Student Projects
Reinforcement Learning for Go-Kart Racing
Our aim is to create an autonomous racing system capable of swiftly learning optimal racing strategies and navigating tracks more effectively (faster) than traditional methods and human drivers using RL.
Keywords
robotics, racing, rl, reinforcement learning, autonomy, control, planning
Labels
Semester Project , Master Thesis
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Published since: 2024-05-08 , Earliest start: 2024-05-03 , Latest end: 2026-07-18
Organization Research Frazzoli
Hosts Di Cicco Maurilio, Dr.
Topics Information, Computing and Communication Sciences
Strategic Interactions of Future Mobility Systems
Mobility is typically self-optimized for a particular region to accommodate internal travel needs. However, as soon as one considers multiple, interacting regions (e.g., urban areas interacting with agglomerations, and agglomerations interacting with rural areas), important coordination issues occur, including scheduling mismatches, fleet allocations, and congestion peaks. In short, a mobility system composed of self-optimized mobility systems seems to often operate suboptimally. In this project, we will investigate the idea of strategic interactions of future mobility stakeholders across heterogeneous regions, such as urban areas, agglomerations, and rural areas, leveraging techniques from network design, optimization, game theory, and policy making.
Keywords
Optimization, Game theory, Multi-agent interactions, Transportation systems, Robotics
Labels
Semester Project , Master Thesis
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Published since: 2024-04-26 , Earliest start: 2024-04-15 , Latest end: 2024-12-31
Applications limited to ETH Zurich
Organization Research Frazzoli
Hosts He Mingjia
Topics Mathematical Sciences , Information, Computing and Communication Sciences , Engineering and Technology
Submodular optimization for scenario sampling in autonomous vehicles safety testing
A key barrier hindering the swift introduction of autonomous vehicles (AVs) in real-world contexts is the challenge in establishing clear safety benchmarks. Specifically, the issue of systematically assessing both performance and safety remains a significant stumbling block within the industry. This challenge is mainly twofold: Firstly, how can we identify an ideal scenario set to evaluate the vehicle's performance within a targeted Operational Design Domain (ODD) and what criteria would be useful in amplifying or paring down this set? Secondly, how do we determine a substantial stopping criteria for the evaluation campaign, and what level of confidence should be attached to the observed performances?
Labels
Semester Project , Master Thesis
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Published since: 2024-04-11 , Earliest start: 2024-04-01
Organization Research Frazzoli
Hosts Zanardi Alessandro, Dr.
Topics Mathematical Sciences , Information, Computing and Communication Sciences , Engineering and Technology