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There are two critical sensors for 3D perception in autonomous driving, the camera and the LiDAR. The camera provides rich semantic information such as color, texture, and the LiDAR reflects the 3D shape and locations of surrounding…

Computer Vision and Pattern Recognition · Computer Science 2022-05-31 Kaicheng Yu , Tang Tao , Hongwei Xie , Zhiwei Lin , Zhongwei Wu , Zhongyu Xia , Tingting Liang , Haiyang Sun , Jiong Deng , Dayang Hao , Yongtao Wang , Xiaodan Liang , Bing Wang

Sharing and joint processing of camera feeds and sensor measurements, known as Cooperative Perception (CP), has emerged as a new technique to achieve higher perception qualities. CP can enhance the safety of Autonomous Vehicles (AVs) where…

Computer Vision and Pattern Recognition · Computer Science 2024-04-15 Ahmad Sarlak , Hazim Alzorgan , Sayed Pedram Haeri Boroujeni , Abolfazl Razi , Rahul Amin

Existing Vehicle-to-Everything (V2X) cooperative perception methods rely on accurate multi-agent 3D annotations. Nevertheless, it is time-consuming and expensive to collect and annotate real-world data, especially for V2X systems. In this…

Computer Vision and Pattern Recognition · Computer Science 2025-06-19 Seth Z. Zhao , Hao Xiang , Chenfeng Xu , Xin Xia , Bolei Zhou , Jiaqi Ma

Light Detection And Ranging (LiDAR) has been widely used in autonomous vehicles for perception and localization. However, the cost of a high-resolution LiDAR is still prohibitively expensive, while its low-resolution counterpart is much…

Computer Vision and Pattern Recognition · Computer Science 2022-05-05 Lin Bai , Yiming Zhao , Xinming Huang

While automated vehicles hold the potential to significantly reduce traffic accidents, their perception systems remain vulnerable to sensor degradation caused by adverse weather and environmental occlusions. Collective perception, which…

Computer Vision and Pattern Recognition · Computer Science 2025-07-10 Sven Teufel , Dominique Mayer , Jörg Gamerdinger , Oliver Bringmann

Collaborative perception empowers autonomous agents to share complementary information and overcome perception limitations. While early fusion offers more perceptual complementarity and is inherently robust to model heterogeneity, its high…

Computer Vision and Pattern Recognition · Computer Science 2026-03-03 Yushan Han , Hui Zhang , Qiming Xia , Yi Jin , Yidong Li

Reliable perception remains a key challenge for Connected Automated Vehicles (CAVs) in complex real-world environments, where varying lighting conditions and adverse weather degrade sensing performance. While existing multi-sensor solutions…

Vehicle-to-vehicle (V2V) communication is a key component of the future autonomous driving systems. V2V can provide an improved awareness of the surrounding environment, and the knowledge about the future actions of nearby vehicles.…

Networking and Internet Architecture · Computer Science 2017-10-03 Vladimir Savic , Elad M. Schiller , Marina Papatriantafilou

Connected Autonomous Vehicles (CAVs) benefit from Vehicle-to-Everything (V2X) communication, which enables the exchange of sensor data to achieve Collaborative Perception (CP). To reduce cumulative errors in perception modules and mitigate…

Computer Vision and Pattern Recognition · Computer Science 2025-12-09 Lei Wan , Hannan Ejaz Keen , Alexey Vinel

Cooperative perception, or collective perception (CP) is an emerging and promising technology for intelligent transportation systems (ITS). It enables an ITS station (ITS-S) to share its local perception information with others by means of…

Robotics · Computer Science 2021-01-15 Mao Shan , Karan Narula , Yung Fei Wong , Stewart Worrall , Malik Khan , Paul Alexander , Eduardo Nebot

The effectiveness of autonomous vehicles relies on reliable perception capabilities. Despite significant advancements in artificial intelligence and sensor fusion technologies, current single-vehicle perception systems continue to encounter…

Vehicle-to-vehicle (V2V) communications have greatly enhanced the perception capabilities of connected and automated vehicles (CAVs) by enabling information sharing to "see through the occlusions", resulting in significant performance…

Computer Vision and Pattern Recognition · Computer Science 2023-11-09 Yunsheng Ma , Juanwu Lu , Can Cui , Sicheng Zhao , Xu Cao , Wenqian Ye , Ziran Wang

Estimating and understanding the surroundings of the vehicle precisely forms the basic and crucial step for the autonomous vehicle. The perception system plays a significant role in providing an accurate interpretation of a vehicle's…

Computer Vision and Pattern Recognition · Computer Science 2022-03-16 Sreenivasa Hikkal Venugopala

We present DeepIPCv2, an autonomous driving model that perceives the environment using a LiDAR sensor for more robust drivability, especially when driving under poor illumination conditions where everything is not clearly visible. DeepIPCv2…

Robotics · Computer Science 2024-04-05 Oskar Natan , Jun Miura

Accurate 3D object detection is essential for ensuring the safety of autonomous vehicles. Cooperative perception, which leverages vehicle-to-everything (V2X) communication to share perceptual data, enhances detection but is vulnerable to…

Computer Vision and Pattern Recognition · Computer Science 2026-05-05 Xi Zhou , Tao Huang , Qing-Long Han , Rana Abbas , Mostafa Rahimi Azghadi

The autonomous driving industry is rapidly advancing, with Vehicle-to-Vehicle (V2V) communication systems highlighting as a key component of enhanced road safety and traffic efficiency. This paper introduces a novel Real-time…

Robotics · Computer Science 2024-10-24 Xinwen Zhu , Zihao Li , Yuxuan Jiang , Jiazhen Xu , Jie Wang , Xuyang Bai

To maintain high perception performance among connected and autonomous vehicles (CAVs), in this paper, we propose an accuracy-aware and resource-efficient raw-level cooperative sensing and computing scheme among CAVs and road-side…

Networking and Internet Architecture · Computer Science 2024-03-26 Xuehan Ye , Kaige Qu , Weihua Zhuang , Xuemin Shen

Cooperative perception allows a Connected Autonomous Vehicle (CAV) to interact with the other CAVs in the vicinity to enhance perception of surrounding objects to increase safety and reliability. It can compensate for the limitations of the…

Computer Vision and Pattern Recognition · Computer Science 2023-01-16 Donghao Qiao , Farhana Zulkernine

Vehicle-to-Everything (V2X) collaborative perception has recently gained significant attention due to its capability to enhance scene understanding by integrating information from various agents, e.g., vehicles, and infrastructure. However,…

Computer Vision and Pattern Recognition · Computer Science 2023-12-27 Li Xiang , Junbo Yin , Wei Li , Cheng-Zhong Xu , Ruigang Yang , Jianbing Shen

As our cities become more intelligent and more connected with new technologies like 6G, improving communication between vehicles and infrastructure is essential while reducing energy consumption. This study proposes a secure framework for…

Signal Processing · Electrical Eng. & Systems 2024-07-23 Ruotong Zhao , Deepak Mishra , Aruna Seneviratne
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