Auterion’s Post

New Embedded Vision Summit Demo Video: "INUITIVE Demonstration of the M4.51 Depth and AI Sensor Module Based on the NU4100 Vision Processor"

ℹ #AIthenaProject 📍 In interactive #urban #traffic #environments, vehicles, pedestrians and other road users, navigate highly complex road networks under a variety of environmental conditions while interacting with different types of road users. ✅ In the #AIthenaProject Use Case 2 ▶ AI extended Situational Awareness/Understanding ◀ information from perception layers,  communications and map information is fused into #Local #Dynamic #Maps (LDM) to link #layers (from static to dynamic) to achieve accurate and complete knowledge of the scene. Find out more about the #Use #Case 2 on the #AIthenaProject 🔗 https://lnkd.in/eVv35_hA Virtual Vehicle Research GmbH I Siemens #BE Siemens Digital Industries Software I TTTech TTTech Auto I Infineon Technologies I Continental I Continental Engineering Services I Eindhoven University of Technology I Vicomtech

embedded world: Altera optimizes FPGAs for edge AI - Embedded.com https://buff.ly/43VrrQY

Digital twins are going to be essential for scaling simulation to scale AI for use cases that interact with humans and the real world environment. The automotive industry is pioneering the capture and use of data on and adjacent to public roads, and we'll soon see the same piece of road captured and simulated many times, and then briefly recaptured as vehicles pass through in the real world. I wonder if there's a use case for identifying the changes in digital twins based on local capture and measurement? This would enable the underlying environments used to simulate to be kept up to date, and to also underpin prediction models on environment changes. #digitaltwin #simulation #artificialintelligence

🎉 Excited to share that the work we have been working on for the past two years got accepted to #ECCV2024, the European Conference on Computer Vision. Our work #JDT3D (joint 3D object detection and tracking) aims to address the gaps in the current #Transformer based end-to-end detection and tracking architecture for #autonomousdriving. When I started my master, I strongly believed that object detection shouldn’t be done just on single frames, but should leverage the past frames of detection as much as possible while performing object tracking jointly in an implicit way. In this work, we took inspiration from the #Transformer (the now famous architecture in natural language processing), adopted it to 3D #LiDAR-based computer vision, and proposed several key optimization and training techniques (track sampling augmentation, confidence-based query propagation) to bridge the performance gap with the traditional two-step data association approaches. This work wouldn’t have been possible without the diligence and preseverance of Brian Cheong who is still working on further improvements in this area, and the unwavering support and supervision from Professor Steven Lake Waslander. As a commitment to the research community, we are preparing to open-source our work, so stay tuned by following us at Toronto Robotics and Artificial Intelligence Laboratory. Check out the full paper in detaills linked below. Paper arxiv link: https://lnkd.in/eVEBWU7S #autonomousdriving #computervision #lidar #objectdetection #multiobjecttracking #robotics #artificialintelligence

AntiFake, Your Shield Against Synthesized Speech The AntiFake tool, created by the McKelvey School of Engineering at Washington University in St. Louis, serves as an innovative defense mechanism specifically engineered to prevent unauthorized speech synthesis proactively. Learn More about #AI Tech:  https://o-trim.co/ecohub X: https://o-trim.co/XTwitter https://lnkd.in/gRXRADiT #ONPASSIVE #ONPASSIVEECOSYSTEM #Techtrend #AI #research #robotics #technology #technews #TheFutureOfInternet #speechsynthesis

Wonders and Dangers of new tech https://lnkd.in/gSe7ejzH     With the type of scientific breakthroughs almost daily, we do not need sci-fi We are living in sci-fi become real world!   ‘Scientists have created jellyfish-shaped robots that can be steered through underwater obstacle courses using only light. This research has the potential to revolutionize #drug delivery systems and fluid manipulation in lab-on-a-chip devices.’ On the other hand, we also hear NHS UK is unable to see patients, as due to MS breakdown, patients records can’t be retrieved and people are stranded all over the world with flight cancellations.   As Technology takes over the world, are we becoming slaves to it?   Also if few lines of code from Crowdstrike (what a name!) software can do this, what happens to the world if #Internet is down for a few moments-leave alone days! With hackers adopting AI faster than us, is this doomsday near us?   Is that the biggest #Blackswan (Grey Rhino?) existential threat facing us all!  

