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#ValidateWithQuanser This paper delves into the challenges of ensuring stability (in some sense) and robustness in large-scale second-order consensus networks (SOCNs) and autonomous vehicle platoons in the discrete-time domain. It proposes a graph-theoretic methodology for designing a state feedback law for these systems in a discrete-time framework. In the end, the article concludes with simulation and experimental results (using Quanser's QLabs and QCars) that show that the proposed framework is very effective in predicting the behaviour of vehicle platoons, even for more complex agents with nonlinear dynamics. Find the full article here: https://lnkd.in/ddBwHgtG #robustness #stabilityanalysis #graphtheory #trajectory #gaussiannoise #vehicledynamics #northeasternuniversity

🚀 Excited to share two recent paper acceptances at top conferences! 1️⃣ RTSS 2024: Our paper, "RT-BEV: Enhancing Real-Time BEV Perception for Autonomous Vehicles", co-authored with Jinkyu Lee and Kang Shin, introduces a framework that co-designs message synchronization with detection using environment-aware Regions of Interest (ROIs). This approach significantly reduces end-to-end latency in Bird's Eye View (BEV) perception for autonomous vehicles, without sacrificing accuracy. 2️⃣ ICCAD 2024: Our paper, "AyE-Edge: Automated Deployment Space Search for Real-Time Object Detection on the Edge", co-authored with Chao Wu*, Yifan Gong*, Liangkai Liu* (co-first authors), Mengquan Li, Yushu Wu, Xuan Shen, Zhimin Li, Geng Yuan, Weisong Shi, and Yanzhi Wang, focuses on co-designing the configurations for camera keyframe selection, model compression, and DVFS (Dynamic Voltage and Frequency Scaling) driver. This approach achieves better power efficiency while maintaining real-time object detection on edge devices. Big thanks to my amazing co-authors and collaborators for making this possible! Stay tuned for more updates! #RTSS2024 #ICCAD2024 #AutonomousVehicles #RealTime #PowerEfficiency #BEVPerception #ObjectDetection #Research

🚦👉 𝗥𝗼𝘂𝗻𝗱𝘁𝗮𝗯𝗹𝗲 𝗗𝗶𝘀𝗰𝘂𝘀𝘀𝗶𝗼𝗻: Gilles Gallee represents #Ansys at #IAAtransportation panel discussion on #Autonomous driving virtual #validation and #AI integration. 𝗤𝘂𝗼𝘁𝗲: The core technology is the ability to simulate millions of #scenarios at scale within a reasonable time frame. Gilles Gallee, Director of AV #Simulation Solution, Ansys https://ansys.me/401Cb0n #ADASAD #ADASPerceptionSystems #AVxcelerate #Autonomy

Discover how Gaussian Splatting revolutionizes AV simulation! This cutting-edge technique enhances the realism of autonomous vehicle simulations by efficiently rendering complex environments with high fidelity. Learn how Gaussian Splatting can improve safety and performance in autonomous driving technology. #gaussiansplatting #avsimulation #autonomousvehicles #selfdrivingcars #simulation #automotive #machinelearning #deeplearning #computervision #technology #rendering #highfidelity #complexenvironments #cuttingedge #futureoftransportation #autonomousdriving #ai #artificialintelligence #vehicletechnology #simulationtechnology

Today we're announcing PRISM-1 🔺 a breakthrough in dynamic scene reconstruction. This entire video is *simulated* — for the first time including dynamic and deformable features like cyclists which was previously the big blocker to simulating the long-tail of edge cases. You can even see fine grained structures and complex reflections in the puddles. This is end-to-end simulation for end-to-end autonomous driving. More details in our blog here: https://lnkd.in/eiZ-3s5E

article of the day# Design and Assurance of Safety-Critical Systems with Artificial Intelligence in FPGAs: The Safety ArtISt Method and a Case Study of an FPGA-Based Autonomous Vehicle Braking Control System #FPGA #artificialintelligence #safetycriticalsyatems

With a lot of focus on AI currently, synthetic environments driven by Modelling & Simulation are, and will be, required to train AI systems. This is why Wayve's technology is a game changer for self driving cars. CFMS's Modelling & Simulation, and synthetic environments are doing the same in the #Space, #Aerospace, #Energy, #Manufacturing & #Construction and #Rail sectors by transforming the level of AI and robotics that can be applied to these industries (where real world data is often lacking). Get in touch to find out more. #inspection #AI #digitaltwin #autonomoussystems

🎉 Excited to share my first paper with Fabian Flohr on my work at the Intelligent Vehicles Lab! 📚 🚗 Title: "A Review of Trajectory Prediction Methods for the Vulnerable Road User" This review paper delves into the crucial role of predicting the movement of vulnerable road users (VRUs) in the realm of autonomous driving. We explore the cutting-edge Deep-Learning techniques that are redefining trajectory prediction for VRUs. The paper categorizes these methods based on their prediction schemes and provides a comparative analysis of their performance on leading datasets. Additionally, we give a comprehensive overview of possible input and output representations and modeling possibilities for state and context. 👁 Looking Ahead: We also discuss exciting potential directions for future research, aiming to further enhance autonomous driving technology and VRU safety. 🔗 Link to the paper: https://lnkd.in/dZaRGshb #PhD #AutonomousVehicles #DeepLearning #TrajectoryPrediction

ML#1 Probability Mass Function (pmf) vs Probability Density Function (pdf) When dealing with a finite sample space, we can easily calculate the probability of each sample with a value between 0 and 1, known as PMF. However, in scenarios like robotic arm movements or autonomous vehicles responding to voice commands, where the sample space is infinite, calculating the probability of an event yields a value of zero. This is where the concept of probability density function (PDF) comes into play, enabling the calculation of probabilities for events within a subset of the sample or a range of a function. Key points: 1. PMF: discrete random variables, PDF: continuous random variables. 2. For pdf, it's essential that the subsets are valid sets, defined by Lebesgue measure (or integral), or simply integrable for the pdf to be well-defined. P.S. Remember, the "random" vectors in the sample space are always deterministic, it's the environment that is stochastic.

I'm going back to Stuttgart for the third year in a row. The main reason is that we have interesting discussions every year—hopefully, that will be the case again in 2024. If you are around and you are interested in industry-leading simulation or data tools, stop by and say hi. #aiMotive #AVTE #Stuttgart #AutomatedDriving #AI #Simulation #Data