Himal Raveen’s Post

🚗🤖 AI Revolutionizing Automotive Engineering 🤖🚗 AI is reshaping automotive engineering, driving innovation across the board. In autonomous driving, it enhances safety and efficiency through real-time decision-making. Generative design algorithms optimize vehicle design, while predictive maintenance ensures manufacturing efficiency. Beyond the vehicle, AI offers personalized user experiences and predictive analytics for fleet management. Embracing AI-driven innovation paves the way for safer, smarter, and more sustainable mobility solutions. Let's accelerate towards a future where AI powers the next generation of automotive engineering. 🌟 #AI #AutomotiveEngineering #EmbeddedSystems #Innovation #AUTOSAR #VCycle #C #Python #Diagnostics

Aptiv Innovates Autonomous Driving with MATLAB! Explore how Aptiv is pushing the boundaries of autonomous driving using virtual scenarios powered by MATLAB. Discover how Aptiv leveraged RoadRunner to create digital twin scenarios for ADAS validation using ASAM Open Simulation Interface standards. This user story delves into the innovative approaches that enhance safety and efficiency in autonomous vehicle development. 🚗 👉 Read the Story: https://spr.ly/6043QABVH Drive the future of mobility with cutting-edge technology! #AutonomousDriving #MATLAB #Innovation #VirtualScenarios #DigitalTwin #MathWorks

Attendees will learn: -Safety by validation: the necessity of accurate synthetic data -End-to-end real-time physics-based approach for accurate synthetic data generation -Bridging the gap between Tier 1s and OEMs by enhanced camera sensor simulation -Highlights of challenging edge cases for camera perception testing and simulation

If you're developing an autonomous vehicle, this is for you. Acquiring real-life data for dangerous (critical) driving situations is challenging. Synthetic data can fill this gap. Learn more in this educational webinar, on May the 28th:

🚗✨ Exciting News! ✨🚗 I'm thrilled to announce that I have successfully completed the Self-Driving Car Course! This comprehensive course provided deep insights into the world of autonomous vehicles, covering both theoretical foundations and practical implementations. Throughout the course, I had the opportunity to: 🔹 Dive into the theory of vehicle automation, understanding everything from sensor technology and perception to planning and control systems. 🔹 Gain hands-on experience by building and programming self-driving car using the NVIDIA Jetbot, a small but powerful AI-powered robot. 🔹 Implement machine learning models for tasks like lane detection and obstacle avoidance, pushing the boundaries of what these technologies can achieve. The rise of autonomous vehicles is transforming the future of transportation, and I am excited to be part of this cutting-edge field. I'm looking forward to applying the skills and knowledge I've gained to contribute to innovative projects and collaborate with other professionals in the industry. A big thank you to Ritesh Kanjee & Augmented A.I. team for this incredible learning experience. Here's to the future of self-driving technology! #AutonomousVehicles #SelfDrivingCars #AI #MachineLearning #Robotics #NVIDIAJetbot #ContinuousLearning #FutureOfTransportation

🏎 AUTONOMOUS VEHICLE CONTROL SYSTEM 🏎 Have you ever asked yourself how cars can drive autonomously? Excited to share my final project in advancing Autonomous Guiding Vehicle Control Systems conducted at Universitat Politècnica de Catalunya. It's been possible to utilise the Hough transform algorithm in MATLAB for accurate lane line detection and tracking, allowing the vehicle to maintain its lane position precisely. Additionally, the project explores integrating color detection to enable the vehicle to stop and avoid obstacles. Main key steps: 1. Image Processing and Lane Detection: We created a MATLAB script for the Hough transform algorithm to identify positive and negative lines representing lane edges. 2. Lane analysis: We developed a MATLAB script to identify the longest positive and negative lines of the lane. This facilitated the creation of diverse scenarios, enabling the car to receive specific instructions on how to react in each situation. 3. Driving phase: We tested a mini self-driving BMW electric car on the circuit and created additional scenarios to provide more optimized and precise commands. This was achieved using an updated Simulink control function. 4. Stopping phase: To enable the car to stop at red signals, a filter was integrated into the Simulink circuit to detect the color red. By quantifying the "redness" perceived by the camera and setting a threshold value, it was possible to instruct the car to halt if this value was exceeded. 5. Obstacle Avoidance: In this phase, we created various scenarios in the Simulink model requiring different adjustments to the car's steering and speed. Unlike simply stopping the car, obstacle avoidance is more complex because it involves more than just stopping the engine. The system must decrease the car's speed and steer the wheels based on the proximity and position of the obstacle as detected by the car's webcam. I would like to sincerely thank everyone involved for their invaluable support and guidance throughout this project. T.B.N: Further information can be found in the attached report. #AutonomousVehicles #Matlab #Simulink #ComputerVision #Innovation #FutureTechnology #IntelligentTransport #SelfDriving

