Antonio Correas’ Post

Synthetic data is used to recreate real-world conditions to train AI algorithms in data-rich environments, where reality is not sufficient. A good example where this is being used today is the training of perception algorithms in autonomous vehicles to address novel view points (). Most importantly, synthetic data allows to factor in edge cases that are rare but may cause a catastrophic incident when they happen (like rogue shopping carts 🛒 or runaway chickens on the road 🐓). DataGenesis does the same: it creates a demographic baseline for your customer data, and then recreates test and training conditions from real-world that can include a good amount of edge cases. Do you need families with quintuplets? We’ve got you covered! https://lnkd.in/e_raUEJD #ai #tech #innovation

A short article for the AI or simulation lovers to check out during the weekend. It summarizes why we need simulations to train autonomous vehicles/vessels/equipment. Simply put "... While it isn’t ethical to have someone run unexpectedly into a street to train AI to handle such a situation, it’s significantly less problematic for an animated character inside a virtual world...We should think of simulation as an accelerator to what we do in the real world. It can save time and money and help us with a diversity of edge-case scenarios....." #AI #simulation #autonomous

Autonomous cars and artificial intelligence (AI) share a profound and fascinating connection in the world of technology. Both represent significant advancements that are shaping the future of mobility and computational intelligence. Both autonomous cars and artificial intelligence heavily rely on complex algorithms and machine learning to operate efficiently and make precise decisions. In autonomous cars, these algorithms are essential for processing sensor data, such as cameras, radars, and lidar, and interpreting the vehicle's surroundings. Similarly, AI systems use algorithms to analyze large datasets, identify patterns, and learn from past experiences. Moreover, both are constantly evolving and improving. Advances in AI technology, such as deep neural networks and reinforcement learning algorithms, are enabling autonomous cars to become more autonomous and safer each day. Simultaneously, the widespread implementation of autonomous cars is driving the development of more sophisticated AI algorithms capable of handling a variety of complex traffic situations and making real-time decisions. They are intrinsically linked by their reliance on advanced algorithms, the constant pursuit of improvement, and the ability to radically transform how we interact with the world around us.

Autonomous driving is a complex integration of multiple software components that enable vehicles to perceive their environment, make decisions, and navigate without human intervention. Here are the key components of the autonomous driving software stack: 1. Sensors and Data Collection: LIDAR, RADAR, cameras, and ultrasonic sensors provide a 360-degree view of the vehicle's surroundings. 2. Perception: Deep learning models, especially convolutional neural networks (CNNs), are commonly used to detect and classify objects, lanes, traffic signs, and other critical elements. 3. Sensor Fusion: Data from different sensors is combined to get a comprehensive and accurate representation of the environment. 4. Localization: High-definition maps and sensors like GPS, IMUs, and odometry are used to determine the vehicle's position with centimeter-level accuracy. 5. Path Planning: Path planning algorithms determine the best route, considering traffic rules, dynamic obstacles, and other constraints. 6. Control: Control algorithms ensure smooth and safe driving behaviors. 7. Connectivity and Updates: Modern autonomous vehicles are connected to the cloud for over-the-air updates, data sharing, and sometimes remote control. These components work together to make autonomous driving a reality.

👁 OpenCV: the sight of AI OpenCV is not just a tool it offers incredible features for real-time image processing, critical in autonomous driving where every millisecond counts. It's fascinating to see how algorithms can empower a machine to make intelligent, life-saving decisions. 🛠 Using the powerful Haar Cascade algorithm, I've developed a system that tracks cars (marked with red boxes) and pedestrians (highlighted in yellow). This technology mirrors the advanced systems used in Tesla cars, showcasing the potential of machine learning in making vehicles smarter and safer. 🧠 This project isn't just a testament to my passion for AI and machine learning; it's a glimpse to show how autonomous cars interact seamlessly and safely with their environment. And guess what? This is just the beginning. The potential applications of OpenCV are limitless, from facial recognition to motion detection and many more #OpenCV #HaarCascade #MachineLearning #AutonomousVehicles #TechInnovation

Everything You Wanted to Know About Models but Were Afraid to Ask Artificial Intelligence (AI) has become an integral part of our daily lives, powering everything from smartphone assistants to autonomous vehicles. At the heart of these AI systems are models – complex mathematical structures that enable machines to perform tasks that typically require human intelligence. Discover the different types of AI models, how they're created, and their real-world applications. 

