no code implementations • 4 Apr 2024 • Juanwu Lu, Can Cui, Yunsheng Ma, Aniket Bera, Ziran Wang
In this paper, we propose the Sequential Neural Variational Agent (SeNeVA), a generative model that describes the distribution of future trajectories for a single moving object.
no code implementations • 10 Mar 2024 • Juanwu Lu, Wei Zhan, Masayoshi Tomizuka, Yeping Hu
For interpretability, the model achieves target-driven motion prediction by estimating the spatial distribution of long-term destinations with a variational mixture of Gaussians.
no code implementations • 14 Dec 2023 • Can Cui, Zichong Yang, Yupeng Zhou, Yunsheng Ma, Juanwu Lu, Lingxi Li, Yaobin Chen, Jitesh Panchal, Ziran Wang
Autonomous driving systems are increasingly popular in today's technological landscape, where vehicles with partial automation have already been widely available on the market, and the full automation era with "driverless" capabilities is near the horizon.
1 code implementation • 7 Dec 2023 • Yunsheng Ma, Can Cui, Xu Cao, Wenqian Ye, Peiran Liu, Juanwu Lu, Amr Abdelraouf, Rohit Gupta, Kyungtae Han, Aniket Bera, James M. Rehg, Ziran Wang
Autonomous driving (AD) has made significant strides in recent years.
1 code implementation • 21 Nov 2023 • Can Cui, Yunsheng Ma, Xu Cao, Wenqian Ye, Yang Zhou, Kaizhao Liang, Jintai Chen, Juanwu Lu, Zichong Yang, Kuei-Da Liao, Tianren Gao, Erlong Li, Kun Tang, Zhipeng Cao, Tong Zhou, Ao Liu, Xinrui Yan, Shuqi Mei, Jianguo Cao, Ziran Wang, Chao Zheng
We first introduce the background of Multimodal Large Language Models (MLLMs), the multimodal models development using LLMs, and the history of autonomous driving.
1 code implementation • 25 Oct 2023 • Yunsheng Ma, Juanwu Lu, Can Cui, Sicheng Zhao, Xu Cao, Wenqian Ye, Ziran Wang
We approach this objective by identifying the key challenges of shifting from single-agent to cooperative settings, adapting the model by freezing most of its parameters and adding a few lightweight modules.
1 code implementation • 27 May 2023 • Can Cui, Yunsheng Ma, Juanwu Lu, Ziran Wang
Sensor fusion is a crucial augmentation technique for improving the accuracy and reliability of perception systems for automated vehicles under diverse driving conditions.
no code implementations • 6 Aug 2022 • Juanwu Lu, Wei Zhan, Masayoshi Tomizuka, Yeping Hu
Results show significant performance degradation due to domain shift, and feature attribution provides insights to identify potential causes of these problems.
no code implementations • 10 Mar 2021 • Chen Chai, Juanwu Lu, Xuan Jiang, Xiupeng Shi, Zeng Zeng
An AutoML method based on XGBoost, termed AutoGBM, is built as the classifier for prediction and feature ranking.