no code implementations • 22 Apr 2024 • Lei He, Leheng Li, Wenchao Sun, Zeyu Han, Yichen Liu, Sifa Zheng, Jianqiang Wang, Keqiang Li
To the best of our knowledge, this is the first survey specifically focused on the applications of NeRF in the Autonomous Driving domain.
no code implementations • 16 Apr 2024 • Yuning Wang, Zhiyuan Liu, Haotian Lin, Junkai Jiang, Shaobing Xu, Jianqiang Wang
In this study, we propose PreGSU, a generalized pre-trained scene understanding model based on graph attention network to learn the universal interaction and reasoning of traffic scenes to support various downstream tasks.
no code implementations • 13 Mar 2024 • Zikun Xu, Jianqiang Wang, Shaobing Xu
To this end, this paper proposes UniLiDAR, an occupancy prediction pipeline that leverages geometric realignment and semantic label mapping to facilitate multiple datasets training and mitigate performance degradation during deployment on heterogeneous platforms.
no code implementations • 5 Sep 2023 • Ji-An Pan, Qing Xu, Keqiang Li, Chunying Yang, Jianqiang Wang
This article is devoted to addressing the cloud control of connected vehicles, specifically focusing on analyzing the effect of bi-directional communication-induced delays.
no code implementations • 4 Sep 2023 • Shuai Li, Haotian Zheng, Jiawei Wang, Chaoyi Chen, Qing Xu, Jianqiang Wang, Keqiang Li
In mixed traffic where human-driven vehicles (HDVs) also exist, existing research mostly focuses on "looking ahead" (i. e., the CAVs receive information from preceding vehicles) strategies for CAVs, while recent work reveals that "looking behind" (i. e., the CAVs receive information from their rear vehicles) strategies might provide more possibilities for CAV longitudinal control.
no code implementations • 29 Jun 2023 • Yuning Wang, Zeyu Han, Yining Xing, Shaobing Xu, Jianqiang Wang
Autonomous vehicles (AV) are expected to reshape future transportation systems, and decision-making is one of the critical modules toward high-level automated driving.
no code implementations • 7 Jun 2023 • Zeyu Han, Jiahao Wang, Zikun Xu, Shuocheng Yang, Lei He, Shaobing Xu, Jianqiang Wang, Keqiang Li
In an effort to bridge this gap and stimulate future research, this paper presents an exhaustive survey on the utilization of 4D mmWave radar in autonomous driving.
no code implementations • 22 Mar 2023 • Jianqiang Wang, Dandan Ding, Zhan Ma
With this aim, we extensively exploit cross-scale, cross-group, and cross-color correlations of point cloud attribute to ensure accurate probability estimation and thus high coding efficiency.
no code implementations • 28 Jan 2023 • Jianqiang Wang, Dandan Ding, Hao Chen, Zhan Ma
This work extends the Multiscale Sparse Representation (MSR) framework developed for static Point Cloud Geometry Compression (PCGC) to support the dynamic PCGC through the use of multiscale inter conditional coding.
no code implementations • 17 Sep 2022 • Dandan Ding, Junzhe Zhang, Jianqiang Wang, Zhan Ma
A learning-based adaptive loop filter is developed for the Geometry-based Point Cloud Compression (G-PCC) standard to reduce attribute compression artifacts.
no code implementations • 26 Aug 2022 • Ruixiang Xue, Jianqiang Wang, Zhan Ma
Although convolutional representation of multiscale sparse tensor demonstrated its superior efficiency to accurately model the occupancy probability for the compression of geometry component of dense object point clouds, its capacity for representing sparse LiDAR point cloud geometry (PCG) was largely limited.
1 code implementation • 3 Apr 2022 • Jianqiang Wang, Zhan Ma
Recently, numerous learning-based compression methods have been developed with outstanding performance for the coding of the geometry information of point clouds.
no code implementations • 1 Dec 2021 • Mengchi Cai, Qing Xu, Chunying Yang, Jianghong Dong, Chaoyi Chen, Jiawei Wang, Jianqiang Wang, Keqiang Li
Formation control methods of connected and automated vehicles have been proposed to smoothly switch the structure of vehicular formations in different scenarios.
2 code implementations • 20 Nov 2021 • Jianqiang Wang, Dandan Ding, Zhu Li, Xiaoxing Feng, Chuntong Cao, Zhan Ma
We call this compression method SparsePCGC.
no code implementations • 25 Oct 2021 • Chunying Yang, Jianghong Dong, Qing Xu, Mengchi Cai, Hongmao Qin, Jianqiang Wang, Keqiang Li
To confirm effectiveness of this method, a prototype system is developed, which consists of sand table testbed, its twin system and cloud.
no code implementations • 21 Oct 2021 • Wenzheng Hu, Zhengping Che, Ning Liu, Mingyang Li, Jian Tang, ChangShui Zhang, Jianqiang Wang
Deep convolutional neural networks are shown to be overkill with high parametric and computational redundancy in many application scenarios, and an increasing number of works have explored model pruning to obtain lightweight and efficient networks.
3 code implementations • 25 Aug 2021 • Mengchi Cai, Qing Xu, Chaoyi Chen, Jiawei Wang, Keqiang Li, Jianqiang Wang, Xiangbin Wu
Unsignalized intersection cooperation of connected and automated vehicles (CAVs) is able to eliminate green time loss of signalized intersections and improve traffic efficiency.
no code implementations • 23 Mar 2021 • Xuewu Lin, Yu-ang Guo, Jianqiang Wang
Early tracking-by-detection algorithms need to do two feature extractions for detection and tracking.
4 code implementations • 18 Mar 2021 • Mengchi Cai, Qing Xu, Chaoyi Chen, Jiawei Wang, Keqiang Li, Jianqiang Wang, Qianying Zhu
Multi-vehicle coordinated decision making and control can improve traffic efficiency while guaranteeing driving safety.
3 code implementations • 7 Nov 2020 • Jianqiang Wang, Dandan Ding, Zhu Li, Zhan Ma
Recent years have witnessed the growth of point cloud based applications because of its realistic and fine-grained representation of 3D objects and scenes.
2 code implementations • 26 Sep 2019 • Jianqiang Wang, Hao Zhu, Zhan Ma, Tong Chen, Haojie Liu, Qiu Shen
This paper presents a novel end-to-end Learned Point Cloud Geometry Compression (a. k. a., Learned-PCGC) framework, to efficiently compress the point cloud geometry (PCG) using deep neural networks (DNN) based variational autoencoders (VAE).
1 code implementation • 1 Dec 2016 • Xi Xiong, Jianqiang Wang, Fang Zhang, Keqiang Li
Combining deep reinforcement learning and safety based control can get good performance for self-driving and collision avoidance.
Robotics