47 citations · 157 across the 29 of their papers we have counts for
31 papers
Second-Order Continuum Model for Disordered Traffic
Shashank Rajput, Venkatesan Kanagaraj, Martin Treiber +2
In this study, a two-dimensional second-order macroscopic traffic flow model is proposed to capture the complex dynamics of disordered traffic by explicitly incorporating coupled l…
Beyond Lanes: Traffic Flow Dynamics in Disordered Conditions Based on High-Resolution Trajectory Data
Shrey Agrawal, Gowri Asaithambi, Venkatesan Kanagaraj +3
Disordered traffic flow is characterized by weak or non-existent lane discipline in the presence of strong vehicle heterogeneity and continuous lateral interactions, challenging tr…
MTA-RL: Robust Urban Driving via Multi-modal Transformer-based 3D Affordances and Reinforcement Learning
Guangli Chen, Dianzhao Li, Wenjian Zhong +2
Robust urban autonomous driving requires reliable 3D scene understanding and stable decision-making under dense interactions. However, existing end-to-end models lack interpretabil…
Ethics-Aware Safe Reinforcement Learning for Rare-Event Risk Control in Interactive Urban Driving
Dianzhao Li, Ostap Okhrin
Autonomous vehicles hold great promise for reducing traffic fatalities and improving transportation efficiency, yet their widespread adoption hinges on embedding credible and trans…
Dynamic tail risk forecasting: what do realized skewness and kurtosis add?
Giampiero Gallo, Ostap Okhrin, Giuseppe Storti
This paper compares the accuracy of tail risk forecasts with a focus on including realized skewness and kurtosis in "additive" and "multiplicative" models. Utilizing a panel of 960…
An open-source framework for data-driven trajectory extraction from AIS data -- the -method
Niklas Paulig, Ostap Okhrin
Ship trajectories from Automatic Identification System (AIS) messages are important in maritime safety, domain awareness, and algorithmic testing. Although the specifications for t…
Leader-Follower Identification with Vehicle-Following Calibration for Non-Lane-Based Traffic
Mihir Mandar Kulkarni, Ankit Anil Chaudhari, Karthik K. Srinivasan +3
Most car-following models were originally developed for lane-based traffic. Over the past two decades, efforts have been made to calibrate car-following models for non-lane-based t…
Autonomous Driving Small-Scale Cars: A Survey of Recent Development
Dianzhao Li, Paul Auerbach, Ostap Okhrin
While engaging with the unfolding revolution in autonomous driving, a challenge presents itself, how can we effectively raise awareness within society about this transformative tre…
Self-organized free-flight arrival for urban air mobility
Martin Waltz, Ostap Okhrin, Michael Schultz
Urban air mobility is an innovative mode of transportation in which electric vertical takeoff and landing (eVTOL) vehicles operate between nodes called vertiports. We outline a sel…
Two-step dynamic obstacle avoidance
Fabian Hart, Martin Waltz, Ostap Okhrin
Dynamic obstacle avoidance (DOA) is a fundamental challenge for any autonomous vehicle, independent of whether it operates in sea, air, or land. This paper proposes a two-step arch…
2-Level Reinforcement Learning for Ships on Inland Waterways: Path Planning and Following
Martin Waltz, Niklas Paulig, Ostap Okhrin
This paper proposes a realistic modularized framework for controlling autonomous surface vehicles (ASVs) on inland waterways (IWs) based on deep reinforcement learning (DRL). The f…
Vision-based DRL Autonomous Driving Agent with Sim2Real Transfer
Dianzhao Li, Ostap Okhrin
To achieve fully autonomous driving, vehicles must be capable of continuously performing various driving tasks, including lane keeping and car following, both of which are fundamen…
A Platform-Agnostic Deep Reinforcement Learning Framework for Effective Sim2Real Transfer towards Autonomous Driving
Dianzhao Li, Ostap Okhrin
Deep Reinforcement Learning (DRL) has shown remarkable success in solving complex tasks across various research fields. However, transferring DRL agents to the real world is still…
Robust Path Following on Rivers Using Bootstrapped Reinforcement Learning
Niklas Paulig, Ostap Okhrin
This paper develops a Deep Reinforcement Learning (DRL)-agent for navigation and control of autonomous surface vessels (ASV) on inland waterways. Spatial restrictions due to waterw…
Adaptive local VAR for dynamic economic policy uncertainty spillover
Niels Gillmann, Ostap Okhrin
The availability of data on economic uncertainty sparked a lot of interest in models that can timely quantify episodes of international spillovers of uncertainty. This challenging…
Enhanced method for reinforcement learning based dynamic obstacle avoidance by assessment of collision risk
Fabian Hart, Ostap Okhrin
In the field of autonomous robots, reinforcement learning (RL) is an increasingly used method to solve the task of dynamic obstacle avoidance for mobile robots, autonomous ships, a…
Spatial-temporal recurrent reinforcement learning for autonomous ships
Martin Waltz, Ostap Okhrin
This paper proposes a spatial-temporal recurrent neural network architecture for deep -networks that can be used to steer an autonomous ship. The network design makes it possibl…
Vessel-following model for inland waterways based on deep reinforcement learning
Fabian Hart, Ostap Okhrin, Martin Treiber
While deep reinforcement learning (RL) has been increasingly applied in designing car-following models in the last years, this study aims at investigating the feasibility of RL-bas…
Vulnerability-CoVaR: Investigating the Crypto-market
Martin Waltz, Abhay Kumar Singh, Ostap Okhrin
This paper proposes an important extension to Conditional Value-at-Risk (CoVaR), the popular systemic risk measure, and investigates its properties on the cryptocurrency market. Th…
Addressing Maximization Bias in Reinforcement Learning with Two-Sample Testing
Martin Waltz, Ostap Okhrin
Value-based reinforcement-learning algorithms have shown strong results in games, robotics, and other real-world applications. Overestimation bias is a known threat to those algori…
Missing Velocity in Dynamic Obstacle Avoidance based on Deep Reinforcement Learning
Fabian Hart, Martin Waltz, Ostap Okhrin
We introduce a novel approach to dynamic obstacle avoidance based on Deep Reinforcement Learning by defining a traffic type independent environment with variable complexity. Fillin…
Modified DDPG car-following model with a real-world human driving experience with CARLA simulator
Dianzhao Li, Ostap Okhrin
In the autonomous driving field, fusion of human knowledge into Deep Reinforcement Learning (DRL) is often based on the human demonstration recorded in a simulated environment. Thi…
Formulation and validation of a car-following model based on deep reinforcement learning
Fabian Hart, Ostap Okhrin, Martin Treiber
We propose and validate a novel car following model based on deep reinforcement learning. Our model is trained to maximize externally given reward functions for the free and car-fo…
Outer power transformations of hierarchical Archimedean copulas: Construction, sampling and estimation
Jan Górecki, Marius Hofert, Ostap Okhrin
A large number of commonly used parametric Archimedean copula (AC) families are restricted to a single parameter, connected to a concordance measure such as Kendall's tau. This oft…