Remember in connect-the-dots, where the more you look, the more you score? The same principle applies to motion prediction in autonomous driving, too! Check our #CVPR2024 paper “SmartRefine: A Scenario-Adaptive Refinement Framework for Efficient Motion Prediction” by Yang Zhou, Hao Shao, Letian Wang, Steven Lake Waslander, Hongsheng Li, Yu Liu. By this work, we outperform all published ensemble-free works on the Argoverse 2 leaderboard (single agent track) at the submission of the paper. Our key insight is that, motion prediction models confront various driving scenarios, and each comes with different difficulties, thus the refinement potential in different scenarios is not uniform. In this work, we introduce SmartRefine, a novel approach to refining motion predictions with minimal additional computation by leveraging scenario-specific properties and adaptive refinement iterations. Abstract: Predicting the future motion of surrounding agents is essential for autonomous vehicles (AVs) to operate safely in dynamic, human-robot-mixed environments. Context information, such as road maps and surrounding agents' states, provides crucial geometric and semantic information for motion behavior prediction. To this end, recent works explore two-stage prediction frameworks where coarse trajectories are first proposed, and then used to select critical context information for trajectory refinement. However, they either incur a large amount of computation or bring limited improvement, if not both. In this paper, we introduce a novel scenario-adaptive refinement strategy, named SmartRefine, to refine prediction with minimal additional computation. Specifically, SmartRefine can comprehensively adapt refinement configurations based on each scenario's properties, and smartly chooses the number of refinement iterations by introducing a quality score to measure the prediction quality and remaining refinement potential of each scenario. SmartRefine is designed as a generic and flexible approach that can be seamlessly integrated into most state-of-the-art motion prediction models. Experiments on Argoverse (1 & 2) show that our method consistently improves the prediction accuracy of multiple state-of-the-art prediction models. Specifically, by adding SmartRefine to QCNet, we outperform all published ensemble-free works on the Argoverse 2 leaderboard (single agent track) at submission. Comprehensive studies are also conducted to ablate design choices and explore the mechanism behind multi-iteration refinement. Paper: https://lnkd.in/g4SPxRDE Code: https://lnkd.in/g3YysfSH #CVPR2024 #autonomousdriving #autonomousvehicles #selfdrivingcars #reinforcementlearning #deeplearning #motionprediction
Toronto Robotics and Artificial Intelligence Laboratory’s Post
More Relevant Posts
-
Prototyping Manager at AWS | Helping the World’s Largest Enterprises Explore Generative AI, Machine Learning and other Emerging Technologies on AWS.
📘 Fascinating Read: How Generative AI is Revolutionizing Autonomous Vehicle Safety at Zoox 🚀 Excited to share this insightful article from Amazon Science about how Scenario Diffusion, a powerful generative AI technique, is being used by Zoox to dramatically improve the safety and navigation capabilities of their autonomous vehicles. 🤝 This collaboration is particularly exciting because Scenario Diffusion was jointly developed by researchers from Amazon and Zoox. This close partnership highlights the power of collaboration in accelerating innovation within generative AI, especially when tackling complex challenges like autonomous vehicle safety. Here are some key takeaways that resonated with me: 🌐 Generating diverse and realistic scenarios: Scenario Diffusion allows Zoox to create an almost limitless number of complex and unpredictable driving situations for their vehicles to train on. This is crucial for ensuring they can handle the unexpected on the road. 🧠 Learning from human experts: The model incorporates insights from experienced human drivers to make its responses even more realistic and nuanced. This is a great example of how generative AI can be used to augment, not replace, human expertise. 📈 Scaling safety testing: By automating the generation of test scenarios, Zoox can significantly increase the amount of testing their vehicles undergo, making them safer and more reliable. This is a major step forward for the autonomous vehicle industry. Overall, I'm incredibly impressed with the work Zoox and Amazon are doing with Scenario Diffusion. It's a powerful example of how generative AI can be used to make our roads safer and pave the way for a future of autonomous transportation. 💭 What are your thoughts on this article and the potential of generative AI for autonomous vehicle safety? 🔗 Scenario Diffusion helps Zoox vehicles navigate safety-critical situations https://buff.ly/3UNirLi #AI #autonomousvehicles #Zoox #generativeAI #ScenarioDiffusion #safety #transportation #futureofmobility
