EMMA: End-to-End Multimodal Model for Autonomous Driving blog: https://waymo.com/blog/2024/10/introducing-emma/ paper: https://storage.googleapis.com/waymo-uploads/files/research/EMMA-paper.pdf 使用…
Uncertainty-aware Short-term Motion Predictionof Traffic Actors for Autonomous Driving ...
large model-enabled smart driving solution based on the latest generation A2000 chip from the Huashan series. This innovative end-to-end architecture and high-performance autonomous driving SoC (System on a Chip) is designed to power next-generation autonomous driving applications across various ...
We introduce EMMA, an End-to-end Multimodal Model for Autonomous driving. Built on a multi-modal large language model foundation, EMMA directly maps raw camera sensor data into various driving-specific outputs, including planner trajectories, perception objects, and road graph elements. EMMA maximizes...
Xiao, Y., Codevilla, F., Gurram, A., Urfalioglu, O., López, A.M.: Multimodal end-to-end autonomous driving. arXiv:1906.03199 (2019) 1.Mohanapriya, D., Mahesh, K.: Chapter 5—an efficient framework for object tracking in video surveillance. In: The Cognitive Approach in Cloud Comp...
Nullmax Intelligence addresses the challenges of autonomous driving with a smarter, more human-like approach. Beyond visual input, NI supports auditory, textual, and gestural inputs, utilizing a multimodal end-to-end model for task inference.Photo...
An apparatus for autonomous vehicles includes a perception pipeline having independent classification processes operating in parallel to respectively identify objects belonging to a specific object type based on sensor data flows from multiple ones of a plurality of different types of sensors. The ...
foundation models or building new ones from the ground up. These models create realistic synthetic videos of environments and interactions, providing a scalable foundation for training complex systems, from simulating humanoid robots performing advanced actions to developing end-to-end autonomous driving ...
Since the advent of Multimodal Large Language Models (MLLMs), they have made a significant impact across a wide range of real-world applications, particularly in Autonomous Driving (AD). Their ability to process complex visual data and reason about intricate driving scenarios has paved the way ...
Multimodal image fusion and object detection are crucial for autonomous driving. While current methods have advanced the fusion of texture details and semantic information, their complex training processes hinder broader applications. Addressing this challenge, we introduce E2E-MFD, a novel end-to-end ...