A Diffusion-Model of Joint Interactive Navigation

Simulation of autonomous vehicle systems requires that simulated traffic participants exhibit diverse and realistic behaviors. The use of prerecorded real-world traffic scenarios in simulation ensures realism but the rarity of safety critical events makes large scale collection of driving scenarios expensive. In this paper, we present DJINN - a diffusion based method of generating traffic scenarios. Our approach jointly diffuses the trajectories of all agents, conditioned on a flexible set of state observations from the past, present, or future. On popular trajectory forecasting datasets, we report state of the art performance on joint trajectory metrics. In addition, we demonstrate how DJINN flexibly enables direct test-time sampling from a variety of valuable conditional distributions including goal-based sampling, behavior-class sampling, and scenario editing.Comment: 10 pages, 4 figure

Video Killed the HD-Map: Predicting Driving Behavior Directly From Drone Images

The development of algorithms that learn behavioral driving models using human demonstrations has led to increasingly realistic simulations. In general, such models learn to jointly predict trajectories for all controlled agents by exploiting road context information such as drivable lanes obtained from manually annotated high-definition (HD) maps. Recent studies show that these models can greatly benefit from increasing the amount of human data available for training. However, the manual annotation of HD maps which is necessary for every new location puts a bottleneck on efficiently scaling up human traffic datasets. We propose a drone birdview image-based map (DBM) representation that requires minimal annotation and provides rich road context information. We evaluate multi-agent trajectory prediction using the DBM by incorporating it into a differentiable driving simulator as an image-texture-based differentiable rendering module. Our results demonstrate competitive multi-agent trajectory prediction performance when using our DBM representation as compared to models trained with rasterized HD maps

Impact of Intramyocardial Hemorrhage on Clinical Outcomes in ST-Elevation Myocardial Infarction: A Systematic Review and Meta-analysis

Background: Intramyocardial hemorrhage (IMH) occurs after ST-elevation myocardial infarction (STEMI) and has been documented using cardiac magnetic resonance imaging. The prevalence and prognostic significance of IMH are not well described, and the small sample size has limited prior studies. Methods: We performed a comprehensive literature search of multiple databases to identify studies that compared outcomes in STEMI patients with or without IMH. The outcomes studied were major adverse cardiovascular events (MACE), infarct size, thrombolysis in myocardial infarction (TIMI) flow after percutaneous coronary intervention (PCI), left ventricular end-diastolic volume (LVEDV), left ventricular ejection fraction (LVEF), and mortality. Odds ratios (ORs) and standardized mean differences with corresponding 95% CIs were calculated using a random effects model. Results: Eighteen studies, including 2824 patients who experienced STEMI (1078 with IMH and 1746 without IMH), were included. The average prevalence of IMH was 39%. There is a significant association between IMH and subsequent MACE (OR, 2.63; 95% CI, 1.79-3.86; P < .00001), as well as IMH and TIMI grade <3 after PCI (OR, 1.75; 95% CI, 1.14-2.68; P = .05). We also found a significant association between IMH and the use of glycoprotein IIb/IIIa inhibitors (OR, 2.34; 95% CI, 1.42-3.85; P = .0008). IMH has a positive association with infarct size (standardized mean difference, 2.19; 95% CI, 1.53-2.86; P < .00001) and LVEDV (standardized mean difference, 0.7; 95% CI, 0.41-0.99; P < .00001) and a negative association with LVEF (standardized mean difference, -0.89; 95% CI, -1.15 to -0.63; P = .01). Predictors of IMH include male sex, smoking, and left anterior descending infarct. Conclusions: Intramyocardial hemorrhage is prevalent in approximately 40% of patients who experience STEMI. IMH is a significant predictor of MACE and is associated with larger infarct size, higher LVEDV, and lower LVEF after STEMI

Critic Sequential Monte Carlo

We introduce CriticSMC, a new algorithm for planning as inference built from a novel composition of sequential Monte Carlo with learned soft-Q function heuristic factors. This algorithm is structured so as to allow using large numbers of putative particles leading to efficient utilization of computational resource and effective discovery of high reward trajectories even in environments with difficult reward surfaces such as those arising from hard constraints. Relative to prior art our approach is notably still compatible with model-free reinforcement learning in the sense that the implicit policy we produce can be used at test time in the absence of a world model. Our experiments on self-driving car collision avoidance in simulation demonstrate improvements against baselines in terms of infraction minimization relative to computational effort while maintaining diversity and realism of found trajectories.Comment: 20 pages, 3 figure