-
Scenario Diffusion uses generative AI to navigate safety-critical situations in autonomous vehicles by creating realistic driving scenarios to enhance safety testing, and ensure the reliability of the company’s robotaxis across diverse environments. Learn more about the method from Zoox. Paper: https://lnkd.in/e9n4iVBF Blog post: https://lnkd.in/eNnuQM4e #GenerativeAI #AutonomousVehicles #ML #Zoox
Scenario Diffusion helps Zoox vehicles navigate safety-critical situations
amazon.science
-
The automated creation of synthetic traffic scenarios is integral to validating the safety of autonomous vehicles. We met with Amazon Science to discuss a paper we presented at the 2023 Conference on Neural Information Processing Systems (NeurIPS) where we address this with a method we call Scenario Diffusion. Kai Wang Ethan Pronovost Nicholas Roy Meghana Reddy Ganesina Nour Hendy Andres Morales #GenerativeAI #MachineLearning #Simulation #Robotaxi #AutonomousVehicles
Scenario Diffusion helps Zoox vehicles navigate safety-critical situations
amazon.science
-
The biggest hurdle to the goal of fully autonomous vehicles is in the economics. #AVs #Automotive #Scalability
Navigating the Scaling Challenge in AV Development
https://www.eetimes.com
-
Graduate Research Assistant | Autonomous Driving | Mobile Robotics | CDL Class B License holder
During my last semester, I had the opportunity to delve into the fascinating world of Autonomous Vehicles research. In this Github-hosted website, I have analyzed the biggest challenges faced by the AV industry today and the strides taken by researchers from top institutions and companies like Waymo and Cruise. The website examines key areas such as: 🔶 Reducing dependence on expensive Lidars by leveraging AI 🧠 🔶 Generating novel scenarios for training AD agents 🔶 Connected AVs 🔶 Auto-labeling of AV datasets 🔶 AI-powered sensor fusion for AVs I would love to hear your thoughts on this cutting-edge research. Check out the link to the article below! Link: https://lnkd.in/gAFdb7Xt #autonomousvehicles #ai #robotics
Mini Project 1 - CMSC818B Decisioin Making for Robotics
muditsingal.github.io
-
NEW COLLABORATION📣: 𝗘𝗻𝗮𝗯𝗹𝗶𝗻𝗴 𝗥𝗲𝗮𝗹𝗶𝘀𝘁𝗶𝗰 𝗦𝗶𝗺𝘂𝗹𝗮𝘁𝗶𝗼𝗻𝘀 𝗳𝗼𝗿 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗩𝗲𝗵𝗶𝗰𝗹𝗲𝘀 Autonomous vehicles (AVs) hold the promise of forever changing how we use cars, but before driverless cars become mainstream, we need to ensure they are safe. One of the most difficult challenges to overcome for AV and advanced driver assistance systems (ADAS) developers is the “simulation-to-real” gap, or how virtual training and testing conditions differ from the real world. One of the most promising solutions to this problem is using advanced AI behaviour models to simulate realistic human actions. This project will aim to form a consortium of innovators from across the sector to advance the development and adoption of behavioural models for simulation. In the first stage, the team will integrate Inverted AI’s modelling solutions into CARLA’s platform, one of the world’s leading open-source autonomous driving simulators, to launch an interoperable, scalable solution for commercial and academic research. This project is actively seeking new project partners to create a world-class human behavioural simulation consortium of autonomous vehicle developers and self-driving car manufacturers. Interested organizations are encouraged to click the link for more information. Learn more about the collaboration here: https://bit.ly/3Yirl3N Inverted AI | CARLA simulator
-
-
Senior Engineer @ Arnold NextG GmbH | PhD (DL & Autonomous Driving) @ UAH | IEEE ITS Best PhD Dissertation Award 2024
Good approach to explore digital twin and human-centric simulation.
