An active inference model of car following: Advantages and applications

Driver process models play a central role in the testing, verification, and development of automated and autonomous vehicle technologies. Prior models developed from control theory and physics-based rules are limited in automated vehicle applications due to their restricted behavioral repertoire. Data-driven machine learning models are more capable than rule-based models but are limited by the need for large training datasets and their lack of interpretability, i.e., an understandable link between input data and output behaviors. We propose a novel car following modeling approach using active inference, which has comparable behavioral flexibility to data-driven models while maintaining interpretability. We assessed the proposed model, the Active Inference Driving Agent (AIDA), through a benchmark analysis against the rule-based Intelligent Driver Model, and two neural network Behavior Cloning models. The models were trained and tested on a real-world driving dataset using a consistent process. The testing results showed that the AIDA predicted driving controls significantly better than the rule-based Intelligent Driver Model and had similar accuracy to the data-driven neural network models in three out of four evaluations. Subsequent interpretability analyses illustrated that the AIDA's learned distributions were consistent with driver behavior theory and that visualizations of the distributions could be used to directly comprehend the model's decision making process and correct model errors attributable to limited training data. The results indicate that the AIDA is a promising alternative to black-box data-driven models and suggest a need for further research focused on modeling driving style and model training with more diverse datasets

Building a Credible Case for Safety: Waymo's Approach for the Determination of Absence of Unreasonable Risk

This paper presents an overview of Waymo's approach to building a reliable case for safety - a novel and thorough blueprint for use by any company building fully autonomous driving systems. A safety case for fully autonomous operations is a formal way to explain how a company determines that an AV system is safe enough to be deployed on public roads without a human driver, and it includes evidence to support that determination. It involves an explanation of the system, the methodologies used to develop it, the metrics used to validate it and the actual results of validation tests. Yet, in order to develop a worthwhile safety case, it is first important to understand what makes one credible and well crafted, and align on evaluation criteria. This paper helps enabling such alignment by providing foundational thinking into not only how a system is determined to be ready for deployment but also into justifying that the set of acceptance criteria employed in such determination is sufficient and that their evaluation (and associated methods) is credible. The publication is structured around three complementary perspectives on safety that build upon content published by Waymo since 2020: a layered approach to safety; a dynamic approach to safety; and a credible approach to safety. The proposed approach is methodology-agnostic, so that anyone in the space could employ portions or all of it

Preventing AVF thrombosis: the rationale and design of the Omega-3 fatty acids (Fish Oils) and Aspirin in Vascular access OUtcomes in REnal Disease (FAVOURED) study

Background: Haemodialysis (HD) is critically dependent on the availability of adequate access to the systemic circulation, ideally via a native arteriovenous fistula (AVF). The Primary failure rate of an AVF ranges between 20-54%, due to thrombosis or failure of maturation. There remains limited evidence for the use of anti-platelet agents and uncertainty as to choice of agent(s) for the prevention of AVF thrombosis. We present the study protocol for a randomised, double-blind, placebo-controlled, clinical trial examining whether the use of the anti-platelet agents, aspirin and omega-3 fatty acids, either alone or in combination, will effectively reduce the risk of early thrombosis in de novo AVF

Equalization of four cardiovascular risk algorithms after systematic recalibration: individual-participant meta-analysis of 86 prospective studies

Aims: There is debate about the optimum algorithm for cardiovascular disease (CVD) risk estimation. We conducted head-to-head comparisons of four algorithms recommended by primary prevention guidelines, before and after ‘recalibration’, a method that adapts risk algorithms to take account of differences in the risk characteristics of the populations being studied. Methods & Results: Using individual-participant data on 360737 participants without CVD at baseline in 86 prospective studies from 22 countries, we compared the Framingham risk score (FRS), Systematic COronary Risk Evaluation (SCORE), pooled cohort equations (PCE), and Reynolds risk score (RRS). We calculated measures of risk discrimination and calibration, and modelled clinical implications of initiating statin therapy in people judged to be at ‘high’ 10 year CVD risk. Original risk algorithms were recalibrated using the risk factor profile and CVD incidence of target populations. The four algorithms had similar risk discrimination. Before recalibration, FRS, SCORE, and PCE overpredicted CVD risk on average by 10%, 52%, and 41%, respectively, whereas RRS under-predicted by 10%. Original versions of algorithms classified 29–39% of individuals aged \u3e_40years as high risk. By contrast, recalibration reduced this proportion to 22–24% for every algorithm. We estimated that to prevent one CVD event, it would be necessary to initiate statin therapy in 44–51 such individuals using original algorithms, in contrast to 37–39 individuals with recalibrated algorithms. Conclusions: Before recalibration, the clinical performance of four widely used CVD risk algorithms varied substantially. By contrast, simple recalibration nearly equalized their performance and improved modelled targeting of preventive action to clinical need

Genome-wide association and Mendelian randomisation analysis provide insights into the pathogenesis of heart failure

Abstract: Heart failure (HF) is a leading cause of morbidity and mortality worldwide. A small proportion of HF cases are attributable to monogenic cardiomyopathies and existing genome-wide association studies (GWAS) have yielded only limited insights, leaving the observed heritability of HF largely unexplained. We report results from a GWAS meta-analysis of HF comprising 47,309 cases and 930,014 controls. Twelve independent variants at 11 genomic loci are associated with HF, all of which demonstrate one or more associations with coronary artery disease (CAD), atrial fibrillation, or reduced left ventricular function, suggesting shared genetic aetiology. Functional analysis of non-CAD-associated loci implicate genes involved in cardiac development (MYOZ1, SYNPO2L), protein homoeostasis (BAG3), and cellular senescence (CDKN1A). Mendelian randomisation analysis supports causal roles for several HF risk factors, and demonstrates CAD-independent effects for atrial fibrillation, body mass index, and hypertension. These findings extend our knowledge of the pathways underlying HF and may inform new therapeutic strategies

