The EU’s anti coercion instrument between EU strategic autonomy and member state sovereignty

The article reviews the dynamics that shaped the EU’s novel Anti-Coercion Instrument in the context of the Union’s aspirations for greater strategic autonomy. During the negotiations of the instrument, intended to deter and counteract economic coercion by third countries, tensions arose over the question of the appropriate level of supranationalization of competence for an instrument situated at the crossroads of the Union’s trade and foreign policy. This article sheds light on the compromise that conciliated the diverging positions of the European Parliament and the Commission on the one, and the Council and the Member States on the other side. To this end, it conducts a legal analysis of the most contested points during the legislative process. This includes the question of who gets to determine the existence of economic coercion as well as the range of potential countermeasures. It finds that although it was undisputedly based on the Common Commercial Policy of the EU, the Member States, aware of the significant proximity to foreign policy issues, negotiated significant intergovernmental involvement into the instrument. The article puts forth the argument that this was not the result of legal necessity, but of deliberate political choice. The Regulation displays an inherent tension between EU strategic autonomy and Member State sovereignty, as the analysis of the legal, institutional, and political dimension of the different negotiation positions regarding the new regulation show. The conclusion emphasizes the necessity for a political compromise, in which the degree to which the EU should assume an enhanced geopolitical role is settled. Such a compromise could prevent future legal instruments from being shaped primarily by institutional balance considerations rather than their functionality in view to the Union’s external environment

OREOS: Oriented Recognition of 3D Point Clouds in Outdoor Scenarios

We introduce a novel method for oriented place recognition with 3D LiDAR scans. A Convolutional Neural Network is trained to extract compact descriptors from single 3D LiDAR scans. These can be used both to retrieve near-by place candidates from a map, and to estimate the yaw discrepancy needed for bootstrapping local registration methods. We employ a triplet loss function for training and use a hard-negative mining strategy to further increase the performance of our descriptor extractor. In an evaluation on the NCLT and KITTI datasets, we demonstrate that our method outperforms related state-of-the-art approaches based on both data-driven and handcrafted data representation in challenging long-term outdoor conditions

MOZARD: Multi-Modal Localization for Autonomous Vehicles in Urban Outdoor Environments

Visually poor scenarios are one of the main sources of failure in visual localization systems in outdoor environments. To address this challenge, we present MOZARD, a multi-modal localization system for urban outdoor environments using vision and LiDAR. By extending our preexisting key-point based visual multi-session local localization approach with the use of semantic data, an improved localization recall can be achieved across vastly different appearance conditions. In particular we focus on the use of curbstone information because of their broad distribution and reliability within urban environments. We present thorough experimental evaluations on several driving kilometers in challenging urban outdoor environments, analyze the recall and accuracy of our localization system and demonstrate in a case study possible failure cases of each subsystem. We demonstrate that MOZARD is able to bridge scenarios where our previous work VIZARD fails, hence yielding an increased recall performance, while a similar localization accuracy of 0.2m is achieve

Clinical Evaluation of Subcutaneous Lactate Measurement in Patients after Major Cardiac Surgery

Minimally invasive techniques to access subcutaneous adipose tissue for glucose monitoring are successfully applied in type1 diabetic and critically ill patients. During critical illness, the addition of a lactate sensor might enhance prognosis and early intervention. Our objective was to evaluate SAT as a site for lactate measurement in critically ill patients. In 40 patients after major cardiac surgery, arterial blood and SAT microdialysis samples were taken in hourly intervals. Lactate concentrations from SAT were prospectively calibrated to arterial blood. Analysis was based on comparison of absolute lactate concentrations (arterial blood vs. SAT) and on a 6-hour lactate trend analysis, to test whether changes of arterial lactate can be described by SAT lactate. Correlation between lactate readings from arterial blood vs. SAT was highly significant (r2 = 0.71, P < .001). Nevertheless, 42% of SAT lactate readings and 35% of the SAT lactate trends were not comparable to arterial blood. When a 6-hour stabilization period after catheter insertion was introduced, 5.5% of SAT readings and 41.6% of the SAT lactate trends remained incomparable to arterial blood. In conclusion, replacement of arterial blood lactate measurements by readings from SAT is associated with a substantial shortcoming in clinical predictability in patients after major cardiac surgery

MOZARD: Multi-Modal Localization for Autonomous Vehicles in Urban Outdoor Environments

Visually poor scenarios are one of the main sources of failure in visual localization systems in outdoor environments. To address this challenge, we present MOZARD, a multi-modal localization system for urban outdoor environments using vision and LiDAR. By fusing key point based visual multi-session information with semantic data, an improved localization recall can be achieved across vastly different appearance conditions. In particular we focus on the use of curbstone information because of their broad distribution and reliability within urban environments. We present thorough experimental evaluations on several driving kilometers in challenging urban outdoor environments, analyze the recall and accuracy of our localization system and demonstrate in a case study possible failure cases of each subsystem. We demonstrate that MOZARD is able to bridge scenarios where our previous key point based visual approach, VIZARD, fails, hence yielding an increased recall performance, while a similar localization accuracy of 0.2m is achieved. © 2020 IEEE

VIZARD: Reliable Visual Localization for Autonomous Vehicles in Urban Outdoor Environments

Changes in appearance is one of the main sources of failure in visual localization systems in outdoor environments. To address this challenge, we present VIZARD, a visual localization system for urban outdoor environments. By combining a local localization algorithm with the use of multi-session maps, a high localization recall can be achieved across vastly different appearance conditions. The fusion of the visual localization constraints with wheel-odometry in a state estimation framework further guarantees smooth and accurate pose estimates. In an extensive experimental evaluation on several hundreds of driving kilometers in challenging urban outdoor environments, we analyze the recall and accuracy of our localization system, investigate its key parameters and boundary conditions, and compare different types of feature descriptors. Our results show that VIZARD is able to achieve nearly 100% recall with a localization accuracy below 0.5m under varying outdoor appearance conditions, including at night-time.Comment: 9 page