Advances in vision-based lane detection: algorithms, integration, assessment, and perspectives on ACP-based parallel vision

Lane detection is a fundamental aspect of most current advanced driver assistance systems (ADASs). A large number of existing results focus on the study of vision-based lane detection methods due to the extensive knowledge background and the low-cost of camera devices. In this paper, previous vision-based lane detection studies are reviewed in terms of three aspects, which are lane detection algorithms, integration, and evaluation methods. Next, considering the inevitable limitations that exist in the camera-based lane detection system, the system integration methodologies for constructing more robust detection systems are reviewed and analyzed. The integration methods are further divided into three levels, namely, algorithm, system, and sensor. Algorithm level combines different lane detection algorithms while system level integrates other object detection systems to comprehensively detect lane positions. Sensor level uses multi-modal sensors to build a robust lane recognition system. In view of the complexity of evaluating the detection system, and the lack of common evaluation procedure and uniform metrics in past studies, the existing evaluation methods and metrics are analyzed and classified to propose a better evaluation of the lane detection system. Next, a comparison of representative studies is performed. Finally, a discussion on the limitations of current lane detection systems and the future developing trends toward an Artificial Society, Computational experiment-based parallel lane detection framework is proposed

Report on how EIONET and EEA can contribute to the urban in situ requirements of a future Copernicus anthropogenic CO2 observing system

This report provides a technical review of CO2 and CH4 emissions monitoring methods based on surface mixing ratio measurements, total column mixing ratio measurements and flux measurements. The review demonstrated that all these measurements would fulfil respective in situ requirements of the Copernicus CO2 MVS capacity, contributing to the validation of space observations in and around cities and/or the system’s city-scale emissions estimates. The review furthermore elaborated on the benefits to climate change mitigation monitoring in the respective cities and how these methods could be implemented to monitor local emissions.Negotiated procedure No EEA/IDM/R0/17/008. Services supporting the European Environment Agency’s (EEA) crosscutting coordination of the Copernicus In Situ Componen

Modeling the Use of an Airborne Platform for Cellular Communications Following Disruptions

In the wake of a disaster, infrastructure can be severely damaged, hampering telecommunications. An Airborne Communications Network (ACN) allows for rapid and accurate information exchange that is essential for the disaster response period. Access to information for survivors is the start of returning to self-sufficiency, regaining dignity, and maintaining hope. Real-world testing has proven that such a system can be built, leading to possible future expansion of features and functionality of an emergency communications system. Currently, there are no airborne civilian communications systems designed to meet the demands of the public following a natural disaster. A system allowing even a limited amount of communications post-disaster is a great improvement on the current situation, where telecommunications are frequently not available. It is technically feasible to use an airborne, wireless, cellular system quickly deployable to disaster areas and configured to restore some of the functions of damaged terrestrial telecommunications networks. The system requirements were presented, leading to the next stage of the planned research, where a range of possible solutions were examined. The best solution was selected based on the earlier, predefined criteria. The system was modeled, and a test ii system built. The system was tested and redesigned when necessary, to meet the requirements. The research has shown how the combination of technology, especially the recent miniaturizations and move to open source software for cellular network components can allow sophisticated cellular networks to be implemented. The ACN system proposed could enable connectivity and reduce the communications problems that were experienced following Hurricane Sandy and Katrina. Experience with both natural and man-made disasters highlights the fact that communications are useful only to the extent that they are accessible and useable by the population

On the Impact of Granularity of Space-Based Urban CO2 Emissions in Urban Atmospheric Inversions: A Case Study for Indianapolis, IN

Quantifying greenhouse gas (GHG) emissions from cities is a key challenge towards effective emissions management. An inversion analysis from the INdianapolis FLUX experiment (INFLUX) project, as the first of its kind, has achieved a top-down emission estimate for a single city using CO2 data collected by the dense tower network deployed across the city. However, city-level emission data, used as a priori emissions, are also a key component in the atmospheric inversion framework. Currently, fine-grained emission inventories (EIs) able to resolve GHG city emissions at high spatial resolution, are only available for few major cities across the globe. Following the INFLUX inversion case with a global 1x1 km ODIAC fossil fuel CO2 emission dataset, we further improved the ODIAC emission field and examined its utility as a prior for the city scale inversion. We disaggregated the 1x1 km ODIAC non-point source emissions using geospatial datasets such as the global road network data and satellite-data driven surface imperviousness data to a 3030 m resolution. We assessed the impact of the improved emission field on the inversion result, relative to priors in previous studies (Hestia and ODIAC). The posterior total emission estimate (5.1 MtC/yr) remains statistically similar to the previous estimate with ODIAC (5.3 MtC/yr). However, the distribution of the flux corrections was very close to those of Hestia inversion and the model-observation mismatches were significantly reduced both in forward and inverse runs, even without hourly temporal changes in emissions. EIs reported by cities often do not have estimates of spatial extents. Thus, emission disaggregation is a required step when verifying those reported emissions using atmospheric models. Our approach offers gridded emission estimates for global cities that could serves as a prior for inversion, even without locally reported EIs in a systematic way to support city-level Measuring, Reporting and Verification (MRV) practice implementation

Action-Conditioned Contrastive Policy Pretraining

Deep visuomotor policy learning achieves promising results in control tasks such as robotic manipulation and autonomous driving, where the action is generated from the visual input by the neural policy. However, it requires a huge number of online interactions with the training environment, which limits its real-world application. Compared to the popular unsupervised feature learning for visual recognition, feature pretraining for visuomotor control tasks is much less explored. In this work, we aim to pretrain policy representations for driving tasks using hours-long uncurated YouTube videos. A new contrastive policy pretraining method is developed to learn action-conditioned features from video frames with action pseudo labels. Experiments show that the resulting action-conditioned features bring substantial improvements to the downstream reinforcement learning and imitation learning tasks, outperforming the weights pretrained from previous unsupervised learning methods. Code and models will be made publicly available