NEW COLLABORATION📣: 𝗘𝗻𝗮𝗯𝗹𝗶𝗻𝗴 𝗥𝗲𝗮𝗹𝗶𝘀𝘁𝗶𝗰 𝗦𝗶𝗺𝘂𝗹𝗮𝘁𝗶𝗼𝗻𝘀 𝗳𝗼𝗿 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗩𝗲𝗵𝗶𝗰𝗹𝗲𝘀 Autonomous vehicles (AVs) hold the promise of forever changing how we use cars, but before driverless cars become mainstream, we need to ensure they are safe. One of the most difficult challenges to overcome for AV and advanced driver assistance systems (ADAS) developers is the “simulation-to-real” gap, or how virtual training and testing conditions differ from the real world. One of the most promising solutions to this problem is using advanced AI behaviour models to simulate realistic human actions. This project will aim to form a consortium of innovators from across the sector to advance the development and adoption of behavioural models for simulation. In the first stage, the team will integrate Inverted AI’s modelling solutions into CARLA’s platform, one of the world’s leading open-source autonomous driving simulators, to launch an interoperable, scalable solution for commercial and academic research. This project is actively seeking new project partners to create a world-class human behavioural simulation consortium of autonomous vehicle developers and self-driving car manufacturers. Interested organizations are encouraged to click the link for more information. Learn more about the collaboration here: https://bit.ly/3Yirl3N Inverted AI | CARLA simulator
-
-
The system comprises a novel ML architecture based on latent diffusion, an ML technique used in image generation in which a model learns to convert random noise into detailed images: https://lnkd.in/gfAwvxmJ #AmazonScience #Zoox #GenerativeAI #LLMs #ML #GenAI
Using generative AI, Scenario Diffusion crafts realistic, complex driving scenarios for its robotaxi. Learn why researchers at Zoox believe this technology will become "foundational to the future of safety validation for autonomous vehicles." #GenerativeAI #AutonomousRobots
Scenario Diffusion helps Zoox vehicles navigate safety-critical situations
amazon.science
-
Using generative AI, Scenario Diffusion crafts realistic, complex driving scenarios for its robotaxi. Learn why researchers at Zoox believe this technology will become "foundational to the future of safety validation for autonomous vehicles." #GenerativeAI #AutonomousRobots
Scenario Diffusion helps Zoox vehicles navigate safety-critical situations
amazon.science
-
Modest | Driven App Developer | Delivering Engaging Mobile Experiences | Empowering Businesses with Powerful Online Solutions
Integrating AI and Autonomous Vehicles: A Path to Smarter Transportation Autonomous vehicles (AVs) hold the promise of revolutionizing transportation, and recent research from Purdue University highlights a key advancement: using large language models like ChatGPT to enhance AVs' ability to understand passenger commands. This innovation aligns with broader goals of creating efficient, intuitive, and responsive transportation systems that cater to individual needs. By leveraging AI, AVs can interpret commands such as “I’m in a hurry” and take the most efficient route, combining personalization with safety. At every stage of AV development, AI integration fosters innovation, ensuring these systems continuously improve in real-time decision-making and user interaction. The importance of refining these models is critical, from enhancing command interpretation to adapting to complex environments. Encouraging responsible AI usage ensures that the technology addresses real-world challenges while minimizing risks like algorithmic "hallucination." Education plays a crucial role in this evolution. As AV technology grows more sophisticated, there is an increasing need to equip future engineers and researchers with the skills to design, build, and manage these systems. By investing in educational programs focused on AI, machine learning, and autonomous systems, we can create a workforce prepared to tackle the intricacies of these technologies. Collaboration between industry, academia, and policymakers will also be essential in driving AV innovation forward. Purdue's study, for example, showcases how partnerships can push research and provide a foundation for safe and efficient AV deployment. Such collaborations are key to ensuring that as AVs become more prevalent, they adhere to best practices and regulatory standards. Embracing AI in AVs not only contributes to transportation progress but also aligns with broader societal goals—making transport more accessible, efficient, and sustainable for all. #snsinstitutions #snsdesignthinkers #designthinking
-
