From the 4 of 110 papers with an AI index.
37 citations
- Istituto Nazionale di Fisica Nucleare, Sezione di BolognaIT53 papers
- Université Paris-SaclayFR53 papers
- Istituto Nazionale di Fisica Nucleare, Sezione di PadovaIT52 papers
- Istituto Nazionale di Fisica Nucleare, Sezione di Roma IIT52 papers
- Istituto Nazionale di Fisica Nucleare, Sezione di GenovaIT51 papers
- University of ZurichCH51 papers
- Massachusetts Institute of TechnologyUS50 papers
- University of BristolGB50 papers
- University of Maryland, College ParkUS49 papers
- Istituto Nazionale di Fisica Nucleare, Sezione di BariIT48 papers
- Istituto Nazionale di Fisica Nucleare, Sezione di PisaIT48 papers
- Jagiellonian UniversityPL48 papers
6 papers · 1 filter
Zero-Shot MARL Benchmark in the Cyber-Physical Mobility Lab
Julius Beerwerth, Jianye Xu, Simon Schäfer +2
We present a reproducible benchmark for evaluating sim-to-real transfer of Multi-Agent Reinforcement Learning (MARL) policies for Connected and Automated Vehicles (CAVs). The platf…
Integration of an Agent Model into an Open Simulation Architecture for Scenario-Based Testing of Automated Vehicles
Christian Geller, Daniel Becker, Jobst Beckmann +1
Simulative and scenario-based testing are crucial methods in the safety assurance for automated driving systems. To ensure that simulation results are reliable, the real world must…
Edge Case Detection in Automated Driving: Methods, Challenges, and Future Directions
Saeed Rahmani, Sabine Rieder, Erwin de Gelder +6
Automated vehicles (AVs) promise to enhance transportation safety and efficiency. However, ensuring their reliability in real-world conditions remains challenging, particularly due…
Integrated Wheel Sensor Communication using ESP32 -- A Contribution towards a Digital Twin of the Road System
Ventseslav Yordanov, Simon Schäfer, Alexander Mann +3
While current onboard state estimation methods are adequate for most driving and safety-related applications, they do not provide insights into the interaction between tires and ro…
Preferential Bayesian Optimization with Crash Feedback
Johanna Menn, David Stenger, Sebastian Trimpe
Bayesian optimization is a popular black-box optimization method for parameter learning in control and robotics. It typically requires an objective function that reflects the user'…
Small-Scale Testbeds for Connected and Automated Vehicles and Robot Swarms: Challenges and a Roadmap
Jianye Xu, Johannes Betz, Armin Mokhtarian +11
This article proposes a roadmap to address the current challenges in small-scale testbeds for Connected and Automated Vehicles (CAVs) and robot swarms. The roadmap is a joint effor…