| Project/Area Number |
23KJ0391
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| Research Category |
Grant-in-Aid for JSPS Fellows
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| Allocation Type | Multi-year Fund |
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| Section | 国内 |
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| Review Section |
Basic Section 60060:Information network-related
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| Research Institution | The University of Tokyo |
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Principal Investigator |
林 鵬飛 東京大学, 情報理工学系研究科, 特別研究員(DC2)
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| Project Period (FY) |
2023-04-25 – 2025-03-31
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| Project Status |
Granted (Fiscal Year 2023)
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| Budget Amount *help |
¥1,800,000 (Direct Cost: ¥1,800,000)
Fiscal Year 2024: ¥900,000 (Direct Cost: ¥900,000)
Fiscal Year 2023: ¥900,000 (Direct Cost: ¥900,000)
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| Keywords | Autonomous Driving / Collision Avoidance / Path Planning / Model Predictive Control / Optimization |
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| Outline of Research at the Start |
This research proposal seeks to develop a cooperative driving system using multiple simulation platforms and the V2X technique. It incorporates safe-critical motion planning to assess collision risks and a unique virtual scene construction for validating and providing feedback to the decision layer.
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| Outline of Annual Research Achievements |
Since 2023, I have four first-authored papers that are accepted in prestigious conferences and journals, showcasing my contributions to autonomous driving technologies. These publications include innovative strategies for occlusion-aware path planning, interactive speed optimization in potential field-based path planning, and advanced lane-changing tactics considering time-to-collision. Notably, I've contributed to developing strategies for lane-changing, which reduced both maneuver length and path curvature by 27.1% and 56.1%, respectively, thus improving driving efficiency and passenger comfort. My work in occlusion-aware path planning has provided more effective solutions for unexpected vehicle intrusions, enhancing overall road safety.
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| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
I have currently completed about half of the intended progress. However, I have encountered computational power limitations in the aspect of Virtual Scene Construction. This challenge arises mainly due to the high complexity and resource-intensive nature of accurately simulating and rendering real-world traffic scenarios in virtual environments. Tools like SUMO and CARLA require significant processing capabilities to simulate intricate vehicle dynamics and environmental conditions effectively. Despite optimization efforts, the current computational resources are proving to be a bottleneck, hindering the seamless integration of dynamic potential fields with real-time virtual scenario construction and thereby impacting the overall progression of the research plan.
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| Strategy for Future Research Activity |
My future research direction will focus on enhancing autonomous vehicle (AV) systems by integrating various components for comprehensive decision-making and action.I am going to build a rule-adherence decision making that uses responsibility-sensitive safety (RSS) to ensure that decisions are made following traffic rules and ethical standards. It involves using a Reinforcement Learning (RL) agent and input from a human driving expert to refine the policy network for smarter and safer decision-making.
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