| EPSRC Reference: |
EP/Y002644/1 |
| Title: |
Cross-Layer Uncertainty-Aware Reinforcement Learning for Safe Autonomous Driving |
| Principal Investigator: |
Huang, Dr C |
| Other Investigators: |
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| Researcher Co-Investigators: |
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| Project Partners: |
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| Department: |
Sch of Electronics and Computer Sci |
| Organisation: |
University of Southampton |
| Scheme: |
Standard Research - NR1 |
| Starts: |
01 July 2024 |
Ends: |
30 June 2026 |
Value (£): |
163,109
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| Related Grants: |
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| Panel History: |
| Panel Date | Panel Name | Outcome |
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17 May 2023
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ECR International Collaboration Grants Panel 1
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Announced
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Summary on Grant Application Form |
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Autonomous driving (AD) has a huge market and IS receiving enormous attention in both academia and industry. To deal with complex scenarios, autonomous vehicles (AVs) will use reinforcement learning (RL) to design high-level planners in the functional layer but always suffer from safety issues during sim-to-real transfer. One of the main challenges is that the current practice of functional-layer design does not sufficiently consider the uncertainty in the architecture layer, e.g., the software layer and hardware layer. This open challenge will be tackled in this project by a comprehensive study of the interaction between RL and architecture-layer uncertainty. Specifically, we will build virtual AD scenarios on the simulation platform with formal modeling of architecture-layer uncertainty based on real-world data (WP1). The impact of uncertainties on RL will be discussed via the design of cross-layer uncertainty-aware RL (WP2). Inversely, we will also study the robustness of an RL with respect to cross-layer uncertainty by computing the Pareto front of the largest software/hardware uncertainty patterns that a given RL is robust to (WP3). Extensive analysis including verification (WP2, WP3), simulation (WP2, WP3), and real-world experiments (WP4) will be carried out. The success of this project will greatly improve the practicability of RL in AD with a broader impact on other robotics applications.
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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
| Summary |
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| Date Materialised |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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| Project URL: |
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| Further Information: |
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| Organisation Website: |
http://www.soton.ac.uk |