Authors: Zhili Chen, Maosheng Ye, Shuangjie Xu, Tongyi Cao, Qifeng Chen

Published on: November 14, 2023

Impact Score: 7.4

Arxiv code: Arxiv:2311.08100

Summary

• What is new: PPAD introduces an iterative interaction mechanism for autonomous driving by modeling timestep-wise interactions among the ego vehicle, agents, and the environment, improving integration of prediction and planning.
• Why this is important: Most existing autonomous driving systems process prediction and planning in a strictly sequential manner, failing to dynamically model interactions among vehicles, pedestrians, and the environment.
• What the research proposes: PPAD proposes a novel interaction mechanism where prediction and planning are interwoven at each timestep, utilizing dynamic key objects attention to enhance modeling of interactions.
• Results: Experiments performed on the nuScenes benchmark demonstrate that PPAD surpasses current leading methods in autonomous driving accuracy and safety.

Technical Details

Technological frameworks used: Iterative Interaction of Prediction and Planning Autonomous Driving (PPAD)

Models used: Hierarchical dynamic key objects attention model

Data used: nuScenes benchmark

Potential Impact

Autonomous vehicle manufacturers, ride-hailing services, logistics and transportation companies could greatly benefit or face disruption by adopting insights from PPAD.

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