Remarkable NVIDIA's a new type of artificial intelligence (AI) simulation software in an attempt to turbocharge the development of self-driving cars and robots. #AI #Self-driving ##robotics #automation #software #automotive

🚗 Excited to Share My Journey into the World of Vehicle Modeling and Self-Driving Technology! 🚗 I happy to share that I have completed an Introduction to #Self_Driving_Cars course by #University_of_Toronto, which provided an incredible learning experience beyond just autonomous vehicles. This course deep-dived into advanced modeling techniques for all vehicle types, including electric vehicles (EVs), hybrid vehicles (HVs), and conventional vehicles. It covered essential components like acceleration, braking, steering, and detailed longitudinal, lateral motion, and tire modeling—topics often viewed as purely mechanical but crucial for understanding vehicle dynamics. One of the highlights was learning how to leverage Python programming in CARLA, a powerful simulator for testing and visualizing motion and modeling. The course also introduced cutting-edge methods in motion planning using machine learning and deep learning to recognize road markings and generate optimal driving trajectories. This course has truly broadened my perspective on transforming real-world vehicle behaviors into digital models and simulations. It’s an invaluable experience I highly recommend for anyone interested in the automotive field, self-driving technology, or advanced vehicle modeling. Looking forward to applying these skills and contributing to the future of vehicle innovation! #python #machine_learning #self_driving_car #CARLA #autonomous_vehicles

Excited to see the advancements in autonomous vehicle testing with Parallel Domain's launch of PD Replica! High-fidelity digital twins are a game-changer for the industry, providing safer and more efficient testing environments. 🌟 Full Disclosure: I didn’t even know what a hi-fi twin was before reading this article 😄 ! It’s a reminder that with each passing day, technology evolves, and staying updated is crucial to avoid becoming obsolete.

🚗 Excited to announce the completion of the Self-Driving Cars Specialization from the University of Toronto on Coursera! 🎉 Over the past few months, I've delved deep into the intricate world of autonomous vehicles, covering four comprehensive courses: 1️⃣ Introduction to Self-Driving Cars 2️⃣ State Estimation and Localization for Self-Driving Cars 3️⃣ Visual Perception for Self-Driving Cars 4️⃣ Motion Planning for Self-Driving Cars Throughout this specialization, I've gained a wealth of knowledge and practical skills: 🔹 Understanding the hardware and software components of self-driving cars 🔹 Programming vehicle modeling and control 🔹 Analyzing safety frameworks and industry practices 🔹 Implementing state estimation techniques like Kalman Filters 🔹 Mastering visual perception techniques and convolutional neural networks 🔹 Developing motion planning solutions using Dijkstra's and A* algorithms 🔹 Constructing hierarchical motion planners for real-world scenarios The final project, where I implemented a hierarchical motion planner in the CARLA simulator, was particularly rewarding. Navigating through dynamic scenarios while ensuring robustness to environmental changes provided invaluable hands-on experience. #SelfDrivingCars #AutonomousVehicles #UniversityofToronto #Coursera #Specialization #LearningJourney 🛣️🤖