🚀 Exciting Breakthrough in LiDAR-Based Joint Detection and Tracking! 🚀 We're thrilled to share our latest research from the University of Toronto that has been accepted to #ECCV2024: "JDT3D: Addressing the Gaps in LiDAR-Based Tracking-by-Attention" by Brian Cheong, Jiachen (Jason) Zhou and Steven Lake Waslander. In computer vision, approaches trained in an end-to-end manner have been shown to perform better than traditional pipeline-based methods. However, within LiDAR-based object tracking, tracking-by-detection continues to achieve state-of-the-art performance without learning both tasks jointly. In our work, we explore the potential reasons for this gap and propose techniques to leverage the advantages of joint detection and tracking. 🌟 Key Highlights: - Innovative Approach: We propose JDT3D, a novel LiDAR-based joint detection and tracking model that leverages transformer-based decoders to propagate object queries over time, implicitly performing object tracking without an association step at inference. - Enhanced Techniques: We introduce track sampling augmentation and confidence-based query propagation to bridge the performance gap between tracking-by-detection (TBD) and joint detection and tracking (JDT) methods. - Real-World Impact: Our model is trained and evaluated on the nuScenes dataset, showcasing significant improvements in tracking accuracy and robustness. Check out the full paper linked below and join us at #ECCV2024! Paper: https://lnkd.in/gbT4EStA Code: https://lnkd.in/gU385pcW #autonomousdriving #computervision #tracking #objecttracking #robotics #3DVision #transformers #deeplearning

𝗣𝗿𝗼𝗴𝗿𝗲𝘀𝘀 𝗶𝗻 𝘃𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗶𝗻𝗴 𝘁𝗵𝗲 𝗿𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝘀𝗵𝗶𝗽 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝗱𝗶𝗴𝗶𝘁𝗮𝗹 𝘁𝘄𝗶𝗻𝘀 𝗮𝗻𝗱 𝗽𝗵𝘆𝘀𝗶𝗰𝗮𝗹 𝗺𝗼𝗱𝗲𝗹𝘀    We are pleased to share the results presented by Felix Stocker in his recent talk “Advanced Methodology for Correlating Digital Twins in Passive Vehicle Safety" at crash.tech conference on 13th March at Technische Hochschule Ingolstadt. The international conference always provides an excellent overview of the latest innovations and upcoming challenges in the field of passive safety. Felix’s presentation revolved around methodology for correlating digital twins with physical models in passive vehicle safety 𝘂𝘀𝗶𝗻𝗴 𝗮𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲. The talk described a process that involves capturing expert knowledge through interviews to 𝗱𝗲𝘃𝗲𝗹𝗼𝗽 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 𝗰𝗿𝗶𝘁𝗲𝗿𝗶𝗮 and classification catalogues. A machine learning model was then trained with simulation data and expert ratings to enable 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝘃𝗮𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻 𝗼𝗳 𝘃𝗶𝗿𝘁𝘂𝗮𝗹 𝗺𝗼𝗱𝗲𝗹𝘀. This innovative approach was tested in the validation of WorldSID 50M crash test dummy models. It highlights how expert knowledge can be integrated into machine learning to 𝗰𝗼𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝘁𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗼𝗯𝗷𝗲𝗰𝘁𝗶𝘃𝗲 𝗰𝗼𝗿𝗿𝗲𝗹𝗮𝘁𝗶𝗼𝗻 𝗺𝗲𝘁𝗵𝗼𝗱𝘀.   Keep up to date with the Applus+ IDIADA Germany's advances in vehicle safety technology by subscribing to our LinkedIn channel, or contact us at [email protected].   #ApplusIDIADA #IDIADAGermany #Automotive #VehicleSafety #PassiveSafety #DigitalTwins #PhysicalModels #CorrelationMethods #ArtificalIntelligence #MachineLearning #CrashTest #Dummy #WorldSID #AI

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