3 citations · 3 across the 4 of their papers we have counts for
4 papers
RoAd-RL: A Unified Library and Benchmark for Robust Adversarial Reinforcement Learning
Adithya Mohan, Daniel Kriegl, Torsten Schön
Deep Reinforcement Learning (DRL) has achieved significant success in robotics and autonomous systems, yet remains vulnerable to adversarial perturbations that can severely degrade…
Real-Time Evaluation of Autonomous Systems under Adversarial Attacks
Adithya Mohan, Xujun Xie, Venkatesh Thirugnana Sambandham +1
Most evaluations of autonomous driving policies under adversarial conditions are conducted in simulation, due to cost efficiency and the absence of physical risk. However, purely v…
DrivIng: A Large-Scale Multimodal Driving Dataset with Full Digital Twin Integration
Dominik Rößle, Xujun Xie, Adithya Mohan +3
Perception is a cornerstone of autonomous driving, enabling vehicles to understand their surroundings and make safe, reliable decisions. Developing robust perception algorithms req…
Generation of Realistic Synthetic Raw Radar Data for Automated Driving Applications using Generative Adversarial Networks
Eduardo C. Fidelis, Fabio Reway, Herick Y. S. Ribeiro +5
The main approaches for simulating FMCW radar are based on ray tracing, which is usually computationally intensive and do not account for background noise. This work proposes a fas…