A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Paper introduces a streaming compression method for monitoring pedestrian events on footbridges.
Improved pedestrian crossing prediction for AVs using contextual factors.
Edge filters reduce video data transmission to datacenters.
This study uses ARM to analyze pedestrian crashes under different lighting conditions.
State-of-the-art pedestrian detection models have achieved great success in many benchmarks. However, these models require lots of annotation information and the labeling process usually takes much time and efforts. In this paper, we propose a method to generate labeled pedestrian data and adapt them to support the tra…
A novel pedestrian path-planning model using reinforcement learning.
Study quantifies pedestrian traffic patterns in NYC.
A deep learning model improves pedestrian tracking accuracy.
PIP-Net predicts pedestrian crossing intentions with up to 4-second lead.
Deep learning agent improves pedestrian navigation in urban environments.
This paper presents a novel context-based approach for pedestrian motion prediction in crowded, urban intersections, with the additional flexibility of prediction in similar, but new, environments. Previously, Chen et. al. combined Markovian-based and clustering-based approaches to learn motion primitives in a grid-bas…
With the rise of self-driving vehicles comes the risk of accidents and the need for higher safety, and protection for pedestrian detection in the following scenarios: imminent crashes, thus the car should crash into an object and avoid the pedestrian, and in the case of road intersections, where it is important for the…
This paper compares deep learning and knowledge-based methods for pedestrian trajectory prediction.
Study finds object detection systems have higher error rates for darker-skinned pedestrians.
Deep RL model optimizes pedestrian evacuation in multi-exit scenarios.
A comparison of SLDS and LSTM for pedestrian behavior prediction shows SLDS works better with shorter sequences.
Autonomous Vehicles navigating in urban areas have a need to understand and predict future pedestrian behavior for safer navigation. This high level of situational awareness requires observing pedestrian behavior and extrapolating their positions to know future positions. While some work has been done in this field usi…
This paper presents a novel framework for accurate pedestrian intent prediction at intersections. Given some prior knowledge of the curbside geometry, the presented framework can accurately predict pedestrian trajectories, even in new intersections that it has not been trained on. This is achieved by making use of the …
Representation learning of pedestrian trajectories transforms variable-length timestamp-coordinate tuples of a trajectory into a fixed-length vector representation that summarizes spatiotemporal characteristics. It is a crucial technique to connect feature-based data mining with trajectory data. Trajectory representati…
Paper classifies pedestrians and vehicles detected by LiDAR.
In most agent-based simulators, pedestrians navigate from origins to destinations. Consequently, destinations are essential input parameters to the simulation. While many other relevant parameters as positions, speeds and densities can be obtained from sensors, like cameras, destinations cannot be observed directly. Ou…
We propose and document the evidence for an analogy between the dynamics of granular counter-flows in the presence of bottlenecks or restrictions and financial price formation processes. Using extensive simulations, we find that the counter-flows of simulated pedestrians through a door display many stylized facts obser…
SafeCritic predicts safe trajectories for pedestrians and cyclists avoiding collisions.
Deep Recurrent Q-Network improves autonomous driving in urban areas with pedestrians.
In this paper, we train a recurrent neural network to learn dynamics of a chaotic road environment and to project the future of the environment on an image. Future projection can be used to anticipate an unseen environment for example, in autonomous driving. Road environment is highly dynamic and complex due to the int…
We present Sequential Attend, Infer, Repeat (SQAIR), an interpretable deep generative model for videos of moving objects. It can reliably discover and track objects throughout the sequence of frames, and can also generate future frames conditioning on the current frame, thereby simulating expected motion of objects. Th…
GE finds failures in autonomous systems without domain heuristics.
Method learns attractive areas from agent motions to represent environments.
Bayesian approach models match and non-match score distributions over continuous covariates.
GMM uses mmWave radar to classify traffic modes in poor lighting.
This paper presents the recently published Cerema AWP (Adverse Weather Pedestrian) dataset for various machine learning tasks and its exports in machine learning friendly format. We explain why this dataset can be interesting (mainly because it is a greatly controlled and fully annotated image dataset) and present base…
This paper proposes IMU preintegrated features for efficient deep inertial odometry.
Build accurate DNN models requires training on large labeled, context specific datasets, especially those matching the target scenario. We believe advances in wireless localization, working in unison with cameras, can produce automated annotation of targets on images and videos captured in the wild. Using pedestrian an…
We propose a novel approach to parameter estimation for simulator-based statistical models with intractable likelihood. Our proposed method involves recursive application of kernel ABC and kernel herding to the same observed data. We provide a theoretical explanation regarding why the approach works, showing (for the p…
Mobile app uses CNN to help visually impaired cross streets.
This work improves deep reinforcement learning robustness to adversarial state uncertainty.
EIM algorithm maximizes information projection for multi-modal data modeling.
This is a simple mathematical introduction into Feynman diagram technique, which is a standard physical tool to write perturbative expansions of path integrals near a critical point of the action. I start from a rigorous treatment of a finite dimensional case (which actually belongs more to multivariable calculus than …
Develops a neural model to predict event occurrence and timing.
In a densely populated city like Dhaka (Bangladesh), a growing number of high-rise buildings is an inevitable reality. However, they pose mental health risks for citizens in terms of detachment from natural light, sky view, greenery, and environmental landscapes. The housing economy and rent structure in different area…
Paper proposes PP-GCN for fine-grained social event categorization.
Paper proposes a new trading strategy using corporate event detection from news articles.
Novel RL method handles urban driving tasks including traffic lights.
We present a novel human-aware navigation approach, where the robot learns to mimic humans to navigate safely in crowds. The presented model, referred to as DeepMoTIon, is trained with pedestrian surveillance data to predict human velocity in the environment. The robot processes LiDAR scans via the trained network to n…
We show that the residue density of the logarithm of a generalised Laplacian on a closed manifold defines an invariant polynomial valued differential form. We express it in terms of a finite sum of residues of classical pseudodifferential symbols. In the case of the square of a Dirac operator, these formulae provide a …
Proposes a method to predict stock movements using fine-grained events from finance news.
ProxiModel extracts high-quality news events from news corpora.
AUC is unreliable in rare event settings but stable with moderate numbers of events.