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End-to-End Autonomous Driving: Challenges and Frontiers.

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    End-to-end autonomous driving systems integrate perception and planning for improved performance. This survey analyzes over 270 papers on end-to-end driving, covering methods, challenges, and future trends.

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    Area of Science:

    • Robotics
    • Artificial Intelligence
    • Computer Science

    Background:

    • The autonomous driving field is increasingly adopting end-to-end frameworks.
    • These systems process raw sensor data for direct motion planning, unlike modular approaches.
    • Joint optimization of perception and planning offers advantages over task-specific modules.

    Purpose of the Study:

    • To provide a comprehensive survey of end-to-end autonomous driving research.
    • To analyze the motivation, methodology, challenges, and future directions in the field.
    • To cover over 270 relevant academic papers.

    Main Methods:

    • Systematic literature review of end-to-end autonomous driving papers.
    • Analysis of methodologies, including perception, planning, and sensor fusion.
    • Discussion of challenges such as multi-modality, interpretability, and robustness.

    Main Results:

    • Identified key trends and advancements in end-to-end autonomous driving.
    • Detailed analysis of over 270 papers, categorizing their approaches and findings.
    • Highlighted critical challenges and potential solutions within the surveyed literature.

    Conclusions:

    • End-to-end driving systems show significant promise, benefiting from joint optimization.
    • Addressing challenges like robustness and interpretability is crucial for future development.
    • Emerging techniques like foundation models offer new avenues for end-to-end driving frameworks.