No abstract

Collaborative perception has recently shown great potential to improve perception capabilities over single-agent perception. Existing collaborative perception methods usually consider an ideal communication environment. However, in practice, the communication system inevitably suffers from latency issues, causing potential performance degradation and high risks in safety-critical applications, such as autonomous driving. To mitigate the effect caused by the inevitable latency, from a machine learning perspective, we present the first latency-aware collaborative perception system, which actively adapts asynchronous perceptual features from multiple agents to the same time stamp, promoting the robustness and effectiveness of collaboration. To achieve such a featurelevel synchronization, we propose a novel latency compensation module, called SyncNet, which leverages feature-attention symbiotic estimation and time modulation techniques. Experiments results show that the proposed latency aware collaborative perception system with SyncNet can outperforms the state-of-the-art collaborative perception method by 15.6% in the communication latency scenario and keep collaborative perception being superior to single agent perception under severe latency.

Perception is one of the crucial module of the autonomous driving system, which has made great progress recently. However, limited ability of individual vehicles results in the bottleneck of improvement of the perception performance. To break through the limits of individual perception, collaborative perception has been proposed which enables vehicles to share information to perceive the environments beyond line-of-sight and field-of-view. In this paper, we provide a review of the related work about the promising collaborative perception technology, including introducing the fundamental concepts, generalizing the collaboration modes and summarizing the key ingredients and applications of collaborative perception. Finally, we discuss the open challenges and issues of this research area and give some potential further directions.

Mining provides basic materials and energy for human life and supports economic and social prosperity and development. The decoupling of mining carbon emissions from economic development is an important way of achieving China’s carbon peaking and carbon neutrality goals. This study uses the Tapio decoupling model to measure the relationship between China’s economic development and carbon emissions from 2001 to 2018. It analyzes the overall industry as well as its subdivisions and identifies the factors driving carbon emissions with help from the improved Kaya identity and LMDI decomposition models. The results show that, except for the unstable situation in the oil and natural gas mining industry, the other mining divisions have attained strong decoupling and have become stable, showing a continuous positive trend. On the whole, the mining product smelting and processing industry has achieved a major transformation, moving from negative decoupling to weak decoupling, but there are great differences between different sub-sectors. The overall consumption of China’s mining products, and the incremental carbon emissions have continued to decline, while economic development has shifted from inefficient expansion to high-quality economic development, although without reaching the ideal state. The economic factor and energy intensity effects are the key factors in increasing and restraining carbon emissions, respectively, and their influence should not be ignored. This study aims to provide a decision-making basis for China’s mining industry, that it might carry out carbon emission reduction planning, and promote the clean and efficient construction of the industry and the green and high-quality development of the economy.