no code implementations • 17 Sep 2021 • Peide Cai, Sukai Wang, Hengli Wang, Ming Liu
We further use unsupervised contrastive representation learning as an auxiliary task to improve the sample efficiency.
no code implementations • 11 Aug 2021 • Peide Cai, Hengli Wang, Yuxiang Sun, Ming Liu
Autonomous driving in multi-agent dynamic traffic scenarios is challenging: the behaviors of road users are uncertain and are hard to model explicitly, and the ego-vehicle should apply complicated negotiation skills with them, such as yielding, merging and taking turns, to achieve both safe and efficient driving in various settings.
no code implementations • 30 Jul 2021 • Hengli Wang, Rui Fan, Peide Cai, Ming Liu
In particular, SNE-RoadSeg, our previously proposed method based on a surface normal estimator (SNE) and a data-fusion DCNN (RoadSeg), has achieved impressive performance in freespace detection.
1 code implementation • 18 Jul 2021 • Peide Cai, Hengli Wang, Huaiyang Huang, Yuxuan Liu, Ming Liu
In this work, we present a general deep imitative reinforcement learning approach (DIRL), which successfully achieves agile autonomous racing using visual inputs.
no code implementations • 18 Apr 2021 • Hengli Wang, Peide Cai, Rui Fan, Yuxiang Sun, Ming Liu
With the recent advancement of deep learning technology, data-driven approaches for autonomous car prediction and planning have achieved extraordinary performance.
no code implementations • 18 Apr 2021 • Hengli Wang, Peide Cai, Yuxiang Sun, Lujia Wang, Ming Liu
To address this problem, we propose an interpretable end-to-end vision-based motion planning approach for autonomous driving, referred to as IVMP.
no code implementations • 12 Mar 2021 • Hengli Wang, Rui Fan, Peide Cai, Ming Liu
Supervised learning with deep convolutional neural networks (DCNNs) has seen huge adoption in stereo matching.
no code implementations • 14 Dec 2020 • Rui Fan, Hengli Wang, Peide Cai, Jin Wu, Mohammud Junaid Bocus, Lei Qiao, Ming Liu
Therefore, this paper mainly explores an effective training data augmentation approach that can be employed to improve the overall DCNN performance, when additional images captured from different views are available.
no code implementations • 13 Nov 2020 • Peide Cai, Hengli Wang, Yuxiang Sun, Ming Liu
Traditional decision and planning frameworks for self-driving vehicles (SDVs) scale poorly in new scenarios, thus they require tedious hand-tuning of rules and parameters to maintain acceptable performance in all foreseeable cases.
1 code implementation • ECCV 2020 • Rui Fan, Hengli Wang, Peide Cai, Ming Liu
Freespace detection is an essential component of visual perception for self-driving cars.
no code implementations • 5 May 2020 • Peide Cai, Sukai Wang, Yuxiang Sun, Ming Liu
All-day and all-weather navigation is a critical capability for autonomous driving, which requires proper reaction to varied environmental conditions and complex agent behaviors.
1 code implementation • 27 Apr 2020 • Peide Cai, Yuxiang Sun, Hengli Wang, Ming Liu
Traditional methods for autonomous driving are implemented with many building blocks from perception, planning and control, making them difficult to generalize to varied scenarios due to complex assumptions and interdependencies.
no code implementations • 16 Apr 2020 • Tianyu Liu, Qinghai Liao, Lu Gan, Fulong Ma, Jie Cheng, Xupeng Xie, Zhe Wang, Yingbing Chen, Yilong Zhu, Shuyang Zhang, Zhengyong Chen, Yang Liu, Meng Xie, Yang Yu, Zitong Guo, Guang Li, Peidong Yuan, Dong Han, Yuying Chen, Haoyang Ye, Jianhao Jiao, Peng Yun, Zhenhua Xu, Hengli Wang, Huaiyang Huang, Sukai Wang, Peide Cai, Yuxiang Sun, Yandong Liu, Lujia Wang, Ming Liu
Moreover, many countries have imposed tough lockdown measures to reduce the virus transmission (e. g., retail, catering) during the pandemic, which causes inconveniences for human daily life.
no code implementations • 6 Jan 2020 • Peide Cai, Xiaodong Mei, Lei Tai, Yuxiang Sun, Ming Liu
Drifting is a complicated task for autonomous vehicle control.