Rethinking the Integration of Prediction and Planning in Deep Learning-Based Automated Driving Systems: A Review

• URL: http://arxiv.org/abs/2308.05731v2
• Date: Wed, 17 Jul 2024 09:35:26 GMT
• Title: Rethinking the Integration of Prediction and Planning in Deep Learning-Based Automated Driving Systems: A Review
• Authors: Steffen Hagedorn, Marcel Hallgarten, Martin Stoll, Alexandru Condurache,
• Abstract summary: Automated driving has the potential to revolutionize personal, public, and freight mobility. To promote safety and progress, many works rely on modules that predict the future motion of surrounding traffic. Recent models increasingly integrate prediction and planning in a joint or interdependent step to model bi-directional interactions. We systematically review state-of-the-art deep learning-based prediction and planning, and focus on integrated prediction and planning models.
• Score: 43.30610493968783
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Automated driving has the potential to revolutionize personal, public, and freight mobility. Beside accurately perceiving the environment, automated vehicles must plan a safe, comfortable, and efficient motion trajectory. To promote safety and progress, many works rely on modules that predict the future motion of surrounding traffic. Modular automated driving systems commonly handle prediction and planning as sequential, separate tasks. While this accounts for the influence of surrounding traffic on the ego vehicle, it fails to anticipate the reactions of traffic participants to the ego vehicle's behavior. Recent models increasingly integrate prediction and planning in a joint or interdependent step to model bi-directional interactions. To date, a comprehensive overview of different integration principles is lacking. We systematically review state-of-the-art deep learning-based prediction and planning, and focus on integrated prediction and planning models. Different facets of the integration ranging from model architecture and model design to behavioral aspects are considered and related to each other. Moreover, we discuss the implications, strengths, and limitations of different integration principles. By pointing out research gaps, describing relevant future challenges, and highlighting trends in the research field, we identify promising directions for future research.

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