Measuring Surprise in the Wild

The quantitative measurement of how and when we experience surprise has mostly remained limited to laboratory studies, and its extension to naturalistic settings has been challenging. Here we demonstrate, for the first time, how computational models of surprise rooted in cognitive science and neuroscience combined with state-of-the-art machine learned generative models can be used to detect surprising human behavior in complex, dynamic environments like road traffic. In traffic safety, such models can support the identification of traffic conflicts, modeling of road user response time, and driving behavior evaluation for both human and autonomous drivers. We also present novel approaches to quantify surprise and use naturalistic driving scenarios to demonstrate a number of advantages over existing surprise measures from the literature. Modeling surprising behavior using learned generative models is a novel concept that can be generalized beyond traffic safety to any dynamic real-world environment.Comment: 25 pages, 7 figure

Carbon pricing and planetary boundaries

Human activities are threatening to push the Earth system beyond its planetary boundaries, risking catastrophic and irreversible global environmental change. Action is urgently needed, yet well-intentioned policies designed to reduce pressure on a single boundary can lead, through economic linkages, to aggravation of other pressures. In particular, the potential policy spillovers from an increase in the global carbon price onto other critical Earth system processes has received little attention to date. To this end, we explore the global environmental effects of pricing carbon, beyond its effect on carbon emissions. We find that the case for carbon pricing globally becomes even stronger in a multi-boundary world, since it can ameliorate many other planetary pressures. It does however exacerbate certain planetary pressures, largely by stimulating additional biofuel production. When carbon pricing is allied with a biofuel policy, however, it can alleviate all planetary pressures

Arterial stiffness and subclinical atherosclerosis in the coronary arteries at different stages of dysglycaemia

Aim: Our aim was to investigate in a large population -based cohort study whether increased arterial stiffness and subclinical atherosclerosis in the coronary arteries differ at different stages of dysglycaemia.Methods: Data were obtained from SCAPIS, a population -based cohort of participants 50- 64 years. The study population of 9379 participants was categorised according to glycaemic status: normoglycaemic, pre-diabetes (fasting glucose: 6.1- 6.9 mmol/L and/or HbA1c 6%- 6.4%) and diabetes. Pulse wave velocity (PWV) was measured by the SphygmoCor XCEL system and arterial stiffness was defined by PWV =10 m/s. Coronary artery calcium score (CACS) was assessed by coronary computed tomography and coronary artery calcification was defined by CACS =100.Results: We identified 1964 (21%) participants with dysglycaemia, out of which 742 (7.9%) had diabetes mellitus. PWV =10 m/s was present in 808 (11%), 191 (16%), 200 (27%) and CACS =100 in 801 (11%), 190 (16%), 191 (28%) participants with normoglycaemia, pre-diabetes and diabetes, respectively, all, p &amp;lt; 0.001. The overlap between PWV =10 m/s and CACS =100 within each glycaemic category was 188 (2.5%), 44 (3.6%) and 77 (10) respectively. There was an association between glycaemic status and increased PWV in the fully adjusted models, but not for glycaemic status and CACS =100, where there was no difference for pre-diabetes compared to normoglycaemia, OR 1.2 (95% CI 0.98- 1.4). In the total study population, there was an association between HbA1c and PWV after adjustment, p &amp;lt;0.001.Conclusions: Our results show that increased arterial stiffness and subclinical coronary artery atherosclerosis are present in the early stages of dysglycaemia, but the overlap between markers of major subclinical vascular damage was small in all glycaemic categories. This could be explained by different pathways in the pathogenesis of arterial stiffness or atherosclerosis in the coronary arteries.Funding Agencies|Swedish Heart and Lung Foundation [2016- 0315]; Knut and Alice Wallenberg Foundation [2014- 0047]; Swedish Research Council [822- 2013- 2000]; VINNOVA [2012- 04476]; University of Gothenburg and Sahlgrenska University Hospital; Stockholm County Council; Linkoeping University; Lund University; Skane University Hospital; Umea University; Uppsala University; Swedish Heart and Lung Foundation</p

Benchmarking engineering curricula with the CDIO syllabus

Four internationally-renowned universities-Chalmers University of Technology, Linkoping University, Royal Institute of Technology (Sweden), and the Massachusetts Institute of Technology (USA)-developed a benchmark survey that may be used by any engineering school to benchmark curricula for teaching of personal, interpersonal and system building skills. These skills are enumerated in the CDIO Syllabus. Teaching activities were categorized as Introduce, Teach or Utilize, based on intent, time spent, and linkage to learning objectives, assignments and assessment criteria. Interviews were used to collect the data from instructors of the schools\u27 engineering programs. The data was then reduced and analyzed to illuminate patterns of teaching. The results indicate that much effort is expended in covering these topics, but often in an inefficient, uncoordinated and unplanned manner. For example, there are often frequent repetitions of introducing a topic, without ever teaching it. In other instances, students are expected to utilize knowledge without having been taught it. The results of the benchmark survey indicate that a consistent and deliberately designed curriculum in this area could demand no additional resources, yet provide a much more effective education. The survey gives useful indications of how to begin such a curriculum redesign process