ADAS & Autonomous Vehicle International ’s Post

Checkout how to get improved lane detection for autonomous vehicles by fine-tuning the SegFormer model from Hugging Face using the Berkeley Deep Drive Lane Detection Dataset. https://lnkd.in/gSW-Jihk #AutonomousVehicles #AI #MachineLearning #DeepLearning #SegFormer #HuggingFace #BerkeleyDeepDrive #LaneDetection #Innovation #Tech #FutureOfWork #LinkedInLearning

🚗📡 Teleoperated Driving! 🚀 Have you ever wondered how many automated vehicles one remote operator can supervise? Please check out my latest publication. I have used the new metadata functionality in the FVM software of the Functional Resonance Analysis Method (FRAM) to model the process varying different instantiations. I would love to hear your thoughts! 🚘💡 #Teleoperation #Automation #HumanFactors #SocioTechnicalSystems #FRAM #AutonomousVehicles #Research

https://lnkd.in/eMmyy6qX "The future of the HMI in autonomous vehicles is going to change a lot as we approach complete automation. HMI means Human-Machine Interface and it includes both hardware and software that allows users and devices to communicate with each other." #HMI #HumanMachineInterface #AI #ArtificialIntelligence #Telematics

𝗜𝗻𝗻𝗼𝘃𝗶𝘇 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝗶𝗲𝘀 𝗮𝗻𝗱 𝗠𝗮𝗷𝗼𝗿 𝗢𝗘𝗠 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗲 𝗼𝗻 𝗡𝗲𝘄 𝗦𝗵𝗼𝗿𝘁-𝗥𝗮𝗻𝗴𝗲 𝗟𝗶𝗗𝗔𝗥 𝗳𝗼𝗿 𝗟𝟰 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗩𝗲𝗵𝗶𝗰𝗹𝗲𝘀 The agreement between the two companies adds short-range LiDARs into the OEM’s L4 Autonomous Driving Platform Innoviz Technologies, a Tier-1 direct supplier of high-performance automotive-grade LiDAR sensors and perception software, announced a collaboration with an automotive OEM to advance its L4 autonomous vehicle capabilities. This agreement adds Innoviz's new short-range LiDARs into the OEM’s Level 4 autonomous driving platform. The Company expects the collaboration will result in a series production nomination in the coming months, pending agreement of commercial terms. Innoviz’s Short-Range LiDAR joins the InnovizTwo product platform that includes InnovizTwo Long-Range and InnovizTwo Slim. With high-resolution detection and over 90-degree vertical field of view, the new short-range design is based on the InnovizTwo Long-Range architecture. It leverages the product’s industrialization maturity and is designed to meet the requirements for light commercial vehicles, shuttles, robotaxis, and trucks. This new development phase was meticulously designed over the past six months to align with the OEM's stringent RFQ (Request for Quotation) requirements. With the OEM planning to integrate multiple short-range LiDARs for each of its L4 autonomous vehicles, the partnership is well positioned to advance a safe and powerful autonomous driving experience. "This is an exciting moment for Innoviz as we collaborate with a major global OEM on a new product,” said Omer Keilaf, CEO and Co-Founder of Innoviz Technologies. “As the L4 market is starting to ramp-up, we are happy to be able to provide a complete set of solutions to meet the needs of OEMs worldwide and support the acceleration of AV adoption across all sectors.”

🚗📡 New Paper Published! 🚀 We are excited to share our latest research: "The work organization in teleoperated driving – a human reliability and socio-technical systems perspective." In the journey towards fully autonomous vehicles, teleoperation emerges as a critical fallback for handling complex driving scenarios. Our work delves into the socio-technical complexities and human reliability aspects of teleoperated driving systems, addressing key questions such as: - How many vehicles can one operator monitor and control effectively? - What role does the operational design domain play in shaping reliability? Using analytical tools like the Functional Resonance Analysis Method (FRAM), we provide insights into the interplay between operator workload, traffic performance, and the systemic organization of teleoperation. This research highlights the intricate relationships between human factors, technology, and the broader socio-technical ecosystem. 📖 If you're interested in the detailed findings, the paper is now available in Automatisierungstechnik (Vol. 72, No. 12, 2024). Check it out here: https://lnkd.in/dZ4nAzhY Authors: Dr.-Ing. Niklas Grabbe, Stefan Brettschneider, & Klaus Bengler We would love to hear your thoughts and discuss how this research can impact the development of teleoperated driving systems! 🚘💡 #Teleoperation #Automation #HumanFactors #SocioTechnicalSystems #FRAM #AutonomousVehicles #Research

