scholarly journals A Literature Review of Performance Metrics of Automated Driving Systems for On-Road Vehicles

2021 ◽  
Vol 2 ◽  
Author(s):  
Mysore Narasimhamurthy Sharath ◽  
Babak Mehran
Keyword(s):  
Motion Planning ◽  
Performance Indicators ◽  
Performance Metrics ◽  
Recent Literature ◽  
Automated Driving ◽  
Road Vehicles ◽  
Multiple Stimulus ◽  
Human Likeness ◽  
Environment Perception ◽  
The Ideal

The article presents a review of recent literature on the performance metrics of Automated Driving Systems (ADS). More specifically, performance indicators of environment perception and motion planning modules are reviewed as they are the most complicated ADS modules. The need for the incorporation of the level of threat an obstacle poses in the performance metrics is described. A methodology to quantify the level of threat of an obstacle is presented in this regard. The approach involves simultaneously considering multiple stimulus parameters (that elicit responses from drivers), thereby not ignoring multivariate interactions. Human-likeness of ADS is a desirable characteristic as ADS share road infrastructure with humans. The described method can be used to develop human-like perception and motion planning modules of ADS. In this regard, performance metrics capable of quantifying human-likeness of ADS are also presented. A comparison of different performance metrics is then summarized. ADS operators have an obligation to report any incident (crash/disengagement) to safety regulating authorities. However, precrash events/states are not being reported. The need for the collection of the precrash scenario is described. A desirable modification to the data reporting/collecting is suggested as a framework. The framework describes the precrash sequences to be reported along with the possible ways of utilizing such a valuable dataset (by the safety regulating authorities) to comprehensively assess (and consequently improve) the safety of ADS. The framework proposes to collect and maintain a repository of precrash sequences. Such a repository can be used to 1) comprehensively learn and model the precrash scenarios, 2) learn the characteristics of precrash scenarios and eventually anticipate them, 3) assess the appropriateness of the different performance metrics in precrash scenarios, 4) synthesize a diverse dataset of precrash scenarios, 5) identify the ideal configuration of sensors and algorithms to enhance safety, and 6) monitor the performance of perception and motion planning modules.

Online or Offline: Does It Matter? A Review of Existing Interpretation Approaches and Their Effect on Screening Mammography Metrics, Patient Satisfaction, and Cost

2021 ◽  
Author(s):  
Shruthi Ram ◽  
Tyler Campbell ◽  
Ana P Lourenco
Keyword(s):  
Artificial Intelligence ◽  
Patient Satisfaction ◽  
Current Literature ◽  
Performance Metrics ◽  
Screening Mammography ◽  
Online Reading ◽  
The Future ◽  
Workflow Efficiency ◽  
Ideal Practice ◽  
The Ideal

Abstract The ideal practice routine for screening mammography would optimize performance metrics and minimize costs, while also maximizing patient satisfaction. The main approaches to screening mammography interpretation include batch offline, non-batch offline, interrupted online, and uninterrupted online reading, each of which has its own advantages and drawbacks. This article reviews the current literature on approaches to screening mammography interpretation, potential effects of newer technologies, and promising artificial intelligence resources that could improve workflow efficiency in the future.

Weighing Relative and Absolute Proportionality in Punishment

2019 ◽  
pp. 30-50
Author(s):  
Göran Duus-Otterström
Keyword(s):  
Recent Literature ◽  
The Ideal

Conflicts between relative and absolute proportionality are an important puzzle facing retributivist thought. The question of how those conflicts should be handled has long been neglected. Relative proportionality refers to the ideal that punishments should be comparatively fair among offenders. Absolute proportionality refers to the ideal that punishments should be fitting, that is, neither too harsh nor too lenient. The two senses of proportionality contribute independently to the ideal of proportionality. Thus, it is not plausible to resolve conflicts between them by dropping one of them. Instead, the two senses of proportionality must be weighed. Recent literature about comparative and noncomparative desert provides some guidance for how the two types of proportionality should be weighed. If the two types of proportionality are of roughly equal moral weight, then our greater ability to reliably satisfy relative proportionality gives us some reason to give priority to relative proportionality.

