Autonomous System
Autonomous systems research focuses on developing machines capable of operating independently and achieving goals without continuous human intervention. Current research emphasizes improving robustness and safety through techniques like vulnerability-adaptive protection, advanced control algorithms (including model predictive control and reinforcement learning), and the use of diverse sensor modalities (e.g., dynamic vision sensors, LiDAR) integrated with sophisticated model architectures such as neural networks and transformers. This field is crucial for advancing safety-critical applications across various sectors, including transportation, robotics, and industrial automation, by enabling more reliable and efficient systems.
Papers
Near-optimal Closed-loop Method via Lyapunov Damping for Convex Optimization
Severin Maier, Camille Castera, Peter Ochs
Towards Formal Fault Injection for Safety Assessment of Automated Systems
Ashfaq Farooqui, Behrooz Sangchoolie
Automatic Generation of Scenarios for System-level Simulation-based Verification of Autonomous Driving Systems
Srajan Goyal, Alberto Griggio, Jacob Kimblad, Stefano Tonetta
Adv3D: Generating Safety-Critical 3D Objects through Closed-Loop Simulation
Jay Sarva, Jingkang Wang, James Tu, Yuwen Xiong, Sivabalan Manivasagam, Raquel Urtasun
Formal Methods for Autonomous Systems
Tichakorn Wongpiromsarn, Mahsa Ghasemi, Murat Cubuktepe, Georgios Bakirtzis, Steven Carr, Mustafa O. Karabag, Cyrus Neary, Parham Gohari, Ufuk Topcu
Evaluating Heuristic Search Algorithms in Pathfinding: A Comprehensive Study on Performance Metrics and Domain Parameters
Aya Kherrour, Marco Robol, Marco Roveri, Paolo Giorgini
Autonomous Systems' Safety Cases for use in UK Nuclear Environments
Christopher R. Anderson, Louise A. Dennis
Safe and Robust Robot Behavior Planning via Constraint Programming
Jan Vermaelen, Tom Holvoet