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Скачать или смотреть "Bidirectional Planning for Autonomous Driving Framework with Large Language Model" (2024/10/19).

  • brhmlab waseda
  • 2025-03-03
  • 547
"Bidirectional Planning for Autonomous Driving Framework with Large Language Model" (2024/10/19).
autonomous drivingmulti-modal language modeldecision-makingTakafumi MatsumaruWaseda UniversityGraduate School of Information Production and Systems (IPS)Bio-Robotics and Human-Mechatronics laboratory (BRHM lab)
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Описание к видео "Bidirectional Planning for Autonomous Driving Framework with Large Language Model" (2024/10/19).

Bibliographic information:
Zhikun Ma (Waseda Univ), Qicong Sun (Singapore General Hospital), Takafumi Matsumaru (Waseda Univ) "Bidirectional Planning for Autonomous Driving Framework with Large Language Model", Sensors (MDPI) (ISSN 1424-8220), Vol.24, Issue 20, 6723 (24 pages), (2024.10.19).
https://doi.org/10.3390/s24206723
https://www.mdpi.com/1424-8220/24/20/...

Abstract:
Autonomous navigation systems often struggle in dynamic, complex environments due to challenges in safety, intent prediction, and strategic planning. Traditional methods are limited by rigid architectures and inadequate safety mechanisms, reducing adaptability to unpredictable scenarios. We propose SafeMod, a novel framework enhancing safety in autonomous driving by improving decision-making and scenario management. SafeMod features a bidirectional planning structure with two components: forward planning and backward planning. Forward planning predicts surrounding agents’ behavior using text-based environment descriptions and reasoning via large language models, generating action predictions. These are embedded into a transformer-based planner that integrates text and image data to produce feasible driving trajectories. Backward planning refines these trajectories using policy and value functions learned through Actor–Critic-based reinforcement learning, selecting optimal actions based on probability distributions. Experiments on CARLA and nuScenes benchmarks demonstrate that SafeMod outperforms recent planning systems in both real-world and simulation testing, significantly improving safety and decision-making. This underscores SafeMod’s potential to effectively integrate safety considerations and decision-making in autonomous driving.

Keywords:
autonomous driving, multi-modal language model, decision-making, Takafumi Matsumaru, Waseda University, Graduate School of Information Production and Systems (IPS), Bio-Robotics and Human-Mechatronics laboratory (BRHM lab),

Others:
((Date)) 2025/02/10
((Copyright)) Bio-Robotics and Human-Mechatronics Laboratory (BRHM lab) (Takafumi Matsumaru Laboratory), Graduate School of Information, Production and Systems (IPS), Waseda University.
https://sem-matsumaru.w.waseda.jp/
https://matsumaru.w.waseda.jp/

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