scholarly journals Reliable Road Scene Interpretation Based on ITOM with the Integrated Fusion of Vehicle and Lane Tracker in Dense Traffic Situation

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2457 ◽  
Author(s):  
Jinhan Jeong ◽  
Yook Hyun Yoon ◽  
Jahng Hyon Park
Keyword(s):  
Feature Detection ◽  
Lane Detection ◽  
Experimental Result ◽  
Automated Driving ◽  
Road Vehicles ◽  
Difficult Decision ◽  
Research Areas ◽  
Detection And Tracking ◽  
Difficult Situations ◽  
Systems Studies

Lane detection and tracking in a complex road environment is one of the most important research areas in highly automated driving systems. Studies on lane detection cover a variety of difficulties, such as shadowy situations, dimmed lane painting, and obstacles that prohibit lane feature detection. There are several hard cases in which lane candidate features are not easily extracted from image frames captured by a driving vehicle. We have carefully selected typical scenarios in which the extraction of lane candidate features can be easily corrupted by road vehicles and road markers that lead to degradations in the understanding of road scenes, resulting in difficult decision making. We have introduced two main contributions to the interpretation of road scenes in dense traffic environments. First, to obtain robust road scene understanding, we have designed a novel framework combining a lane tracker method integrated with a camera and a radar forward vehicle tracker system, which is especially useful in dense traffic situations. We have introduced an image template occupancy matching method with the integrated vehicle tracker that makes it possible to avoid extracting irrelevant lane features caused by forward target vehicles and road markers. Second, we present a robust multi-lane detection by a tracking algorithm that incudes adjacent lanes as well as ego lanes. We verify a comprehensive experimental evaluation with a real dataset comprised of problematic road scenarios. Experimental result shows that the proposed method is very reliable for multi-lane detection at the presented difficult situations.

scholarly journals A framework for coordination center responsibilities and performance in a multi-site, transdisciplinary public health research initiative

2019 ◽  
Vol 28 (3) ◽  
pp. 279-289
Author(s):  
Sarah D Hohl ◽  
Sarah Knerr ◽  
Beti Thompson
Keyword(s):  
Performance Indicators ◽  
Performance Metrics ◽  
Public Health Research ◽  
Performance Outcomes ◽  
Transdisciplinary Research ◽  
Funding Agency ◽  
Self Evaluation ◽  
Coordination Center ◽  
The Usa ◽  
And Performance

Abstract Funding bodies in the USA and abroad are increasingly investing in transdisciplinary research, i.e. research conducted by investigators from different disciplines who work to create novel theoretical, methodological, and translational innovations to address a common problem. Transdisciplinary research presents additional logistical and administrative burdens, yet few models of successful coordination have been proposed or substantiated, nor have performance outcomes or indicators been established for transdisciplinary coordination. This work uses the NIH-funded Transdisciplinary Research on Energetics and Cancer (TREC) Centers Initiative as a case study to put forward a working framework of transdisciplinary research coordination center (CC) responsibilities and performance indicators. We developed the framework using a sequential mixed methods study design. TREC CC functions and performance indicators were identified through key-informant interviews with CC personnel and then refined through a survey of TREC research center and funding agency investigators and staff. The framework included 23 TREC CC responsibilities that comprised five functional areas: leadership and administration, data and bioinformatics, developmental projects, education and training, and integration and self-evaluation, 10 performance outcomes and 26 corresponding performance indicators for transdisciplinary CCs. Findings revealed high levels of agreement about CC responsibilities and performance metrics across CC members and constituents. The success of multi-site, transdisciplinary research depends on effective research coordination. The functions identified in this study help clarify essential responsibilities of transdisciplinary research CCs and indicators of success of those transdisciplinary CCs. Our framework adds new dimensions to the notion of identifying and assessing CC activities that may foster transdisciplinarity.

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