The road to fully autonomous vehicles is paved with AI! With machine learning algorithms, self-driving cars are learning how to navigate traffic, recognize pedestrians, and make life-saving decisions in real time. Waymo, Google's self-driving car project, has completed over 20 million miles of testing! The more data these systems gather, the smarter they get – and soon enough, your next road trip might be hands-free. #AI #AutonomousVehicles #TechTrends #Innovation

Have you ever wondered how far AI can push the boundaries of autonomous vehicles? I mean, we’re talking about cars that can drive themselves. It’s thrilling, but also a bit scary, right? What if they can make split-second decisions that could save lives? The future of AI in autonomous vehicles is packed with game-changing innovations. First up, we’ve got advanced perception systems. Think about cars with eyes that interpret their surroundings better than we do. Next, let’s chat about deep learning algorithms. These little wonders allow vehicles to learn from countless scenarios, improving their skills with every drive. And then there’s predictive maintenance. Imagine a car that knows when it’s about to break down and can alert the driver before any mishaps happen. How about vehicle-to-vehicle

As part of the ROADVIEW project, which aims to improve autonomous vehicle driving performance in adverse weather conditions, we present some of our results in this video. Autonomous vehicles, ideally designed to operate without a human driver, must also contend with weather challenges, just like traditional drivers. To integrate these vehicles into our streets in the future, numerous tests are necessary, especially in difficult weather conditions such as heavy rain or dense fog. Since we cannot control the weather, the ability to simulate precise weather conditions greatly facilitates these tests. Additionally, simulating these conditions increases the amount of data available for algorithm training. Here, I present the initial results of my work on simulating adverse weather conditions, which are detailed in an article with the S.T.I team at Cerema. We propose a new training method for CycleGAN, called Group2Group, which significantly reduces artifacts. #roadview #cerema #roadview_eu #ArtificialIntelligenceMachineLearningDataScienceRobotics #generativeai #generativemodels #ai #machinelearning #neuralnetwork

On Day 8 of our **12 Days of AI**, we’re diving into the future of transportation. From autonomous cars to optimised logistics, AI is steering us into a safer, more efficient world. Read the blog here: https://buff.ly/41mK29y #AIinTransportation #12DaysOfAI

🚗✨ Driving the Future of AI Innovation! Real-world driving data is the backbone of advanced AI models for autonomous vehicles. By collecting diverse and accurate data, we enable better object detection, route optimization, and safer decision-making, driving innovation in autonomous technology. Learn more at www.gts.ai. #DrivingInnovation #AutonomousVehicles #AI #RealWorldData #FutureOfMobility #GTSai

I recently watched a thought-provoking video by Ankur Warikoo where he predicts that by 2050, we may no longer be behind the wheel. Instead, AI could be driving our cars, transforming how we navigate the roads. Intrigued by this, I immediately searched how far we are on the journey to autonomous cars. I found that San Francisco is already deploying such technology with autonomous vehicles being tested and used in certain areas! It’s fascinating to see how far cars have come already. Newer models feature advanced driver assistance systems (ADAS) like lane-keeping, collision detection, automatic parking, and even adaptive cruise control that adjusts to traffic conditions. These features point us in the direction of a fully autonomous future. However, I do believe that many people will still want to drive for the sheer joy of it. That said, the general scenario might shift, especially with new policies like California’s proposal to tax drivers just for using the road. I’m curious to hear from you! Do you believe we’ll still be driving in 25 years, or will AI fully take over? What are your thoughts on the current advancements in this space? #FutureOfTech #AI #AutonomousDriving #Innovation #2050Predictions #TechTrends

Find out how self-driving cars use big data and AI, and learn about the five levels of self-driving cars. Discover how much data an autonomous car can generate and how Quobyte provides an efficient solution to handle the load. https://buff.ly/3VCBfvH #SDS #SoftwareDefinedStorage #SoftwareStorage #SelfSrivingCars #AI #AutonomousVehicles #AIDataStorage

Autonomous vehicles are not a dream; they're becoming a reality. With support from regulations and advanced AI technology, we can expect them on roads sooner than later. People, brace yourselves for a new driving era! #AutonomousVehicles #AI #RegulatoryTech

One of the most critical parts of making vehicles fully autonomous: simulators, which require huge volumes of data to be representative of the real world. Whether it's a pedestrian jaywalking or unpredictable weather conditions, these simulators must be able to mirror rare events for vehicles to reduce the risk of accidents and become fully autonomous. Waymo Co-CEO Dmitri Dolgov is focused on scaling their powerful simulation tool to build the world's most trusted driver. Join him in the latest AI Revolution conversation with Growth general partner David George on how they are uniquely positioned to shape the future of AI.

#AI Needs #Data, and Lots of It! To capture the complexities of the real world, AI models require vast amounts of data: -Text: Training advanced language models takes over 570 GB—equivalent to around 45 million books. -Images & Video: AI relies on databases with over 14 million images and thousands of hours of video. Enter #DataCenters: With data demand skyrocketing, datacenters are the backbone of AI, powering the processing and storage of this immense information. As AI grows, #efficient and #scalable datacenters are more crucial than ever. The future of tech is here, and it’s data-driven.