Revolutionizing Autopilot with Self-Supervised Learning Self-supervised learning (SSL) is a powerful technique driving advancements in autonomous vehicle technology. 1️⃣ Learning from Unlabeled Data: Data Abundance: Autonomous vehicles generate massive amounts of data. SSL leverages this unlabeled data to learn useful representations without manual annotation. Pretext Tasks: Uses tasks like predicting the next frame in a video or reconstructing missing parts of an image to train models. 2️⃣ Applications in Autonomous Driving: Perception: Enhances object detection and recognition by learning from unlabeled video and sensor data. Localization: Improves vehicle localization and mapping by training on raw sensor data from cameras, LiDAR, and radar. Behavior Prediction: Predicts the behavior of other road users by analyzing large-scale driving scenarios. 3️⃣ Advantages of Self-Supervised Learning: Cost Efficiency: Reduces the reliance on expensive and time-consuming labeled data. Data Utilization: Maximizes the use of all available data, leading to more robust models. Continuous Improvement: Enables continuous learning and improvement as more data is collected. 4️⃣ Challenges and Considerations: Model Complexity: Developing effective pretext tasks and architectures that generalize well to downstream tasks. Computational Resources: Requires substantial computational power for training on large datasets. Safety and Validation: Ensuring the models meet stringent safety and reliability standards. Self-supervised learning is pushing the boundaries of what's possible in autonomous driving, making self-driving cars smarter and safer. #AutonomousDriving #SelfSupervisedLearning #AI #MachineLearning #Autopilot #EncephAI

🚘 Training a self-driving car is data-hungry, really data-hungry. It requires maintaining safety and accuracy in the ever-changing traffic conditions, but the self-driving car can't move around too freely while learning to do that. You need to expose the autonomous car system to tons of videos, so it can learn to recognize everything it may encounter on its way: kids crossing the street, other vehicles, raindrops on the window, and many other objects and scenarios. How many videos are needed? The estimations vary, and range from 100Ks to Millions of video hours. As autonomous cars become more sophisticated and neural networks go deeper and wider - this number may rise even more. Handling a vast amount of videos causes significant challenges for the autonomous cars industry, just as it does for other AI players: slow training and inference speed, GPU clusters, data storage, networking bottlenecks and high associated costs.   💡 Beamr Cloud, GPU-based video service, allows you to transform videos into smaller, faster and more efficient versions of themselves - and they look exactly the same! 💡Our recent case study showed that training an action recognition model with significantly smaller files yielded similar results. This is part of our quest to accelerate video for machine learning and AI applications (more details in the first comment). #beamr #AutonomousCars #AI #cloud #video

Researchers have developed a technique that allows artificial intelligence (AI) programs to better map three-dimensional spaces using two-dimensional images captured by multiple cameras. Because the technique works effectively with limited computational resources, it holds promise for improving the navigation of autonomous vehicles. #GESSEngineering #GESS #Engineering #AI #Automotive

I am thrilled to announce our new paper in Applied Soft Computing! Our research team has developed a novel framework for robust autonomous vehicle control in real-world scenarios! This framework tackles uncertainties in vehicle dynamics through: + A multi-stage Model Predictive Control (MPC) approach for optimal control outputs ☑️ + A customized, quantized CNN (Nano-FastSCNN) for accurate lane line segmentation (half-precision!) ⚡ + Hardware-in-the-loop (HIL) validation with Jetson Nano for real-time processing ️ This system paves the way for safer and more reliable autonomous vehicles! #autonomousvehicles #pathfollowing #pathplanning #CNN #uncertainty #MPC #HIL #JetsonNano #engineering #research #AI #computervision #deeplearning #machinelearning #robotics #automotive #transportation #safety #iust #iranuniversityofscienceandtechnology Iran University of Science and Technology Thanks for dear co-authors Amir Khosravian Abdollah Amirkhani Salman EbrahimiNejad Paper link: Robust autonomous vehicle control by leveraging multi-stage MPC and quantized CNN in HIL Framework - ScienceDirect https://lnkd.in/dtW5qmZa