AI & SENSOR FUSION: Nullmax unveils multimodal autonomous driving technology Nullmax, an #AI firm specializing in autonomous driving, has introduced its new generation of autonomous driving technology, Nullmax Intelligence (NI), during its 2024 tech conference. This technology features vision-only, map-free and end-to-end multimodal capabilities, designed to enhance automotive intelligence. The NI System includes a multimodal model and a brain-inspired safety model, enabling vehicles to process sensory inputs similar to human perception. It outputs visual results, scene descriptions and driving behaviors. Nullmax says it aims to achieve full-scenario autonomous driving by 2025, with plans to expand AI capabilities to fields such as passenger transportation, cargo delivery and robotics. Read more here: https://lnkd.in/eJjWuwRc #AutonomousVehicles #AV #SelfDriving #ADAS #AutonomousDriving #Automotive #AutomotiveIndustry #Transportation #Technology #FutureMobility #Sustainability #AVTExpoStuttgart #AVTExpoCA #ukimediaevents

𝐓𝐨𝐤𝐞𝐧𝐢𝐳𝐞 𝐭𝐡𝐞 𝐖𝐨𝐫𝐥𝐝 𝐢𝐧𝐭𝐨 𝐎𝐛𝐣𝐞𝐜𝐭-𝐥𝐞𝐯𝐞𝐥 𝐊𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 𝐭𝐨 𝐀𝐝𝐝𝐫𝐞𝐬𝐬 𝐋𝐨𝐧𝐠-𝐭𝐚𝐢𝐥 𝐄𝐯𝐞𝐧𝐭𝐬 𝐢𝐧 𝐀𝐮𝐭𝐨𝐧𝐨𝐦𝐨𝐮𝐬 𝐃𝐫𝐢𝐯𝐢𝐧𝐠 📘 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐭𝐡𝐢𝐬 𝐩𝐚𝐩𝐞𝐫 𝐚𝐛𝐨𝐮𝐭? 🤖 First key aspect: Introduces a novel approach to enhancing autonomous driving by tokenizing the world into object-level knowledge. 📊 Second key aspect: Focuses on improving the handling of long-tail events—rare and unexpected scenarios that challenge autonomous systems. 🧠 Third key aspect: Utilizes advanced AI techniques to create detailed object-level representations that enhance the system's understanding and response to complex driving environments. 🚀 𝐖𝐡𝐲 𝐢𝐬 𝐭𝐡𝐢𝐬 𝐚 𝐛𝐫𝐞𝐚𝐤𝐭𝐡𝐫𝐨𝐮𝐠𝐡? ⏱ First reason: Provides a robust method for improving the reliability and safety of autonomous driving systems in rare and unpredictable situations. 📈 Second reason: Enhances the system's ability to accurately identify and respond to a wide variety of objects and scenarios on the road. 🌍 Third reason: Sets a new standard for addressing long-tail events in autonomous driving, promoting safer and more reliable autonomous vehicles. 🔬 𝐊𝐞𝐲 𝐅𝐢𝐧𝐝𝐢𝐧𝐠𝐬 🔧 First finding: Significant improvements in the detection and handling of long-tail events using object-level knowledge tokenization. 🧩 Second finding: Enhanced accuracy and reliability in object identification and scenario response. 🛠 Third finding: Demonstrated scalability and adaptability of the proposed approach across different driving environments and conditions. 🔍 𝐈𝐦𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐅𝐮𝐭𝐮𝐫𝐞 🌐 First implication: Advances the development of safer and more reliable autonomous driving systems. 🚗 Second implication: Facilitates better handling of rare and complex driving scenarios, improving overall road safety. 📈 Third implication: Encourages further research into object-level knowledge representation and its applications in autonomous systems. 💡 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲𝐬 🎯 First takeaway: Tokenizing the world into object-level knowledge offers a powerful method for enhancing autonomous driving safety and reliability. 🔄 Second takeaway: Addressing long-tail events is crucial for building trust and reliability in autonomous vehicle technology. 🌟 Third takeaway: Continued innovation in this area will drive advancements in autonomous driving and improve road safety for all. Excited to see how this research transforms the field of autonomous driving and enhances the safety and reliability of autonomous vehicles! 🚗🔍 #AIResearch #AutonomousDriving #MachineLearning

