Manabu Tsukada
収録論文 19本 ・ フィジカルAI/ロボット学習
※arXiv著者名で収集。同姓同名の別人の論文が含まれる場合があります。
論文
- Think at 5 Hz, Act at 20 Hz: Asynchronous Fast-Slow Vision-Language-Action Inference for Closed-Loop Driving2026/7/1
- Causal Scene Narration with Runtime Safety Supervision for Vision-Language-Action Driving2026/4/1
- An Open-Source Modular Benchmark for Diffusion-Based Motion Planning in Closed-Loop Autonomous Driving2026/3/1
- Trust, Don't Trust, or Flip: Robust Preference-Based Reinforcement Learning with Multi-Expert Feedback2026/1/26
- Towards Robust LiDAR Localization: Deep Learning-based Uncertainty Estimation2025/9/1
- Towards Efficient Roadside LiDAR Deployment: A Fast Surrogate Metric Based on Entropy-Guided Visibility2025/4/1
- Neural Error Covariance Estimation for Precise LiDAR Localization2025/1/1
- eRSS-RAMP: A Rule-Adherence Motion Planner Based on Extended Responsibility-Sensitive Safety for Autonomous Driving2024/9/1
- Accurate Cooperative Localization Utilizing LiDAR-equipped Roadside Infrastructure for Autonomous Driving2024/7/1
- Large Language Models for Human-like Autonomous Driving: A Survey2024/7/1
- RaceMOP: Mapless Online Path Planning for Multi-Agent Autonomous Racing using Residual Policy Learning2024/3/1
- A Rule-Compliance Path Planner for Lane-Merge Scenarios Based on Responsibility-Sensitive Safety2024/3/1
- Roadside LiDAR Assisted Cooperative Localization for Connected Autonomous Vehicles2023/11/1
- Clothoid Curve-based Emergency-Stopping Path Planning with Adaptive Potential Field for Autonomous Vehicles2023/8/1
- Potential Field-based Path Planning with Interactive Speed Optimization for Autonomous Vehicles2023/6/1
- Occlusion-Aware Path Planning for Collision Avoidance: Leveraging Potential Field Method with Responsibility-Sensitive Safety2023/6/1
- Time-to-Collision-Aware Lane-Change Strategy Based on Potential Field and Cubic Polynomial for Autonomous Vehicles2023/6/1
- Roadside-assisted Cooperative Planning using Future Path Sharing for Autonomous Driving2021/8/1
- AutoMCM: Maneuver Coordination Service with Abstracted Functions for Autonomous Driving2021/7/1