AutonomouStuff’s Post

How to pick an autonomous vehicle architecture ? Perception: We use a set of vehicle sensors, such as cameras, LiDARs, RADARs, ultrasonics, and more... and we perceive the world. Localization: We take the output of Perception, a GPS, and a map, and localize ourselves in the world Planning: From the obstacles around, and from our position, we plan a trajectory from A to B Control: Using the trajectory information, and the vehicle parameters (weight, tire size, etc...), the control system generates a steering angle, and acceleration value. #selfdrive #automotive #selfdriverentacar

Our customer, a leading automotive supplier, required reliable and efficient end-of-line (EOL) testing for fully assembled bumpers. The test should cover a wide range of sensors and electronic components required for functions such as collision avoidance, blind spot assist, pedestrian protection, parking assistance, keyless go, automatic boot opening and cameras. A solution with a vision for the future, as all relevant sensors for autonomous driving are being tested. Would you like to know how Berghof tackled this problem and solved it? 💡Click here for the Case Study: https://lnkd.in/e9nneZz2 #Automotivetesting #bumpertesting #Automotive #AutomotiveOEM #Endoflinetesting #autonomousdriving

How to pick an autonomous vehicle architecture ? Perception: We use a set of vehicle sensors, such as cameras, LiDARs, RADARs, ultrasonics, and more... and we perceive the world. Localization: We take the output of Perception, a GPS, and a map, and localize ourselves in the world Planning: From the obstacles around, and from our position, we plan a trajectory from A to B Control: Using the trajectory information, and the vehicle parameters (weight, tire size, etc...), the control system generates a steering angle, and acceleration value. #selfdrive #automotive #selfdriverentacar

Successfully completed my Final Year Project with precision and dedication. Final Year Project Objectives: > Development of Android application based framework (Leveraging GPS and heading sensors), to enhance outdoor navigation accuracy for self-driving autonomous car to execute a computer vision-based navigation algorithm for guided path trajectory and decision making capabilities. > Development of autonomous outdoor mobile robot which features lane assist, cruise control, traction control, and an autonomous braking system to improve the autonomous vision based self-driving capability. > Wireless interfacing with Simulink to test the performance improvement of self-driving feature.

Self-Driving Model Car with Active Vision System An autonomous model car with flex-touch sensors and a deep-net controlled camera follows high level dirrectional signals to navigate a complex path in a real-world environment. At one point the car seems stuck but with a combination of active vision and touch sensing it finds its way back to its desired path.

Time of Flight (ToF) cameras operate by transmitting the light source and receiving the reflected light source. It ensures they calculate the depth based on the reflected light intensity and the time taken for the light to return to the camera. So, they can empower embedded vision applications to leverage the real-time depth and visual information to capture and use high-quality imaging data. These cameras have seen wide adoption in recent years, especially in the industrial sector, with systems like Modern Automated Guided Vehicles and Autonomous Mobile Robots playing significant roles. However, one of the biggest challenges Time-of-Flight (ToF) faces is multi-camera interference (MCI). In this blog, you’ll discover why MCI is such a big thorn in the side of ToF technology – and how you can overcome it. https://hubs.li/Q02lG6vX0

Berghof testing technology always UpToDate. 💪 👉 Interesting article on how you can test bumpers in an innovative and economical way. 😀

In this article, you'll learn why vector DSPs provide a flexible, scalable answer to the high-performance demands of #automotive #radar processing. #automotive #ADAS #autonomousdriving

Innovating sensor technology for #ADAS & #AutonomousDriving is crucial for not only driving safety but also preventing accidents & saving lives High-resolution 4D imaging radar is one of the key radar innovations that will help to meet more stringent safety standards and enable comfort functions at SAE levels L2+ and beyond. Read our whitepaper for insights on 4D Imaging #Radar: https://okt.to/XgYxC8 #FutureRadarNow #RadarTech2024

🌐 Disrupting the Auto Industry: Kelly's 50% Cost-Saving Triumph! 🚗💡 Facing a shortage of in-house tech talent, a leading automotive player turned to Kelly Technology. Dive into the case study to unveil how we reshaped their LiDAR systems, propelling them into the autonomous car market. Explore the story: https://mykelly.me/48GwtSF Ready to ignite your tech game? Let's talk innovation! 💬🔧 #AutoTechRevolution #TechInnovation