In the rapidly evolving landscape of artificial intelligence, autonomous driving stands at the forefront of technological innovation, promising to revolutionize the way we navigate our world. However, the journey towards fully autonomous vehicles is fraught with challenges, particularly in the realm of data collection and processing. The intricacies of capturing diverse driving scenarios, ensuring safety, and achieving seamless integration with existing infrastructure are formidable hurdles. Recognizing the critical importance of overcoming these challenges, a leading automotive company has embarked on an ambitious initiative. This case study explores their groundbreaking efforts to develop a robust data collection framework, crucial for training and refining autonomous driving systems. By leveraging cutting-edge technologies and meticulous data strategies, this initiative aims to pave the way for a safer, more efficient future in transportation. #AI #Datacollection #Autonomousdriving #cuttingedgetechnologies #Macgence #futureintransportation

Our ACME group often works on projects related to the evaluation of new technology in autonomous mobility. Based on our recent analysis, we observe the following three significant trends in the autonomous space: 🚗 Advanced AI Integration AI and Machine Learning Adoption: Over 80% of new autonomous vehicle models incorporate AI and machine learning technologies. These advanced systems play a critical role in enhancing decision-making processes and improving safety features. 📦 Autonomous Delivery Services Market Growth and Potential: The global market for autonomous delivery robots is on a significant growth trajectory, projected to reach $1.8 billion by 2024. This represents a compound annual growth rate (CAGR) of 19.2% from 2021. Autonomous delivery robots are revolutionizing the logistics and e-commerce sectors by offering efficient, reliable, and cost-effective last-mile delivery solutions. 🛣️ Infrastructure and V2X Communication Investment and Development: Investment in Vehicle-to-Everything (V2X) infrastructure is expected to exceed $5 billion by 2024. This investment focuses on enhancing traffic management and safety by enabling vehicles to communicate with each other and with infrastructure such as traffic lights, road signs, and pedestrian crossings.

🌍 Innovating in HD Mapping for Autonomous Simulation 🌍 At Automotive Artificial Intelligence (AAI), our expertise in high-definition (HD) mapping is transforming the way autonomous driving systems are tested and developed. Our specialized approach in converting HD map data into ASAM e.V. OpenDRIVE maps sets us apart in the simulation field. How We Innovate: 🚀 From HD Maps to ASAM OpenDRIVE: We leverage detailed HD map data to create precise and functional ASAM OpenDRIVE maps, crucial for simulating real-world driving environments in advanced driver-assistance systems (ADAS) testing. Currently our maps are being used by German OEMs like Volkswagen. 🚀🚀 Enhanced Simulation Accuracy: Our maps offer unparalleled detail and accuracy, providing a robust foundation for testing and validating autonomous vehicle systems. 🚀🚀🚀 Supporting Autonomous Development: By enhancing the realism of simulation scenarios, we enable more comprehensive testing and faster iteration, accelerating the path to safe and reliable autonomous vehicles. AAI is dedicated to pushing the boundaries of simulation technology, ensuring that autonomous systems are not only innovative but also aligned with the highest safety standards. Join us as we drive the future of mobility! #HDMapping #AI #AutonomousVehicles #AutomotiveInnovation #AAI #SimulationTechnology #TechLeadership