Bounding overwatch

About: Bounding overwatch is a research topic. Over the lifetime, 966 publications have been published within this topic receiving 15156 citations.

Evaluation of 3D Vulnerable Objects’ Detection Using a Multi-Sensors System for Autonomous Vehicles

Abstract: One of the primary tasks undertaken by autonomous vehicles (AVs) is object detection, which comes ahead of object tracking, trajectory estimation, and collision avoidance. Vulnerable road objects (e.g., pedestrians, cyclists, etc.) pose a greater challenge to the reliability of object detection operations due to their continuously changing behavior. The majority of commercially available AVs, and research into them, depends on employing expensive sensors. However, this hinders the development of further research on the operations of AVs. In this paper, therefore, we focus on the use of a lower-cost single-beam LiDAR in addition to a monocular camera to achieve multiple 3D vulnerable object detection in real driving scenarios, all the while maintaining real-time performance. This research also addresses the problems faced during object detection, such as the complex interaction between objects where occlusion and truncation occur, and the dynamic changes in the perspective and scale of bounding boxes. The video-processing module works upon a deep-learning detector (YOLOv3), while the LiDAR measurements are pre-processed and grouped into clusters. The output of the proposed system is objects classification and localization by having bounding boxes accompanied by a third depth dimension acquired by the LiDAR. Real-time tests show that the system can efficiently detect the 3D location of vulnerable objects in real-time scenarios.

Bounding the quantum capacity with flagged extensions

Abstract: In this article we consider flagged extensions of convex combination of quantum channels, and find general sufficient conditions for the degradability of the flagged extension. An immediate application is a bound on the quantum $Q$ and private $P$ capacities of any channel being a mixture of a unitary map and another channel, with the probability associated to the unitary component being larger than $1/2$. We then specialize our sufficient conditions to flagged Pauli channels, obtaining a family of upper bounds on quantum and private capacities of Pauli channels. In particular, we establish new state-of-the-art upper bounds on the quantum and private capacities of the depolarizing channel, BB84 channel and generalized amplitude damping channel. Moreover, the flagged construction can be naturally applied to tensor powers of channels with less restricting degradability conditions, suggesting that better upper bounds could be found by considering a larger number of channel uses.

Logically Sound Arguments for the Effectiveness of ML Safety Measures

Abstract: We investigate the issues of achieving sufficient rigor in the arguments for the safety of machine learning functions. By considering the known weaknesses of DNN-based 2D bounding box detection algorithms, we sharpen the metric of imprecise pedestrian localization by associating it with the safety goal. The sharpening leads to introducing a conservative post-processor after the standard non-max-suppression as a counter-measure. We then propose a semi-formal assurance case for arguing the effectiveness of the post-processor, which is further translated into formal proof obligations for demonstrating the soundness of the arguments. Applying theorem proving not only discovers the need to introduce missing claims and mathematical concepts but also reveals the limitation of Dempster-Shafer’s rules used in semi-formal argumentation.

Bounding the maximum value of the real-valued sequence

Abstract: For the given arbitrary sequence of real numbers {xi} n=1 we construct several lower and upper bound converging sequences. Our goal is to localize the absolute value of the sequence maximum. Also we can calculate the value of such numbers. Since the proposed algorithms are iterative, asymptotical convergence theorems are proved. The presented task seems to be pointless from the ordinary point of view, but we illustrate its importance for a set of applied problems: matrix analysis, measurement data processing and Monte Carlo methods. According to the modern conception of fault tolerant computations, also known as "interval analysis", these results could also be treated as a part of interval mathematics.

Metamodeling of input–output relationships for complex power market models

Abstract: We present a general methodology for approximating the input–output behavior of complex energy market models Outputs, such as costs, prices, and emissions, depend on policy, economic, and technological assumptions The proposed statistical and bounding methods approximate model behavior, and can help analysts focus Integrated Planning Model (IPM) runs on input assumptions whose results are potentially interesting but uncertain The statistical methods (Multivariate adaptive regression splines) use past run data to make predictions of IPM outputs given new input data sets Those methods are illustrated using results from the IPM, a large-scale linear program that is widely used by the US Environmental Protection Agency and industry to simulate the behavior of the US power market Meanwhile, bounding methods use mathematical properties of linear programs, in addition to past run data, to bound outputs for new inputs These methods can be used to approximate the outputs of any convex optimization model of power systems to reduce computation time and enable incorporation of such models into multimodel decision support systems The bounding approach is demonstrated by an application to the COMPETES model of the northwest European power market