5 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…
UrbanIng-V2X: A Large-Scale Multi-Vehicle, Multi-Infrastructure Dataset Across Multiple Intersections for Cooperative Perception
Karthikeyan Chandra Sekaran, Markus Geisler, Dominik RöÃle +6
Recent cooperative perception datasets have played a crucial role in advancing smart mobility applications by enabling information exchange between intelligent agents, helping to o…
DrivIng: A Large-Scale Multimodal Driving Dataset with Full Digital Twin Integration
Dominik RöÃle, 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…
Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach
Adithya Mohan, Dominik RöÃle, Daniel Cremers +1
Recent advancements in Deep Reinforcement Learning (DRL) have demonstrated its applicability across various domains, including robotics, healthcare, energy optimization, and autono…