13 citations · 60 across the 24 of their papers we have counts for
6 papers · 1 filter
Learning How to Trade-Off Safety with Agility Using Deep Covariance Estimation for Perception Driven UAV Motion Planning
Onur Akgun, Kamil Canberk Atik, Mustafa Erdem +3
We investigate how to utilize predictive models for selecting appropriate motion planning strategies based on perception uncertainty estimation for agile unmanned aerial vehicle (U…
Decentralized State-Dependent Markov Chain Synthesis with an Application to Swarm Guidance
Samet Uzun, Nazim Kemal Ure, Behcet Acikmese
This paper introduces a decentralized state-dependent Markov chain synthesis (DSMC) algorithm for finite-state Markov chains. We present a state-dependent consensus protocol that a…
A Probabilistic Guidance Approach to Swarm-to-Swarm Engagement Problem
Samet Uzun, Nazim Kemal Ure
This paper introduces a probabilistic guidance approach for the swarm-to-swarm engagement problem. The idea is based on driving the controlled swarm towards an adversary swarm, whe…
A New Approach for Tactical Decision Making in Lane Changing: Sample Efficient Deep Q Learning with a Safety Feedback Reward
M. Ugur Yavas, N. Kemal Ure, Tufan Kumbasar
Automated lane change is one of the most challenging task to be solved of highly automated vehicles due to its safety-critical, uncertain and multi-agent nature. This paper present…
Sample Efficient Interactive End-to-End Deep Learning for Self-Driving Cars with Selective Multi-Class Safe Dataset Aggregation
Yunus Bicer, Ali Alizadeh, Nazim Kemal Ure +2
The objective of this paper is to develop a sample efficient end-to-end deep learning method for self-driving cars, where we attempt to increase the value of the information extrac…
Development of A Stochastic Traffic Environment with Generative Time-Series Models for Improving Generalization Capabilities of Autonomous Driving Agents
Anil Ozturk, Mustafa Burak Gunel, Melih Dal +2
Automated lane changing is a critical feature for advanced autonomous driving systems. In recent years, reinforcement learning (RL) algorithms trained on traffic simulators yielded…