Zobrazeno 1 - 10
of 270
pro vyhledávání: '"Cao, Yulong"'
Autor:
Cho, Minkyoung, Cao, Yulong, Sun, Jiachen, Zhang, Qingzhao, Pavone, Marco, Park, Jeong Joon, Yang, Heng, Mao, Z. Morley
An important paradigm in 3D object detection is the use of multiple modalities to enhance accuracy in both normal and challenging conditions, particularly for long-tail scenarios. To address this, recent studies have explored two directions of adapti
Externí odkaz:
http://arxiv.org/abs/2410.12592
Autor:
Huang, Zhiyu, Weng, Xinshuo, Igl, Maximilian, Chen, Yuxiao, Cao, Yulong, Ivanovic, Boris, Pavone, Marco, Lv, Chen
Autonomous driving necessitates the ability to reason about future interactions between traffic agents and to make informed evaluations for planning. This paper introduces the \textit{Gen-Drive} framework, which shifts from the traditional prediction
Externí odkaz:
http://arxiv.org/abs/2410.05582
Autor:
Tan, Shuhan, Ivanovic, Boris, Chen, Yuxiao, Li, Boyi, Weng, Xinshuo, Cao, Yulong, Krähenbühl, Philipp, Pavone, Marco
Simulation stands as a cornerstone for safe and efficient autonomous driving development. At its core a simulation system ought to produce realistic, reactive, and controllable traffic patterns. In this paper, we propose ProSim, a multimodal promptab
Externí odkaz:
http://arxiv.org/abs/2409.05863
Autor:
Cho, Jang Hyun, Ivanovic, Boris, Cao, Yulong, Schmerling, Edward, Wang, Yue, Weng, Xinshuo, Li, Boyi, You, Yurong, Krähenbühl, Philipp, Wang, Yan, Pavone, Marco
Multi-modal large language models (MLLMs) have shown incredible capabilities in a variety of 2D vision and language tasks. We extend MLLMs' perceptual capabilities to ground and reason about images in 3-dimensional space. To that end, we first develo
Externí odkaz:
http://arxiv.org/abs/2405.03685
With the fast development of large language models (LLMs), LLM-driven Web Agents (Web Agents for short) have obtained tons of attention due to their superior capability where LLMs serve as the core part of making decisions like the human brain equipp
Externí odkaz:
http://arxiv.org/abs/2402.16965
Simulation plays a crucial role in the development of autonomous vehicles (AVs) due to the potential risks associated with real-world testing. Although significant progress has been made in the visual aspects of simulators, generating complex behavio
Externí odkaz:
http://arxiv.org/abs/2312.13303
The quest for fully autonomous vehicles (AVs) capable of navigating complex real-world scenarios with human-like understanding and responsiveness. In this paper, we introduce Dolphins, a novel vision-language model architected to imbibe human-like ab
Externí odkaz:
http://arxiv.org/abs/2312.00438
Deep neural networks (DNNs) are increasingly integrated into LiDAR (Light Detection and Ranging)-based perception systems for autonomous vehicles (AVs), requiring robust performance under adversarial conditions. We aim to address the challenge of LiD
Externí odkaz:
http://arxiv.org/abs/2310.14504
In light of the challenges and costs of real-world testing, autonomous vehicle developers often rely on testing in simulation for the creation of reliable systems. A key element of effective simulation is the incorporation of realistic traffic models
Externí odkaz:
http://arxiv.org/abs/2309.00709
Autor:
Zhong, Ziyuan, Rempe, Davis, Chen, Yuxiao, Ivanovic, Boris, Cao, Yulong, Xu, Danfei, Pavone, Marco, Ray, Baishakhi
Realistic and controllable traffic simulation is a core capability that is necessary to accelerate autonomous vehicle (AV) development. However, current approaches for controlling learning-based traffic models require significant domain expertise and
Externí odkaz:
http://arxiv.org/abs/2306.06344