Edutech Future Mobility Labs’ Post

Did you know that autonomous vehicles rely on a combination of sensors like LiDAR, cameras, radar, and ultrasonic technology to navigate the world around them? These sensors work together to ensure precision, safety, and efficiency on the road. I’ve put together a presentation that provides an overview of these essential sensors, their roles, and how they contribute to the innovation in self-driving technology. 🚀 👉 Check it out below and let me know your thoughts! 💬 What sensor technology do you think will drive the most innovation in the coming years? #AutonomousVehicles #SensorTechnology #Innovation #LiDAR #FutureOfTransportation

𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝘃𝗲𝗵𝗶𝗰𝗹𝗲𝘀 𝗶𝘀𝗻’𝘁 𝗷𝘂𝘀𝘁 𝗮𝗯𝗼𝘂𝘁 “𝘀𝗲𝗲𝗶𝗻𝗴”—𝗶𝘁’𝘀 𝗮𝗯𝗼𝘂𝘁 𝘀𝗲𝗲𝗶𝗻𝗴 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴, 𝗶𝗻 𝗮𝗹𝗹 𝗰𝗼𝗻𝗱𝗶𝘁𝗶𝗼𝗻𝘀. Cameras are excellent for detecting traffic lights, lane markings, and pedestrians… until reality kicks in. I've seen firsthand how direct sunlight can completely blind a camera, making traffic lights almost invisible. And detecting an obstacle on a foggy night? A real challenge for cameras alone. This is where 𝘀𝗲𝗻𝘀𝗼𝗿 𝗳𝘂𝘀𝗶𝗼𝗻 comes into play: combining cameras for classification, lidar for precise 3D mapping, and radar for all-weather performance and velocity tracking. Together, these sensors create a system that can "see" more reliably, but there’s a catch: 🏷️ 𝗶𝘁 𝗶𝗻𝗰𝗿𝗲𝗮𝘀𝗲𝘀 𝘁𝗵𝗲 𝗕𝗢𝗠 𝗰𝗼𝘀𝘁. The trade-off? Optimal performance and safety come at a price. For me, sensor fusion is still the clear winner when building reliable AV perception systems. What’s your take- are the added costs justified for better results? #AutonomousVehicles #SensorFusion #Lidar #Radar #ADAS

In my previous post I was talking about #autonomy and #autonomous vehicles, but how do they perceive the world? The answer is through cutting edge #sensing technology, including cameras, LiDAR, radar, and thermal sensors. Discover further insights into the technology that enhances precision, ensures system robustness, and minimizes deployment time. #Innovation #Technology #Sensors #FutureofTransportation

Bio-inspired cameras and AI for supercharged pedestrian detection. More awesome automotive news! 🚗🚗🚗 Researchers have developed a bio-inspired camera system combined with AI that detects pedestrians and obstacles 100 times faster than traditional car cameras. This innovation is inspired by the way biological systems like the human eye process visual information. The article mentions the use of event-driven cameras that only capture changes in the scene, reducing data overload and improving processing speed. This system can even detect pedestrians entering the field of view between frames, a crucial factor for safety at high speeds where traditional cameras might miss critical moments. The article talks about the potential for integrating these bio-inspired cameras with LiDAR sensors, like the ones used on self-driving cars. This hybrid approach is inching us ever closer to autonomous vehicles. Here’s the Techxplore article I found this in: https://lnkd.in/e9E2tFsN Speaking of LiDAR, we’ve got a surge of projects annotating 3D data lately - exciting stuff! #AI #Engineering #SelfDrivingCars #ComputerVision 

🤖 The field of robotics is driving innovations from industrial automation to autonomous vehicles and beyond. Sophisticated software integrates inputs from various sensors like LiDAR, cameras, IMUs, barometers, and GPS, enabling robots to perceive and interact with their environment. Developing this software comes with challenges, particularly in handling and fusing data from these diverse sources. ✅     👉 Dive into the full article by Mariusz Szczepanik to learn about the solutions to synchronising, calibrating, and reducing noise in sensor data! 🔗 https://lnkd.in/gVn94KBj #LiDAR #IMU #robotics #innovation #sensorfusion

