Imitating Driver Behavior with Generative Adversarial Networks

The ability to accurately predict and simulate human driving behavior is critical for the development of intelligent transportation systems. Traditional modeling methods have employed simple parametric models and behavioral cloning. This paper adopts a method for overcoming the problem of cascading errors inherent in prior approaches, resulting in realistic behavior that is robust to trajectory perturbations. We extend Generative Adversarial Imitation Learning to the training of recurrent policies, and we demonstrate that our model outperforms rule-based controllers and maximum likelihood models in realistic highway simulations. Our model both reproduces emergent behavior of human drivers, such as lane change rate, while maintaining realistic control over long time horizons.Comment: 8 pages, 6 figure

Panel 4 (Session A): OEM, Simulation, & Training Support

A mainstream function of NTAS is to serve as a quasi-trade show for collegiate and flight academy providers, updating them each year on the latest in equipment and training support; often focusing on time-critical support; as we did most recently on ADS-B Out implementation in 2015. Panelists are asked to present new aircraft features & capabilities, progress on ADS-B update for both new and retrofit application from NTAS 2015, new & novel equipment and administrative applications, and training support for ADS-B in academic, tablet, computer, and simulation delivery

What Counts in Economic Evaluations in Health? Benefit-cost Analysis Compared to Other Forms of Economic Evaluations

Economic evaluations are increasingly popular, both in the field of global health as well as in purely domestic settings. However, the proliferation and use of economic evaluations by members of multiple publics, many of whom are non-economists, creates misunderstandings as well as strategic opportunities. In this extended essay, Lauer and colleagues develop a critical analysis of economic evaluations that is intended to clarify concepts and terms, and thereby to enable a diverse community of users, performers, and commissioners of economic analyses in health to better understand and use such studies. The authors pay particular attention to cost-effectiveness analysis, long the mainstay of economic evaluations in health, and to benefit-cost analysis. The article starts by noting that economic evaluations in health (EEHs) take a number of typical forms, although all involve a comparison of inputs and outcomes, either of which may or may not be market-traded goods. They call a particular choice of inputs and outcomes a ‘table of accounts’. They argue that the notion of a table of accounts provides a useful way to understand the methodological diversity of EEHs, one which subsumes more established but also more restrictive terminology (e.g. the notion of ‘study perspective’). Lauer and colleagues present tables of account for a number of commonly used EEHs. They then discuss at length benefit-cost analysis, a distinctive form of EEH that has recently attracted substantial attention in the form of so-called ‘investment cases’ in healt

Cost effectiveness analysis

Cost-effectiveness analysis (CEA) is a form of economic evaluation concerned with efficiency: that is, with achieving the most for the resources ( “value for money”). For example, imagine that you have billions of dollars to allocate to global health and have to decide how to spend it. Or, you are a minister of health who wants to rationalize the use of your budget. Or imagine you are the head of an agency mandated to improve human health, and you need to know what strategies to recommend. The primary aim of this chapter is to show that, in each of these cases, you ought to know something about CEA if you want to achieve your objectives. Fortunately, a number of excellent standard accounts are available (Jamison, 2009; Sculpher et al., 2017). So rather than retrace well-trodden ground, this chapter offers a complementary approach intended to respond to the needs of non-economists. It also offers a novel perspective on CEA that should be of interest to specialists. A related aim of the chapter is to explain why – in spite of its relevance – CEA remains underused for problems like those mentioned above, and misused in many cases where it is applied. We attempt to show therefore both why CEA is often appealed to and why its basic principles remain opaque

Performance of emergency surgical front of neck airway access by head and neck surgeons, general surgeons, or anaesthetists:an in situ simulation study

BACKGROUND The “Can’t Intubate Can’t Oxygenate” (CICO) emergency requires urgent front of neck airway access to prevent death. In cases reported to the 4th National Audit Project, the most successful front of neck airway (FONA) was a surgical technique, almost all of which were performed by surgeons. Subsequently, UK guidelines adopted surgical cricothyroidotomy as the preferred emergency surgical FONA technique. Despite regular skills-based training, anaesthetists may still be unwilling to perform an emergency surgical FONA. AIM To compare consultant anaesthetists, head and neck surgeons and general surgeons in a high-fidelity simulated emergency. We hypothesised that head and neck surgeons would successfully execute emergency surgical FONA faster than anaesthetists and general surgeons. METHODS We recruited 15 consultants from each specialty (total 45). All agreed to participate in an in-situ hi-fidelity simulation of an ‘anaesthetic emergency’. Participants were not told in advance that this would be a CICO scenario. RESULTS There was no significant difference in total time to successful ventilation between the three groups (median 86 vs. 98 vs. 126.5 seconds, p=0.078). However, anaesthetists completed the emergency surgical FONA procedure significantly faster than general surgeons (median 50 vs. 86 seconds, p=0.018). Despite this strong performance, qualitative data suggested some anaesthetists still believed ‘surgeons’ best placed to perform emergency surgical FONA in a genuine CICO situation. CONCLUSION Anaesthetists regularly trained in emergency emergency surgical FONA function at levels comparable to head and neck surgeons and should feel empowered to lead this procedure in the event of a CICO emergency

Modeling Human Driving Behavior through Generative Adversarial Imitation Learning

Imitation learning is an approach for generating intelligent behavior when the cost function is unknown or difficult to specify. Building upon work in inverse reinforcement learning (IRL), Generative Adversarial Imitation Learning (GAIL) aims to provide effective imitation even for problems with large or continuous state and action spaces. Driver modeling is one example of a problem where the state and action spaces are continuous. Human driving behavior is characterized by non-linearity and stochasticity, and the underlying cost function is unknown. As a result, learning from human driving demonstrations is a promising approach for generating human-like driving behavior. This article describes the use of GAIL for learning-based driver modeling. Because driver modeling is inherently a multi-agent problem, where the interaction between agents needs to be modeled, this paper describes a parameter-sharing extension of GAIL called PS-GAIL to tackle multi-agent driver modeling. In addition, GAIL is domain agnostic, making it difficult to encode specific knowledge relevant to driving in the learning process. This paper describes Reward Augmented Imitation Learning (RAIL), which modifies the reward signal to provide domain-specific knowledge to the agent. Finally, human demonstrations are dependent upon latent factors that may not be captured by GAIL. This paper describes Burn-InfoGAIL, which allows for disentanglement of latent variability in demonstrations. Imitation learning experiments are performed using NGSIM, a real-world highway driving dataset. Experiments show that these modifications to GAIL can successfully model highway driving behavior, accurately replicating human demonstrations and generating realistic, emergent behavior in the traffic flow arising from the interaction between driving agents.Comment: 28 pages, 8 figures. arXiv admin note: text overlap with arXiv:1803.0104