LOPR: Latent Occupancy PRediction using Generative Models

Environment prediction frameworks are essential for autonomous vehicles to facilitate safe maneuvers in a dynamic environment. Previous approaches have used occupancy grid maps as a bird's eye-view representation of the scene and optimized the prediction architectures directly in pixel space. Although these methods have had some success in spatiotemporal prediction, they are, at times, hindered by unrealistic and incorrect predictions. We postulate that the quality and realism of the forecasted occupancy grids can be improved with the use of generative models. We propose a framework that decomposes occupancy grid prediction into task-independent low-dimensional representation learning and task-dependent prediction in the latent space. We demonstrate that our approach achieves state-of-the-art performance on the real-world autonomous driving dataset, NuScenes

Improved Regret Bounds for Linear Adversarial MDPs via Linear Optimization

Learning Markov decision processes (MDP) in an adversarial environment has been a challenging problem. The problem becomes even more challenging with function approximation, since the underlying structure of the loss function and transition kernel are especially hard to estimate in a varying environment. In fact, the state-of-the-art results for linear adversarial MDP achieve a regret of $\tilde{O}(K^{6/7})$ ($K$ denotes the number of episodes), which admits a large room for improvement. In this paper, we investigate the problem with a new view, which reduces linear MDP into linear optimization by subtly setting the feature maps of the bandit arms of linear optimization. This new technique, under an exploratory assumption, yields an improved bound of $\tilde{O}(K^{4/5})$ for linear adversarial MDP without access to a transition simulator. The new view could be of independent interest for solving other MDP problems that possess a linear structure

Using Interactive Maps in Community Applications

Interactive maps provide unique ways to support community applications. In particular, they enable new collaborative activities. Map-based navigation supports a community environment as well as virtual tours. Interactive maps can also function as a tool in collecting historical information and discussing new spatial layouts. These examples indicate the numerous opportunities for interactive maps to support collaboration

Reliability of streetscape audits comparing on‐street and online observations : MAPS-Global in 5 countries

Background: Microscale environmental features are usually evaluated using direct on-street observations. This study assessed inter-rater reliability of the Microscale Audit of Pedestrian Streetscapes, Global version (MAPS-Global), in an international context, comparing on-street with more efficient online observation methods in five countries with varying levels of walkability. Methods: Data were collected along likely walking routes of study participants, from residential starting points toward commercial clusters in Melbourne (Australia), Ghent (Belgium), Curitiba (Brazil), Hong Kong (China), and Valencia (Spain). In-person on the street and online using Google Street View audits were carried out by two independent trained raters in each city. The final sample included 349 routes, 1228 street segments, 799 crossings, and 16 cul-de-sacs. Inter-rater reliability analyses were performed using Kappa statistics or Intraclass Correlation Coefficients (ICC). Results: Overall mean assessment times were the same for on-street and online evaluations (22 ± 12 min). Only a few subscales had Kappa or ICC values < 0.70, with aesthetic and social environment variables having the lowest overall reliability values, though still in the "good to excellent" category. Overall scores for each section (route, segment, crossing) showed good to excellent reliability (ICCs: 0.813, 0.929 and 0.885, respectively), and the MAPS-Global grand score had excellent reliability (ICC: 0.861) between the two methods. Conclusions: MAPS-Global is a feasible and reliable instrument that can be used both on-street and online to analyze microscale environmental characteristics in diverse international urban settings

Multi-frequency observations as a key to source and environment parameters of FRII objects

Our knowledge of the environments of radio-loud AGN is still sketchy. However, to understand the jet phenomenon it is important to know about the properties of the surroundings in which jets are formed and evolve. Here I present an analytical model of the radio surface brightness distribution of the large scale structure of FRII-type radio sources. The `virtual maps' resulting from this model can be compared with observed maps to obtain estimates for a range of source properties from the model. These properties include parameters describing the gas density distribution of the source environment, the energy transport rate of the jets and the orientation angle of the source jet axis with respect to the line of sight. The model is tested using radio maps of Cygnus A for which there are independent measurements of some of these parameters available in the literature. The model estimates agree well with these observations. Varying the resolution of the radio maps used in thecomparison does not change the results significantly.Comment: 10 pages, 2 figures, to appear in `Life Cycles of Radio Galaxies', ed. J. Biretta et al., New Astronomy Review

Impact of positivity and complete positivity on accessibility of Markovian dynamics

We consider a two-dimensional quantum control system evolving under an entropy-increasing irreversible dynamics in the semigroup form. Considering a phenomenological approach to the dynamics, we show that the accessibility property of the system depends on whether its evolution is assumed to be positive or completely positive. In particular, we characterize the family of maps having different accessibility and show the impact of that property on observable quantities by means of a simple physical model.Comment: 11 pages, to appear in J. Phys.

Efficient Continuous-Time SLAM for 3D Lidar-Based Online Mapping

Modern 3D laser-range scanners have a high data rate, making online simultaneous localization and mapping (SLAM) computationally challenging. Recursive state estimation techniques are efficient but commit to a state estimate immediately after a new scan is made, which may lead to misalignments of measurements. We present a 3D SLAM approach that allows for refining alignments during online mapping. Our method is based on efficient local mapping and a hierarchical optimization back-end. Measurements of a 3D laser scanner are aggregated in local multiresolution maps by means of surfel-based registration. The local maps are used in a multi-level graph for allocentric mapping and localization. In order to incorporate corrections when refining the alignment, the individual 3D scans in the local map are modeled as a sub-graph and graph optimization is performed to account for drift and misalignments in the local maps. Furthermore, in each sub-graph, a continuous-time representation of the sensor trajectory allows to correct measurements between scan poses. We evaluate our approach in multiple experiments by showing qualitative results. Furthermore, we quantify the map quality by an entropy-based measure.Comment: In: Proceedings of the International Conference on Robotics and Automation (ICRA) 201