#ChipNewsExpress As the automotive industry moves toward Advanced Driver Assistance Systems (ADAS) and autonomous vehicles (AV), sensor fusion powered by AI is becoming essential. By combining data from multiple sensors (like radar, LiDAR, and cameras), AI helps create a more accurate, reliable view of the environment, enabling safer driving experiences. The future of transportation is evolving with AI-driven sensor fusion, pushing the boundaries of what's possible in automotive design. #AIChipLink #Semiconductors #Chips #IC #AI #ADAS #AutomotiveInnovation

In the realm of autonomous vehicles, LiDAR technology is making significant strides in ensuring pedestrian safety. LiDAR, or Light Detection and Ranging, uses pulsed laser light to measure distances and generate detailed 3D maps of the environment. 🌐🚀 This technology is crucial for autonomous vehicles as it allows them to "see" and understand their surroundings in real time. More importantly, it enables these vehicles to detect pedestrians, even in challenging conditions such as low light or bad weather. ☔🌙 By accurately identifying and tracking pedestrians, LiDAR can help autonomous vehicles predict pedestrian movements and adjust their path accordingly, significantly reducing the risk of accidents. This is a major step forward in our journey towards safer roads. 🛣️👍 Moreover, the integration of AI with LiDAR can further enhance pedestrian safety. Machine learning algorithms can analyze LiDAR data to recognize pedestrian behaviors and anticipate potential hazards. This combination of technologies is paving the way for a future where pedestrian safety is paramount. 🚸🤖 As we continue to innovate and improve upon these technologies, we move closer to a world where road accidents are a thing of the past. Let's embrace LiDAR and the safety it brings to our streets! 🌍💡 #IntellectCoreAdvisoryandSolutions #LiDAR #PedestrianSafety #AutonomousVehicles #AI #MachineLearning #RoadSafety 🚦

Researchers in Korea have adapted the YOLO 3D machine-learning framework for 3D object detection in real time. A critical requirement for the success of autonomous vehicles is their ability to detect and navigate around 3D obstacles, pedestrians and other vehicles across diverse environments. Current autonomous vehicles employ smart sensors such as Lidar for a 3D view of their surroundings and depth information, while radar is typically used for detecting objects at night and in cloudy weather, and a set of cameras is often utilised for providing RGB images and a 360o view – collectively forming a comprehensive dataset which we know as a point cloud. The researchers at the Department of Embedded Systems Engineering at Incheon National University (INU), Korea, have developed a deep learning- based, end-to-end, 3D object-detection system. The system is built on the YOLOv3 (You Only Look Once) deep-learning object-detection technique, which is the most active state-of-the-art method available for 2D visual detection, which the researchers modified to detect 3D objects. This technique uses point-cloud data and RGB images as input, and it generates bounding boxes with confidence scores and labels for visible obstacles as output. Read more ➡ https://lnkd.in/ey4jz7XP #uncrewedsystemstechnology #autonomousvehciles #embeddedsystems #objectdetection

If you're into autonomous vehicles, robotics, or LiDAR tech, this paper is a must-read! It introduces ScoreLiDAR, a game-changing method for completing sparse 3D LiDAR scans efficiently and accurately. 🌟 Why It Matters to You ➡️ Speed is critical in autonomous systems. This method slashes scene completion time from 30 seconds to just 5 seconds (5x speedup!)—perfect for real-time applications. ➡️ It doesn't just speed up—it enhances quality, achieving superior results compared to the previous state-of-the-art. Key Insights ➡️ Novel Structural Loss: Combines scene-wise and point-wise accuracy, ensuring not just better overall shapes but also capturing fine details like vehicles and obstacles. ➡️ Distillation Magic: Compresses powerful diffusion models into lightweight ones without compromising performance. Real-World Impact: Offers autonomous systems faster and more accurate environmental awareness, which is vital for safety and navigation. #AnalyticsVidhya #DataScience #GenerativeAI

Kudan's SLAM technology has definitely been implemented in navya. I asked macnica. They said they couldn't disclose detailed information, but Kudan's SLAM has been implemented in navya's self-driving EV bus. As I said before, I asked a navya operator and he told me that navya does not use any remote control via communication, and achieves autonomous driving completely offline. I was also surprised twice when I heard that autonomous driving is achieved [camera-less (without cameras), using only LiDAR and sensors]. #Kudan #macnica　#navya #ARMA #SLAM #CameraLess　#NoCamera