing “play” on your favorite show and seeing its intro sequence pop up on your screen.
Impact of Latency on Driver Experience
Latency is a problem for autonomous cars. Latency is the time it takes for data to travel between two points, and if you’re trying to drive a car with high-speed Internet access on board, that can be problematic.
If you think of your phone as an example of how latency works, imagine yourself trying to text someone while walking down the street at night–your message may not send until after you’ve already passed them by! If this happens enough times over time, it could mean that messages get lost or delayed, which could cause confusion among friends and family members who rely on being able to communicate quickly via their phones in order for things like dinner plans or dates at movie theaters (or anywhere else) go off without a hitch.”
Impact of Latency on Data Collection
In the autonomous vehicle space, latency is a major concern for data collection. If you want to collect high-resolution images of objects in front of your car and use these images to create a virtual representation of the world around it, then you’re going to need some serious processing power.
The problem is that all that processing takes time–a lot of time. If your car can only process one image every second (and this is actually optimistic), then even if there are no other cars on the road ahead or behind, it would take about 30 seconds just for one image from each direction! That doesn’t sound like much but consider this: if there were four lanes each with two cars moving at 60 mph (100 kmh), that means around 880 vehicles could pass by before getting any new data about them!
Edge computing is the future.
Edge computing is the future of IoT. It improves the driver experience and data collection, reduces latency and is more cost effective.
Latency is one of the most critical challenges facing autonomous vehicle developers today, but edge computing may provide a solution.
Latency is a significant challenge for autonomous vehicle developers. The ability to sense and respond quickly is critical, especially for autonomous vehicles. As one example, if an object appears in front of your car at night, you want it to be able to brake immediately so that you don’t hit that object or swerve out of control and crash into another car or pedestrian.
Edge computing can help address this problem by providing data from cameras and other sensors at low latency (meaning it takes less time for the information to get from point A (the camera) to point B (your smartphone)) so that decisions can be made faster than ever before possible without edge computing technology involved in the process.
In addition, edge computing has applications beyond just autonomous vehicles: It could also improve healthcare services by providing quicker access to patient records; enable more efficient use of resources by allowing businesses like Amazon Web Services (AWS) and Microsoft Azure cloud customers within their geographic regions; provide faster response times when dealing with natural disasters such as hurricanes Irma/Maria which devastated Puerto Rico last year; allow individuals living abroad who aren’t connected via cable/DSL internet connection still have access through mobile devices while abroad
Conclusion
Edge computing is an exciting new technology that promises to revolutionize the autonomous vehicle industry. The ability to process data on-board vehicles means that they can react faster and more efficiently than ever before. This will lead us towards safer roads where fewer accidents occur because of human error or faulty equipment.
